Learn AWS QuickSight Data Visualization and Analytics with practical lab exercises | AMG Inc | Skillshare

Learn AWS QuickSight Data Visualization and Analytics with practical lab exercises

AMG Inc, Technologist

Learn AWS QuickSight Data Visualization and Analytics with practical lab exercises

AMG Inc, Technologist

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42 Lessons (1h 59m)
    • 1. AWS QuickSight Course Introduction

    • 2. 001 Introduction to Data Visualization

    • 3. 002 What is Data Visualization

    • 4. 003 Types of Data Visualizations

    • 5. 004 Types of Data

    • 6. 005 Introduction to Charts

    • 7. 006 Introduction to analytics

    • 8. 007 What is Data Analytics

    • 9. 008 Types of Analytics

    • 10. 009 What is AWS

    • 11. 010 What is QuickSight

    • 12. Lab 001 AWS Account Creation

    • 13. Lab 002 Creating Quicksight Account audio

    • 14. Lab 003 Dashboard Overview

    • 15. Lab 004 Importing Data in QuickSight audio

    • 16. Lab 005 Data Preparation

    • 17. Lab 006 Add Calculated Fields

    • 18. Lab 007 Save Delete Edit Duplicate existing dataset

    • 19. Lab 008 Creating Tables audio

    • 20. Lab 009 Sorting and Formatting Tables 2

    • 21. Lab 010 Conditional Formatting and Show Totals

    • 22. Lab 011 Create Pivot Table

    • 23. Lab 012 Aggregation methods

    • 24. Lab 013 KPIs

    • 25. Lab 014 Adding Filters

    • 26. Lab 015 Types of Filters

    • 27. Lab 016 Text Filters

    • 28. Lab 17 Date Filter

    • 29. Lab 18 Numeric Filters

    • 30. Lab 019 Date Controls

    • 31. Lab 020 Numeric Controls

    • 32. Lab 021 Text Controls

    • 33. Lab 022 Pie Chart

    • 34. Lab 023 Bar and Column Chart

    • 35. Lab 024 Line Graph

    • 36. Lab 025 Area Graph

    • 37. Lab 026 Stacked bar and column chart

    • 38. Lab 027 Combo Charts

    • 39. Lab 028 Tree maps

    • 40. Lab 029 Geographical plots

    • 41. Lab 030 Dashboards

    • 42. Capstone Project updated

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About This Class

This course covers the basic to advanced data visualization techniques. It is designed for students having no or very little experience in the field of data analytics. The course is divided into 11 modules. Starting from the very basic introduction of data visualization and data analytics and explains the difference amongst the two. Later the course covers all steps for setting the accounts of AWS and Quicksight to allow the students to get started using the visualization tool. Followed by an introduction of both AWS and Quicksight and a detailed overview of the tool. All the major types of visualizations and analysis are covered which are dealt by data analysts on a day to day basis along with the types of data that is represented effectively using these visuals . 

Importing data from multiple sources, data preparation and transformation is covered in this course. Visualization structures such as tables and pivot tables are covered in depth with the help of various lab sessions. To be able to add calculated fields using the existing data is a very strong pursuit of a data analyst, this aspect has been covered in depth and repeatedly throughout the lab sessions. Then there are some important features of Quicksight namely, filters and controls which are explained accompanied with their respective types using practical examples. Each section is covered using the practical lab sessions.

The course has a capstone project at the end which explains the start to end workflow of data visualization using Quicksight, after which the student is able to gain practical insight to the course.

By the end of this course students will emerge with a solid understanding of data visualization and analytics using AWS Quicksight and hands-on experience designing analysis and visualization. 

What Will Students Learn in your Course?

  • Level Up from beginner to confident Data Analyst. 
  • Gain fundamental understanding of data visualization and analytics.
  • Deal with data manipulation and transformation
  • Importing Data from different source
  • Experience working with cloud service
  • Drafting reports and analysis.
  • Learn to use multiple visualization structures for presenting data
  • Creating, publishing and sharing your own dashboards
  • Learn AWS Quicksight from experienced professional data analysts. 

Course requirements:

  • A computer (Windows, Mac, or Linux)
  • No prior knowledge of AWS Quicksight is required.
  • No programming experience needed.

Target students:

  • Anyone interested in learning visualization and data analytics
  • Anyone who wants to enter the field of analytics and visualization
  • Higher Management who want to expand their horizon by learning data visualization and analytics to make smart business decisions
  • Freelancers or other creative professionals

Meet Your Teacher

Teacher Profile Image




Our company goal is to produce best online courses in Data Science and Cloud Computing technologies. Below is our team of experienced instructors.


Adnan Shaikh has been in the information technology industry for the past 18 years and has worked for the fortune 500 companies in North America. He has held roles from Data Analyst , Systems Analyst, Data Modeler, Data Manager to Data Architect with various technology companies.

He has worked with Oracle , SQL Server, DB2 , MySql and many other relational and non relational databases over the course of his career. He has written multiple data access applications using complex SQL queries and Stored Procedures for critical production systems.

He has a masters degree from Northwestern University of Chica... See full profile

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1. AWS QuickSight Course Introduction: Welcome to the AWS quick site for data visualization and analytics course. Where our goal will be to make the data visualization and analytics experience easy to learn for people from every walks of lives are focused throughout the course will be to teach you the in-demand data visualization and analytics skills using AWS website through hands-on lab exercises. We will use quick site visualization environment which is easy to use and very functional. We have enhance the learning experience by explaining key concepts visually. We will have a capstone project at the end of the course. Quizzes are designed to test students learning of new concepts. Meet our team of instructors who have created and design this course. Together we have close to 30 years of experience in practical implementation of technology and teaching technical courses at the university level. Our team members have specialization in areas of information technology, software engineering, data science, and data visualization. We will have a total of nine modules that are designed to help the students learn progressively. We will start from the basic introduction of the course and gradually move to intermediate concepts. By the end of these lessons, you will be able to make your own reports and dashboards and manipulate data. Key concepts will be explained visually for our students so they can learn effectively. Aws quick site is a cloud-based business analytic service. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Are Capstone projects will help you understand the overall steps which should be followed in order to achieve the desired dashboards, implementing all the concepts taught in this course. Here are some of the companies requiring data analyst scales to fuel their day-to-day and critical software development communities. We look forward to having you join alcohols and the promise that this course will help you build your foundation of data visualization and analysis that will help you make your resume stand out and demand a competitive salary in the marketplace. 2. 001 Introduction to Data Visualization: Hello and welcome to data visualization and analytics with AWS. Before we dive into visualization and analytics, it is important to understand what is visualization. So starting off with the concept of visualization data, which realization is the practice of translating information into a visual context, such as a map, all craft to make data easier for the human brain to understand and pull insights from. There are three primary goals of data visualization. The very first is to explore exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. The second is analyze. Data analysis is defined as a process of cleaning, transforming, and modelling data to discover useful information for business decision-making. And lastly, present presenting data involves the use of a variety of different graphical techniques to visually show the reader the relationship between different datasets, to emphasize the nature of a particular aspect of the data or to geographically place data appropriately on a map. There are five stages of data visualization in general, through which the data is passed to extract meaningful information. These stages include number one, purpose, purpose is the foundation of a project. Number two, relation to find the relationship amongst the data. Number three, visualization, create visuals for representing the data number for enhancing use visual properties to define different levels of data. And lastly, validation, validation of data through visualization. 3. 002 What is Data Visualization: What is data visualization? Data visualization is the representation of data or information in a graph, chart or other visual format. It communicates relationships of data with images. This is important because it allows trends and patterns to be more easily seen. We need data visualization because of visual summary of information makes it easier to identify patterns and trends than looking through thousands of rules on a spreadsheet. It's the way the human brain works. In this slide, you can see that it is easier to answer the questions like which is the most populous city and which state has most cities? By looking at the bar graph, rather comparing them through the tabulated data. Data visualization has many uses. Each type of data visualization can be used in different ways. There are some of the most common ways that our visualization is used. Number one, changes over time. This is perhaps the most basic and common use of data visualization, but that doesn't mean it's not valuable to reason. It is most common is because most data has an element of time involved. Therefore, the first step in a lot of data analysis is to see how the data trends overtime number to determining the frequency. Frequency is also a fairly basic use of data visualization because it also applies to data that involves time. If time is involved, it is logical that you should determine how often the relevant events happen over time. Correlations, identifying correlations is an extremely valuable use of data visualization. It is extremely difficult to determine the relationship between two variables without a visualization. Yet it is important to be aware of relationships, data. This is a great example of the value of data visualization and data analysis when planting the outer schedule or timeline for a complex project, things can get confusing. A Gantt chart soils that issue by clearly illustrating each task within the project and how long it will take to complete network examination and example of examining a network with data visualization can be seen in market research. Marketing professionals need to know which audiences to target with their message. So they analyze the entire market to identify audience clusters, bridges between the clusters, influencers within clusters and outliers. Analyzing value and risk, determining complex matrix such as value and risk, requires many different variables to be factored in, making it almost impossible to see accurately with the plane spreadsheet, data visualization can be as simple as color-coding a formula to show which opportunities are valuable and which are risky. 4. 003 Types of Data Visualizations: There are numerous tools available to help create data visualizations. Some are more manly and some are automated. But either way, this should allow you to make any of the following types of visualizations. Data visualizations belong in the timber bottle category if they satisfy two conditions, that they are linear and that they are one-dimensional temporal visualizations normally feature lines that either standalone or overlap with each other with a start and finish time. Data visualizations that belong in the hierarchical category are those that order groups within larger crops. Hierarchical visualizations are best suited if you're looking to display clusters of information, especially if the flow from a single origin point. Datasets connect deeply with other datasets. Network data visualizations show how they relate to one another within a network. In other words, demonstrating relationships between datasets without body explanations, just like the name, multi-dimensional data visualizations have multiple dimensions. This means that there are always two or more variables in the mix to create a 3D data visualization because of the many concurrent layers and datasets, these types of visualizations tend to be most by Brando, eye-catching visuals and other plus, these visuals can break down a ton of data down to key takeaways. Geo-spatial, low spatial data visualizations relate to real life physical locations or willing familiar maps with different data points. These type of data visualizations are commonly used to display sales or acquisition over time and can be most recognizable for their use in political campaigns or to display market penetration in multi-national cooperations. 5. 004 Types of Data: Types of data. Data can be defined as a systematic record of a particular quantity. It is the different values of that quantity represented together in this set, it is a collection of facts and figures to be used for a specific purpose, such as a survey or analysis, when arranged in an organized form, it can be called information. Data may be qualitative or quantitative. Once you know the difference between them, you can know how to use them. Categorical or qualitative data. They represent some characteristics or attributes that depict descriptions that may be absorbed but cannot be computed or calculated. For example, that on attributes such as intelligence, honesty, wisdom, cleanliness, and creativity collected using the students of your class, a sample would be classified as qualitative. They are more exploratory than conclusive in nature, numerical or quantitative data. These can be measured and not simply observed. They can be numerically represented and calculations can be performed on them. For example, data on the number of students playing different sports from your class gives an estimate of how many total students play which sport. This information as numerical and can be classified as quantitative nominal data. Nominal scales are used for labeling variables without any quantitative value, nominal scales could simply be called labels, ordinal data. With ordinal scales, the order of the values is what's important. And significant. Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, et cetera, discrete data. These are data that can take only certain specific values rather than a range of values. For example, data on the blood group of a certain population or on their genders is termed as discrete data. A usual way to represent this is by using bar charts, continuous data. These are data that can take values between a certain range with the highest and lowest values. The difference between the highest and lowest value is called the range of data. For example, the age of persons can take values even in decimals or so is the case of height and weights of the students of your school. These are classified as continuous data. 6. 005 Introduction to Charts: If you are wondering what are different types of graphs and charts, they uses a names. This lecture summarizes them with examples and pictures. Chart is an essential part of working with data as they condense large amounts of data into easy to understand format. It is a graphical representation of data. Charts allows users to see what are the results of data and help better understand and predict current and future data types of chart, full stop is bar graphs. Bar charts represent categorical data with rectangular bars. Bar graphs are among the most popular types of crops and charts in economics, statistics, marketing, and visualization in digital customer experience, they are commonly used to compare several categories of data. For example, the bar chart here represents the total sum of seals for product a and product B over three years. Next line graph a line chart graphically displays data that changes continuously over time when you want to show trends, for example, how house prices have increased over time, or when you want to make predictions based on the data history over time. The following line over here and this graph shows unmute seals of a particular business company for the period of consecutive years by a chart, the pie chart breaks down of group into smaller pieces. It shows part hold relationships to illustrate numerical proportions. To make a pie chart, you need a list of categorical variables and numerical variables. It is very useful for displaying nominal or ordinal categories of data to show up us and date or proportional data when comparing areas of growth within business, such as profit. The chart you see here represents the proportion of types of transportation used by 1000 students to go to their school. Histogram. The histogram displays a frequency distribution of a dataset. At first glance, histograms look alike to bar graphs. However, there is a key difference between them. Bar charts represent categorical data and histogram represents continuous data. The users for histogram are when you want to represent the shape of the data's distribution. And when you want to see whether the outputs of two or more processes are different. The example here of histogram represents the per capita income per five h crops Scatter Plot chart. Scatter plots help you predict the behavior of one variable, which is the dependent variable based on the measure of other variable, which is independent variable. The uses of Scatterplot include when trying to find out a better, there's a relationship between two variables to predict the behavior of dependent variables based on the measure of the independent variable. The example here presents data for seven online stores at their monthly e-commerce sales and online advertising costs for last year. When chart Venn diagram uses overlapping circles to visualize the logical relationships between two or more groups of items the users of venture chart include when you want to compare and contrast groups of things, or you want to categorize or group the items here in this example of Venn diagram, it compares the features of birds and bats. Area chart. Area chart shows the change in one or several quantities over time. Its uses include when you want to show trends rather than express specific values or to display the magnitude of the change in the example shown here, it shows quarterly sales for product categories a and b for last year spline chart. It's a form of line chart that represents smooth curves through different data points. When you want to plot data that requires the usage, a fitting, such as a product lifecycle chart or an impulse response shot. In the example shown here, it shows sales of a company through several months of a year. Public chart, bubble charts are super useful types of crops. We're making a comparison of the relationships between data in three numeric data dimensions, the y-axes data, the x-axis data, and data depicting the bubble size when you have to display three or four dimensions of data, this comes in handy or when you want to compare and display the relationships in the example shown here, the bubble chart shows the relationship between cost, X-axis, profit at y-axis and probability of success, which is in bus and dangerous as the bubble size dotplot, dotplot is used for relatively small sets of data and the values falls into a number of discrete categories. Dot plots can be used to plot frequency counts when you have a small amount of categories. Or they might be useful when the variable is quantitative or categorical. In the example shown here, suppose you have Class of 26 students, they're asked to tell their favorite color. The dot plot represents their choices. 7. 006 Introduction to analytics: Hi guys. Now we are going to discuss another important aspect of the course, witches data analytics. So let's just see that what is Data Analytics, this diverse field of computer science is used to find meaningful patterns in data and uncover new knowledge based on applied mathematics, statistics, predictive modelling, and machine learning techniques. Companies can use the insights they gained from data analytics to inform their decisions, leading to better outcomes. Data analytics eliminates much of the guesswork from planning marketing campaigns, choosing what content to create, developing products and more. It gives you a 360-degree view of your customers, which means you can understand them more fully, enabling you to better meet their needs. When you understand your audience better, you can market to them more effectively. Data analytics also gives you useful insights into how your campaigns are performing so that you can fine tune them for optimal outcomes. Data analytics provide you with more insights into your customer's, allowing you to tailor customer service to their needs, provide more personalization and build stronger relationships with them. And lastly, data analytics can help you streamline your processes, saved money, and boost your bottom line when you have an improved understanding of what your audience wants, you waste less time on creating ads and content that doesn't match your audience in trust. This means less money wasted, as well as improved results from your campaigns and content strategies. 8. 007 What is Data Analytics: What is data analytics? The term data analytics refers to the process of eggs and mining data sets to draw conclusions about the information they contain. Data analytic techniques enables you to take raw data and uncover patterns to extract valuable insights from it. Today, many data analytics techniques use specialized systems in software that integrate machine learning, algorithms, automation, and other capabilities. What is the role of data analytics? The very first has gathered hidden insights. Hidden insights from data are gathered and then analyzed with respect to business requirements, number to generate reports. Reports are generated from the data and are passed on to the respective teams and individuals to deal with further actions for a high rising business. Number Three, perform market analysis. Market analysis can be performed to understand the strengths and weaknesses of competitors. Number for improved business requirement, analysis of data allows improving business to customer requirements and experience. Data analytics is nothing new today though, the growing volume of data and advanced analytics technologies available. I mean, you can get much deeper data insights more quickly. The insights that Big Data and modern technologies make possible are more accurate and more detailed. In addition to using data to inform future decisions, you can also use Quran data to make immediate decisions with the increasing demand for data analytics in the market, many tools have emerged with various functionalities for this poppers. 9. 008 Types of Analytics: There are four different types of analytics here. We start with the simplest one and go further to more sophisticated types as it happens, the more complex an analysis is, the more value it brings. Data analysis is somewhat abstract concept to understand without the help of examples. So to better illustrate, here are the four types of data analysis and examples of each descriptive analytics. This diverse field of computer science is used to find meaningful patterns in data and uncover new knowledge based on applied mathematics, statistics, predictive modeling and machine learning techniques. Diagnostic analytics diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason a business may see the leads increase that in a particular month and use diagnostic analysis to determine which marketing efforts contributed the most predictive analytics. Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data, which are then used to form predictions about the future. For example, to predict next year's revenue data from previous years will be analyzed. If revenue has gone up 20 percent every year for many years, we would predict that revenue next year will be 20 percent higher than this year. This is simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales, forecasting or qualifying leads. Prescriptive analytics prescriptive data analysis combines the information found from the previous three types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data driven choices are made. 10. 009 What is AWS: Hi and congratulations on reaching Section two of the course. In this section we are going to get an overview of the tools and technology that we are going to use throughout the course. The first and foremost question that popped up into your mind probably is, what is Amazon Web Services, also known as AWS. So let me enlighten you. Cloud computing is a term referred to storing and accessing data over the internet. It doesn't store any data on the hard disk of your personal computer. In cloud computing, you can access data from a remote server. Aws is a comprehensive, easy to use computing platform offered by Amazon. The platform has developed with a combination of infrastructure as a service platform as a service, and packaged software as a service offerings. The highly profitable Amazon Division provides servers, storage, networking, remote computing, E-mail, mobile development, and security. Amazon web services is the world's most comprehensive and broadly adapted Cloud Platform, offering over 175 fully featured services from data centers globally, Amazon web services is the world's most comprehensive and broadly adopted Cloud Platform, offering over 175 a fully featured services from data centers globally, millions of customers, including the fastest growing startups, largest enterprises, and leading government agencies are using AWS to lower costs, become more agile. And in a wheat pasta, AWS was launched in 2002. The company wanted to sell its unused Infrastructure as a Service or as an offering to the customers. Amazon launched its first AWS product in 20064 years later, in 2012, Amazon hosted a huge event focused on collecting customer input about AWS. The company still hold similar events such as re-invent, which allows customers to share feedback about AWS. In 2015, Amazon announced that its AWS revenue had reached $7.8 billion. Between then and 2016, AWS launched measures that helped customers migrate their services to AWS. Today, AWS offers customers 160 products and services. Amazon Web Services offers a wide range of different business purpose, global, cloud based products. The product includes storage, databases, analytics, networking, mobile development tools, enterprise applications with a pay-as-you-go pricing model. Amazon Web Services are widely used for various computing purpose like website hosting, application hosting, media sharing, mobile and social applications, content delivery and media distribution, storage, backup and disaster recovery, development and test environments, academic computing, search engines, and social networking. Here you can see a few of many eminent companies that use AWS. 11. 010 What is QuickSight: What is quick site? Quick site lets you easily create and publish interruptive BI dashboard's that include machine learning powered insight. Quick Site dashboards can be accessed from any device and seamlessly embedded into your applications, portals and websites. Quick site is serverless and can automatically scale to tens of thousands of users without any infrastructure to manage or capacity to plan for. It is also the first BI service to offer people Session pricing where you only pay when your users access their dashboards or reports, making it cost effective for large scale deployments using Amazon quick site, you can access data and prepare it for use in reporting. It saves your prepare data either in spice memory or as a direct Query. You can use a variety of data sources for analysis. When you create an analysis, the typical workflow looks like number one, you create a new analysis. Number two, you add new or existing datasets. Number three, choose fields to create the first chart, Quick site automatically suggests the best visualization. Number four, add more charts, tables or insights to the analysis, resize and rearrange them on one or more sheets. Use extended features to add variables, custom controls, colors, additional pages called cheats, and more. And lastly, you publish the analysis as a dashboard to share it with other people. Let's walk through the major benefits of Amazon quick site. The very first being data source compatibility. Datasource entities can either be CSV files, SAS data sources, Bio sources, or relational data sources like Amazon, Aetna, Amazon Redshift, Amazon, S3, presto, et cetera. Any other data source can be accessed by either linking them or importing them through supported ones. In fact, any instance of data stored in an internet accessible environment can be worked with quick site. Number two, slick and smooth spice engine despise engine is a super-fast parallel in-memory calculation engine and as quick and easy to use, this analytical feature has a unique Columnar storage that is combined with the latest hardware technology to empower its users to query large amounts of data are processed and analyzed them at a lightning pace simultaneously, this engine is designed to be extremely powerful and makes debt out readily available through its replication process, it enables even the non geeks to use the BI tool with relative ease and clarity by simply logging in, connecting two data source and perform analysis. Thirdly, portable, it can be accessed at any time and any place. Amazon quick site is a very handy tool, especially for all business owners, since it can literally be accessed from anywhere, laptop, desktop, smartphone, tablets, and even offline. After installing offline mode, just install the app and login and you're good to go there as a native mobile app for iOS, specifically. Fourth, flexible, no space constraints. Quick site is designed in such a way that business users are not constrained by a conservative cloud design. Uses can play around with massive data without dwelling much into. It's behind the scene, walking. As soon as you log in, you are directed to the dashboard where you can create your visualizations in a Jiffy with a world-class data engine and endless documentation, flexibility of the tool increases with each, cuz age. Number five, smart interactive visualizations, the spice calculation Engine helps to model accurate processes and retrieves the required at faster than usual. Number six, self-service analysis, business users are equipped with self-service exploratory analytics. The GUI available on the dashboard enables to slice and dice the data as per the required analysis and saved them up as stories. The stories can later be shared with others in your organization. Lastly, highly scalable. Amazon quick site can be used across several business domains to measure business metrics independently that can be scaled across tens of thousands of users who can walk independently and simultaneously across all the data sources. Features off quick site embedded analytics seamlessly embedded the ability to both view and author dashboards within your applications. Ml insights summarize your business metrics in plain language or use machine learning to predict outcomes such as Anomaly Detection or forecasting without prior data science experience, Amazon quick site queue asked questions using natural language on all your data and receive answers in seconds. 12. Lab 001 AWS Account Creation: Hi everyone, welcome to the first Mitchell reveal or our cause. I'm in this video we're going to see that how we can create an Uber account. So let's just get to it. In my browser. I'm going to type AWS account creation. And I'm going to go to AWS, got Amazon.com. And I'm going to open this up. So I'm going to click on sign up now. And you need to provide an email address. So I'm just going to provide mine. You can go ahead and give your floors. The next step is to choose a password. So whatever password you state that in and confirm that maybe doping the same password. And now we're going to select an AWS account name. So I'm going to erase this and continue. Okay, so an account already exists. I'm just going to check my email address and then gone. Okay, so I'm just going to the next thing that we have is we need to provide some contact information. So this is my postman desk phone number. I provide my And you can just go ahead and do some of your phone number. And this is important because it will give you the data. The next step is that you need to provide your country and region the address, city, state and prevents. I'm forced to go. So I'll give you a chance to fill out this information. And after you're done, just click on create account and continue to enter your personal information are your contact information. The next step is to add your payment information. So consumption but found that this information is confidential. So why just go ahead and argue yours and mine. So it will ask you a credit card or debit card number, expiration date, and the God orders Nim. And once you're done, just click on the Next button. Once you're done with the payment information, it will confirm your identities. You need to enter your country or region, and then you need to give your cell phone number. And then you need to give the security. And lastly, you need to click on the send SMS button and it will send you a verification code. The ones who have done with the entire process, the verification process and all of your billing information and Uganda conformation. So you reach up to this page, which is the AWS management console. And here we are giving the services tab. We have Euler for services which are Amazon could, right? So we passed the compute services customer and implement machine learning, AR and VR is cooled down to half the cost management and analytics. And here you can see quick site. So we went to explore that and begin to see that how we can sign into the Quickstart application in the next video. 13. Lab 002 Creating Quicksight Account audio: Hello guys and welcome to lab two and continent from where we left off in the previous lab, we are going to create an account for quick side. So typing quick site in this tab and click on the very first option that you get. Now click on the sign-up for quick site. We have an option to create a quick side account. So we have two additions. A one is the Standard Edition and the other is the Enterprise edition. Select the standard edition and it has 60 day trial period after which it will charge you per month. Now let's just go ahead and click on continue. So next it asks you to fill in the information of the region account name, notification, email, whether or not you want to enable the notification by email. Over here, the last two boxes are asking if you want to enable the quick side to auto discover your Amazon S3 bucket or the S3 storage analytics data or the IoT analytics. So we're going to keep it basic for now. So fill in the required information and we are going to click on the finish button. Once you are done, this window will appear. So congratulations, you're signed up for Amazon quick site. And this is my account name and this is my username. So click this button to go onto Amazon quick site. So it's just setting up a few samples to help us get started. And our samples have been successfully loaded. And finally, Welcome to quick side. So next up we will see the dashboard overview in the next lab session. 14. Lab 003 Dashboard Overview: Welcome to lab three. This is how the outlook of quick site looks like and we're going to explore it. Here are some sample analysis. So let's just look at the very first averages favorite. So there is nothing in favorite At the moment. The second tab is recent. It contains all of the recently viewed items. Next, we have dashboards. There no dashboards as of now, but we'll see how to create them in the course. And then again, analysis. So these are the built-in analysis which are offered to us by AWS quick site. Let's just explore one of them. So here is the analysis. We have several tabs visualize, filter, parameters, actions, themes, and settings. These are the visual types, and this sheet contains all of these visuals. So the import is complete, a 100% success. These many rows they were imported from the spice memory and 0 rows were skipped. Let me just close this. So this is the field's list and we can create the visuals using this field list. So let's just see the field well for this visual. So here's the fields well, and we have debt to be precise to year as x axis. Then we have value, which is a build amount and the revenue goal. So this is the trend line for the revenue and the bill amount. So we can create an extra sheet by clicking this plus icon. So here we can create more visuals. This score sheet one. Let me just scroll down and show you all of the visuals that are currently present. So this adoption of a year allows us to add visual title description, calculated field, and a parameter we have undo and redo options. We can also print and share. And we can expand the fields well. So here we can see the entire field well for this particular visual which is selected, if I change the selected visual than the fields well will also change according to the visual. So now let's go to datasets. These are all of the datasets which are already built in and we can add a new dataset by this option. So that's it for now. 15. Lab 004 Importing Data in QuickSight audio: Hi guys and welcome to new lab session. And in this lab session we are going to see how we can import a new dataset into quick side for further analysis. So we have an option over here for new dataset, which is in the dataset stab. So these are all of the existing datasets that are provided by quick side. In order to add a new data set, we click on this option. So here we can create a dataset from the following sources. So quick side supports connection with Salesforce, S3 analytics, RDS, ethanol, redshift, and MySQL. Also, we have an upload option which could be in the format of csv, tsv, CLF, ELF, Excel, OSX, and JSON. And here are some other options that we have. So the spice memory capacity for this region is off 1GB and 14.5 MB of which has been consumed. So these existing data sources have consumed the spice memory. So let's just upload a file by clicking this option. I have financial sampled data. I'm just going to click this and I am going to click on the Open option. So this is uploading and it has been uploaded. So we have an option of sheets over here. If there would be multiple sheets, there will be multiple options from which we can select. But for normally only have been cheat. I'm just going to select this. And here we have option of either select or we can also edit or preview the data. So for now I am going to click on edit or preview the data. So here is our data that you see. And now you can see that the spice memory, more of it has been consumed, 2009.5 MP has been consumed of the remaining 1GB. And this is how we import the data. 16. Lab 005 Data Preparation: Hello guys and welcome to lab five. And now we are going to see how we can prep the data that was previously uploaded in the previous lab session. So we have all of these fields over here. So the first thing that we can do is uncheck any of the field that we don't want to see and using the analysis. So for instance, I don't want to see the manufacturing praise for instance. So let us just deselect this and we can select it back. And you can see that it's going to appear and it is going to disappear accordingly. So another thing that we can do is we can rename the field names. So here we have an abbreviation for cost of goods sold, COGS. So let's just rename this. I am going to click on this pencil icon. And I am going to name this as cost of goods sold, and I am going to click on apply. So one other thing that we can do is change the datatype. So here date is typed, it does go to go. But over here you can see that profit is type string and we cannot apply any of the numerical functions, your operations to it. So let's just change its datatype by clicking here, and we can select it as type integer or as type decimal. So there's an alternate way of doing this. For that we have to go to field list, click on this icon. And we can change the data type to either a decimal. So for now, I am just going to stick with the string because we have a dollar sign with profit and we need to remove this dollar sign in, in order to convert it into an integer. So let's just drop the dollar sign off the discounts column. So it has these dollar signs over here. I'm going to click on this icon and click add calculation. So we have a function which is named as replace. So replace, replaces whatever substring we give it with whatever other string that we want. So here it's going to replace the dollar sign with an empty string. I'm going to name it as discounts knew, and I am going to save it. So here is our calculated field has just moved to the very end. And here it's typos tell string. Let's see all of the options for datatype. So we have all of the plausible string type options, but nothing for numbers. So we can fix this by again going to edit calculations. So here we have another function which is Bohr's decimal. Let's just double-click this and enclose all of this within the brackets and click on save. So here are discounts knew as now in decimal, and the dollar sign you can see has been successfully removed. So this is how we can change the data type of a string, which has a currency sign associated to it. 17. Lab 006 Add Calculated Fields: Hi and welcome to lab six. Let's see how to add a calculated field. So it's similar to the way that we removed the dollar sign, but we have another option. So here you can see that we have add Calculated Field button. Let's just click this. So here is how we can add a calculated field. Let's just cancel this for now. So we have the month and the year which is derived from the deed, but we don't have the D. So let's just extract this. Click on Add calculated field. Let's just name it as d. And the function that we have is extract. So we're going to the WDS for d and it's going to extract the D from date. And let's just see if this. So here we have the D. So we can use this as well in the visualizations that we have the month, year, and the D. So I've kept this radio show I because we'll be creating more calculated fields in this course. 18. Lab 007 Save Delete Edit Duplicate existing dataset: Hello oil in this lab we are going to see how we can save our data set so that it is there that is being used as a financial example that we've used in previous labs. So we can rename this as financial sample nu. And the options that we have as either we can save it or we can save and visualize it. So I am just going to click on say for now. So here you can see that the financial sample nu has been added to the datasets list. And when we click on this, we have certain options. So we can either delete a dataset, we can edit the dataset, we can create a duplicate of it, we can share it or create analysis out of it. So let's just edit this. So at lenses on the same page that we were editing in the previous labs. So I'm just going to click on save. Since we have done lots of editing. Let's just click this again. The other option that we have this duplicate, it will create a duplicate of this very dataset. So here is how we can create a duplicate. I can rename this duplicate samples as well. So let's just rename it as group one. And click on duplicate. I'm going to refresh the page. And here we have Group one in our list. So when I click this, I have the same options. Let's just edit this. And now you can see that we have all of the same functions and data that we performed on the financial sample data. So we have all of the calculated fields and everything. So let's just cancel this. Click on Groupon again. And now let's just delete the dataset and click on delete. So it is disappeared from the list, again, less CO2 financial sample. And lastly, let's explore create analysis, which allows us to create new analysis using the very dataset that has been selected. So when I click this, so here is our page on which we can analyze our data and visualize our data using the field's list. Data's in the dataset. 19. Lab 008 Creating Tables audio: Hello and welcome to lab number it. And we're going to learn to create a table in quick side. So the data set that we're going to use for this lab is the built-in dataset. And the name of this dataset is web and social media analytics. So let's just click this. And let's now click on Create analysis. We are ought to select a visual type. So the icon for the table visualize over here. So ideally we have one dimension and dropped by or one measure in the value, but we can have multiple inboard as well. So let's just select this icon. And we need to supply the groupBy dimensions and also the value as measures. So I am going to select events from the field list. I'm going to drag and drop it to the dimensions field. Well, and here you can see that all of the events have been listed out for me. The next thing that I'm going to do is I am going to add a value. So for the value I am going to select website visits. And either we can drag and drop or simply a single click can automatically added to I'm just going to click this. So by, so by default and measure that we have for aggregation is some. So each event, it corresponds to the website visits. And the blank that you see over here is that there is no event which is associated with these website visits. So we can change the aggregate, we can aggregate it as some average count, count distinct as Marx, median men, person died and many more. So let's just add another value, let's say website page views. And let's just add one more, which is the Twitter mentions. So let me just expand my visual. And when I click this visualize icon, it just expands this. So here is our visual that idle for witches sum or website visits are morph website web pages and some off Twitter mentions by. So this is how we can create a table view or a table fish with. 20. Lab 009 Sorting and Formatting Tables 2: Hello, Orland broke into lab nine. In this lab we are going to begin from where we left off. We are going to see how to sort the DB2. So for this, we need to click on the Visualize tab. The next thing that I'm going to do is click on the visual and the very first option that we have is swap. So this allows us to swap the rows and the columns or rows can be converted into columns and columns to rows. So here now you can see that event is changed into columns. So we have the website visits, we have their website page views, and the Twitter mentions in all of the venues have been changed in two rows. We can also expand this, and let's just expand the visual as well. Now let's just go to settings. So we have multiple settings for headers, for cells and for a title as well. So let's just explore them one by one so we can enable and disable the header bag like English check box. Next thing we have is the font size, which we can convert to either medium large, or extra large. But let's just keep it medium for now. And also we have the horizontal alignment which we can change to center, do right, left or, or dot. Let me just swap this. And now if I select center and right, so only Event is taking the effect but not the others since either don't have enough space to logistics pandas and drive us again. Yeah, now center. So it's changed. So similarly we have the vertical alignment as well where we can change it to drop medal or do button. Now for the cells, so for the sales also we have the font size and we have the horizontal alignment and the foreign tes, we can change it to small, or we can change it to large. And the horizontal alignment, we can go to Center or whatever it is suitable. So the next option that we have is that we can show the column or hide the coelom by clicking this option hide column. And if we wanted to show it, we need to go to fields, will select that particular column and then go do shock or limb. So it will show it again. So also we can move the columns either by swapping like this. We can select the column that we want to move and Nick and select the right or the left arrow was in order to move it do right or move it to left. And lastly, we can play around with the title as well. So we can either shortly data or we can disable the title. We can change the font sizes as well. And we can rename it by double-clicking and typing a whole new title. So let's just type in events versus the website visits was says website page views and the three dimensions. So that is how we can cite the DB2 and how we can play around with the settings. 21. Lab 010 Conditional Formatting and Show Totals: Hello everyone. So one quick thing that we're going to see in the table visual as how can we apply conditional formatting. So for that, we click on these three dots that you see and we go to conditional formatting options. So it's asking me for the column that I want to apply the conditional formatting on, and I want to apply it to Twitter mentioned. So now it's giving me the option of whether I want to add a background color or text color or an IDE. So I'm going to go with the background color, and here I'm going to choose greater than. So you can have multiple conditions. That is, you can have equals, does not equal greater than and all of these or even between. But I'm going to go with greater than the value, for instance 50. And when the value is greater than 50, I want the color to be changed to. Let's just apply. So now you can see that the conditional formatting has been applied on this column, and this is how you do it. The next thing, and a very important thing is how we can show the totals in a table. So for that then again, you have to click on these three dots and select show daughters. So these are the totals for two dimensions. These are for website visits and these four website page views. But you can see that we don't have any header attached to this. So for that, let's just go back, close this and go to the format visual settings. And in total tab, you can see that we have an option of short or open door. How do you want to position them? You want to position them top or you want to position them to the button, do you want to label to appear with them? So if I write here total and clickers, so this label is apparent, and if I erase this, so there is no label with it. So that's how you play around with a table. 22. Lab 011 Create Pivot Table: Hello everyone and welcome to lab number 11. And in this lab we are going to see how can we create a pivot table. Previously, we saw how can we create a table? And now we have a slight variation of that. And here it is the icon to the pivot table. But before we begin with the pivot table, I would like to add another dataset to my existing sheet. So let's just see how we can do that. For that, you need to click on this pencil icon, which is next to dataset and click on Add dataset. I'm going to use the sales pipeline dataset. Just select this and click on select. So when you click this icon to drop this menu down, you can see that we have two datasets. One is the sales pipeline and won the web and social media analytics that we were previously using. Okay, so right now we have selected the sales pipeline and we want to create a pivot table using the dataset that we have here. These are the fields list items. So I'm just going to click on some random item. Let's see. We want to see the salesperson. So you can see that automatically a visual has been created. But since we want a pivot table to be created, I'm going to click on this icon. So here we have the count of the records by salespersons are by default in the pivot table. If we only select the number of rows and nothing is provided in the columns or the values. So it selects the count of that particular field by default. Now, let us see that we want to add a column to it, which is the opportunity stage. So here we have the opportunity stage. I'm just going to expand this. So here we have the salesperson and then we have the opportunity stages, and we have the count accordingly. So a way to read this would be that and Smith has 59 qualified records and, and Smith has 298 prospect records. So what about this values that you see here? So we need to add a measure in order for this to work. So for instance, we add the forecasted monthly revenue. So we are going to see this by salesperson and by the stages. Now what we can derive from this pivot table is that and Smith has this much forecasted monthly revenue in terms of some and this is the qualified stage. And so we can say that Jennifer has about this much forecasted monthly revenue, which is in the closed one stage. So that is how we create a pivot table, and that is how we can see the data in terms of two dimensions. Now what if we want to add more rows? So for that, let us say we have this segment Field and I want to add it to the rows. So simple drag and drop food do. All right, so now you can see that we have the salesperson and we have the segments for this salesperson, which is SMB, startup and enterprise. And then according to that, our forecasted monthly revenue has been divided, and it's further divided according to the opportunity stages. So in the pivot table, we can have multiple dimensions across which we can view our data. In the next video, we're going to see that how we can change the aggregated formulas. So stay tuned. 23. Lab 012 Aggregation methods: Hi guys. In this video we're going to see the various aggregation methods data provided to us by AWS quick site in case of a pivot table. So here in the values, we can see this across column wise, but we can also converted to rows by clicking this option. It doesn't change the result, but does change the layout. So back to column arise when you click on this option here, you see the very first option that we have as aggregate and by default it selected to be the sum, but we can change it to average count, count distinct max, median, min, person dial, standard deviations than deviation population variance and variance population. The other thing that we have is either we can show it as a number or a currency or pass it. So let's just change it to currency. So here you can see that all of this is now represented as currency. Also, we can change the format to either dollar, pound, your role, yen, so and so forth. And we do have more formatting options available as well. So the next thing is add table calculation and we have the option of having the running daughters difference, percentage difference. So by selecting any one of these, we can change the view of the table entirely. So what if we want to select the percent of total? So now you can see that this has been changed to the person dot dawdle instead of sum, we do see the person off the total for the sum. And if you want to change the aggregate to average, this will change the values, but some is actually better, it makes more sense. So these are the options that are provided to us with a pivot table. Also, we can apply conditional formatting and we can also remove the values field. So if I remove it, you know that by default, count is there to save us. But adding a measure has its own books. So let me just add it back. So that's it for the pivot tables. And in the next video, I will show how we can create KPIs. 24. Lab 013 KPIs: Hello everyone. In this video we're going to see how can we create KPIs. So we use a key performance indicator, which is also known as a KPI, to visualize a comparison between a key value and its target value. So a KPI displays a value comparison, the two values being compared, and a progress bar. So let's just see how it's done. The data that we're going to use is the sales pipeline data. And I am going to create a new visual. And we can do that by clicking on this Add icon and select Add visual. By default, it's an autograph, but we want a KPI. So this is the eye cannot be used for this. I'm just going to select it. And now it's asking me for a value, for the target value and for a trend group. So the value that I have in mind is the weighted revenue. So by default it's taking its sum and the target value is the forecasted monthly revenue. Okay? So here we go. So in this example, the following KPI shows how closely the revenue is meeting its forecast. So it's 19.84%. So that's the weightage revenue which has maintained its forecasted monthly revenue. So we can also change the aggregations. By going to the settings. You can show the progress bar, you can show the primary value only. Or if you unselect this, all of the values are displayed. So the comparison method could be the friends, it could be percent, it could be differences present. Or we can always select the Auto option. So the comparison format could be in terms of these formats. And read the primary value display. It could be a comparison or it could be actual. But comparison is better because it gives us a better understanding. So that is how we play around with KPIs and how they give us more insights into our data. So it tells us that whether our values are reaching the target values or not, how much are they lagging behind, and what's our success rate. So thank you for watching this video. 25. Lab 014 Adding Filters: Hello everyone and welcome to a new lab session. And in this lab session we're going to see how can we add a filter to a visual. So for that, we're going to first create a visual. I'm going to go to visualize and select salesperson. I'm going to create a pivot table out of it, so we need a salesperson, let say v also add a region. And lastly, Active items. That means just to rearrange all of this so that it makes more sense. So salesperson as columns. So let's just expand this visual. I'm going to click on this Visualize tab again just to get a bigger and better picture. So here is our visual. We'll just count of records by region, active item and seagulls person. Now we're going to apply a filter to it. In order to do that, we need to click on the Filter icon and we're going to create a new folder. And then we're going to select the column on which we want the filter to be applied to. For instance, we are going to apply a filter on the Region column. And now I'm going to click on these three dots and click edit. So here we have an option of include. If I drop it down, we can exclude and me can include. So let's say that I want to include us, but not other regions. Or for instance, I want to include us and IBAC, but not EMEA. Now, when I will click on apply, this filter has been successfully applied. These are the regions that we have and the count off active item Ba salesperson is visible to us. So if we change the include to exclude and then apply. So you can see on the contrary now we have EMEA, but not us and APIC. So that's how you apply filters to a visual. Thank you for watching the video. 26. Lab 015 Types of Filters: Hi guys. In this video we're going to explore different types of filters and we have the same visual and damped as in the previous lab. So we're going to go to the Filter icon, Create a New Filter that too on region. And now let me just edit this filter by clicking these three dots and clicking on edit. So here we have the filter type. So we explored the very first dive in the previous lab as well, which is the filter list. When this option is selected, we have a list of the items from which we can select or deselect the value. The next type is a custom filter list. So unlike a filter list, we don't have a filter list to select from. However, we need to type the items and one item is bar value. So let's say I only want to include a PAC and I want to include us. And then I'll click on apply. So this is how the custom filter list works. So instead of just electing, We need to type the items. The next is customFilter. So here instead of include and exclude, we have multiple options. Witches equals does not equal starts with ends with contains and does not contain. So let's just see what ends with actually does. So let's say I want to have all of the regions which end with an s And then apply so our changes visible and the only region that ends with an S as us. So we have the field for that particular region. And lastly we have a top and bottom filter. So in a dopant bottom filter, we have the option of whether we want to show the rows from the top, but we want to show them from the bottom. And then we have to actually tell how many rows do we, do we actually want to show also we can say that by which field you want to apply this. So let's say I want to apply this by active item instead off the region and heresy that I wanted to show the d2 and then apply. So we have the top due according to the active item. And if I go to bottom and then apply, so our visual actually changes according to this filter. So we can close this filter. And other thing that we can do is actually select this and disable this. It will have no effect on the visual whatsoever. And lastly is that we can also select and deleted. Thank you for watching this video. Stay tuned because we will see how can we apply text filters next. 27. Lab 016 Text Filters: Hello guys and welcome to lab number 16. And in this lab we are going to explore the text filters. So text filters are basically applied on any textual field that we have. So here we have region, active item, anti-nodes, person, which are the textual field. So we are going to see that how can we applied filters onto it? So we'll go to the Filter icon and we're going to create a new file into, let's say that this filter is going to be on sales person. The next step is that we're going to edit this filter. So we explored all of the filter types in the previous lab. So let's just how one of this type is used to be applied to text filter. So let say I am going to go to custom filter. And here I'm going to use does not contain and the parameter that I'm going to provide it that it should not contain ER, and let's just apply. So now you can see that all of the sales person which do not contain ER in them have been displayed. Let's just try another type of filter, which is the customFilter list. And I am going to include and Smith and Victor book. And so I'm, I'm excluding these and apply. You can see that this filter has been applied, but the previous filter that we had, which was the names but the ER or to be excluded, that is now not applicable. So for that, if you want multiple filters to be applicable, we will have to add a new filter to this particular visual. So I'm just going to close this filter and we're going to create a new filter from here, which is due on the salesperson because we want both of the conditions to be satisfied. And from here I am going to edit this. And in my custom filter, I'm going to say that it should not contain. And now let us apply. I'm closing this. So you can see that both of these filters are applied to this particular visual is that it's excluding and Smith and vector book and also its only displaying the names of those salesperson which do not contain ER. So thank you for watching this video. So this is how you can apply multiple filters to a visual and how we can use text filters in our rituals. 28. Lab 17 Date Filter: Hello and welcome to lab session number 17 of our course. And in this lab session we are going to see how we can deal with the date field and how can we create Date filters. So they're slightly different from the text filter. But in order to manipulate this, we need to create a visual which contains the date field. So I am just going to double-click this date field. This is in the sales pipeline dataset and we're just going to drag and drop. So we have a dimension which is added. But let's just click on the pivot table because that way we will have a more clear picture off the filter. So now you can see that it has a debit column and it has account for each date. So doesn't make much sense as of now, but I would like to know the weightage revenue. So we have created revenue in the values field and we have date as the rules. So I'm not going to go into the columns complexity as of now. So you can see that we have multiple dates for January and for each date we have awaited revenue. But what if we want to see this revenue according to a year or according to a month. So for that, we will just click on this icon over here. It will drop down the menu and all of the options that we have for the date column. So the aggregate is either by year, by quarter, month, vk, they are minute. So since we wanted perhaps monthly, I'll click on that. Now you can see that our visual has immensely changed. So we have the Date column and we have the weighted revenue column. But now instead of a date by date weightage revenue for the month of John, we have a collective revenue for the month of January 2011. Now what do we want to see it on yearly basis? So then we change the aggregate to you. So here we go. Now we have all of the years cumulated weighted revenue. But since we are going to apply filters, so I am going back to months. And so you can sort it as well, ascending or descending, and you can change the format of the data as well. So let's just apply the filter. We go on to the Filter icon, we click on create one, and we want to create this on the date. So let's just edit it and see the options that it provides us. So now you can see that the filter type we have has been created. So Rehovot time range, we have relative dates and top and bottom set it o. So let's just explore the time range first. So in time range we have an option of between after, before, four. In between we can give a range, we have a start date and we have ended. So for instance, I say that I want to see all of the data from from first July 2015, and I don't want to include the time and I want to see it till December 2020. So it has the option of whether you want to include the start date or you want to include the end date. And if we don't select these checkboxes, it will exclude them. And whether you want to exclude the nulls or include the nulls, or you only want to have the novels. So lets just see for 2015. So here we have July 2015 and it's going up to 2 thousand. 16. Now let's just apply this filter to our visual and has now been changed. And now we can see the data from July the 15th to December 2016 because apparently that has the values up to that point. So that is how you walk with the between Picchu. Similarly, we can walk with after and before equals. So this was the time range. What have we have relative dates? So in the relative dates filter type, we have the date and time series which are relative to the current date by default. But we can also include a date and time from the pedometer. So since our current date and time is way ahead of the date and time which is available in this visual. So we're going to go ahead and create a parameter first. So I'm going to click on this icon and select Create one named this bottoming dot as d. Then select It's type, which is date and time, and give it a static value, we can set it dynamically as well, but for now I'm just going to give it a static value. Click on this and a date picker arrives. So let's say I want this to be in 2013 and the month is October. And I'm creating this and then I'm connecting it to the filter. And here we are going to select this instead of the Quran date and select a pedometer. So it's giving us the option whether you want the unit does matter to be in years or you wanted to be in quarters, months, weeks, days, or hours or minutes. So let's say we wanted to be and we want to see the previous year. So you can see previous yellow, you can see this year, year-to-date last n years of what? To the, to the particular anchor point or next NGOs to whatever anchor point you have a set as the palate. So I'm just going to click on apply. So the previous year, 2013 is 2012, and we can see the data for that year only. Now what if we change this to the CIA and now Apply? Now we can see 2013. So you can also change it to next Enya's. And over here we can give the number of years to be two and let's apply. So here we can see 2014, which is next to 20132015 setup. So that is how we can walk away around relative dates. And the last type that we have this top and bottom third, third, and it works similar to the way that we saw how this works for text filters. So I'm just going to give an overview. So for instance, I want to see the talk and I want to see about den. And let's say months. Now when I, now when I apply, sorry, I need to give this biofield as well. So this is by date and apply. So it's giving me the top ten months. And this is by date count. You can change the aggravate to max. So this is for the maximum, for the maximum dates. And you can also change this to probably waited revenue and change this to, let's say max. And now let's apply. So here we can see the maximum weighted revenue for the top ten dates. So thank you for watching this video. In the next video, we are going to discuss the numeric filters. 29. Lab 18 Numeric Filters: Welcome to lab number 18, guys. And in this lab we are going to see how we can work around with numeric filters. So I've just created a new sheet and I'm going to create a new visual as well. So the visual is going to be a pivot table. We're going to have segment as the rows salesperson in the column. And we are going to have the forecasted monthly revenue as the value. The aggregate is some. So let's just expand this. This is our visual and we're going to apply the filter onto it by clicking this icon, I'm going to create a new filter by clicking on create one. Cinematic filters are to be applied to numeric fields. And the pneumatic field that we have over here is forecasted monthly, revenue is latest and weighted revenue. So since we have accosted monthly revenue in the visual, so it's better to have a filter on this. Now let's just edit. So here we have different options when we're dealing with the pneumatic feeds. We have a no aggregation option. We have an option for some average count, count distinct, max, median, men, percentile, standard deviation, standard deviation, population variance, and variance population. So for instance, we have selected the sum as the aggregate. And the next options of conditions that we have as equals does not equal greater than, less than, greater than or equal to, less than or equal to orbit. So for instance, we want it to be between. So we ought to select a minimum value and the maximum value. So some random values that I'm selecting, our denise, Okay, and so here you can see that our visual has changed and it's showing me the result for those segments and those salesperson who have a forecasted monthly revenue, some in-between these two maximum and minimum values. So only these are visible the restaurant. Similarly, we can work with average and count and count distinct and so on and so forth. So thank you for watching the video. This is all about voters. And in the next lab session, we are going to learn about controls. 30. Lab 019 Date Controls: Hi and welcome to lab session number 19. This lab session is regarding how we can add controls two parameters. And in this lab we are going to use the parameter that we created in order to explain the deep Pinto. So we have the date style meter over her. Click on this drop-down option and click on add control. Now you need to name your control. Let's see, we want to name it as DPT, or let's say we see select today. So we have a style and the only style that we have is a date picker. So we just add this. So the addition that you can see in this visual is that we have a control and we have an option of selecting the date. Now previously, we entered the date in the parameter field that MY added this pedometer. So heavy added static default value. And every time we wanted to change, we have to set this value. But now with the help of control, we don't need to go into the parameter setting. Rather, we can change this in the control option. So here, instead of October, if I give it October 2014, let's say first. So it has changed. And now you can see that the filter that we applied, let me just take you to the preview. So here we have the previous year date filter. So you can see that because of the control, instead of 2012, we do see 2013 as the previous year. So that is how we add controls which are with respect to date. In the next video, we're going to see how we can add numeric controls. 31. Lab 020 Numeric Controls: Hi and welcome to lab number 20. And in this lab we are going to see that how can we apply numeric photos? So we have a visual over here that we also used for applying the pneumatic photo. So I'm going to play a new filter to it that to create one. And that will be on obviously forecasted monthly revenue. And let's just edit the photo. So for instance, the sum not equals, but let's say greater than. And now instead of a value, let's just use about a meter because we're going to add a control to the pedometer and no bad ammeters added yet. So we're going to create a pedometer. So let's just go to five meters first and our divided meter, and let's just name this Bonomi dark as the sum. And this is going to be a number. It's going to take a single value and let's just enter or default static value and click Create. So we're going to use it with a combination of the filter. I'm just clicking here. Now we have to filter. And here instead of equals, I'm going to do greater than and clicking on the option, I'm just going to click on yes. So it is showing me the sum for all of the values which are greater than the value, aesthetic value that I entered in the batter meter because we're using the pedometer. I'm just going to select this and then I'm going to apply. So you can do that. It hasn't filled it anything. So we can change the value of that static Vita meter by going to the parameters option and then edit by meter and then change the value and update this. So you can see that the values have been filtered out. But instead of going again and again to the power meters and changing the default value, I can do is add or control, named this control as select the number, make it a text field. You can make a drop-down list slideshow. So let's just go with the slideshow. So we have to add a minimum value and maximum value on which the slider will operate. So this is my minimum value. These are some random values, and this is my maximum value. I am including the step size over her and adding this. So here we have a select number control in our visual which was previously not present. Now coupon to filter this, we will just click this and right now we stand here. But you can increase the number and the visual will change and filter the values accordingly. We can also decrease this. So this is the amount of flexibility that AWS quick site provides us with. So in the next video, we're going to see that how we can walk around with the text controls. 32. Lab 021 Text Controls: Welcome to lab number 21, which is the last lab for this section. And in this lab we are going to see that how we can add control to a text field, to a basically a text bottom Ido. So we have this visual over her where we have the event on one side and we have the Twitter mentions on the other four and these two dimensions and according to the events. So I'm going to add a filter by clicking, by clicking this filter option and clicking this ad pelted option, the filter is going to be on event and we're going to edit this. So we only want this filter for this particular view. And instead of filter list, let's just do a custom filter. And instead of equals, let's select contains and use about a meter. So it doesn't have anatomy to right now of which is of type text. So we are going to add Academy from it and name it as the sub string. You can name it whatever you feel like. So it's going to be a string type and a single value. And the value that I'm going to enter is new and I'm going to create this. So we're going to combine it with a filter. So here instead of equals min and offenders, we're going to do contains used the fat in Edo new and selected as syllabus doc string basically. And then we're going to apply. So it has filtered all of the events which contain the substring witches nu. So if you want the substring to change, we'll have to go to the parameters again and then edit the settings of the parameters. But let's just add a control duit. And here named this as select substring or type substring. So we have an option of drop-down list and text field. So if the select dropdown, then we have to end at the specific values that you want your drop-down list to have. So we can have NEW. Another item that we can have in our dropdown is promo, or for instance, mobile salacious, keep this up to these many values up to now, and click on add. So here we have an option of select substring so we can select Oil, new mobile, oh, promo. If I select promo. So you only have these two events which contain promo in them. And if I select mobile, so we have only one event which contains mobile. And if I click on oil than we have all of the events of air. So that is how we deal with controls using the text pedometers. 33. Lab 022 Pie Chart: Welcome to Section nine or four cores. And in this section we're going to see that how can we create the crop visualizations on earlier, we saw the theoretical part of this, but now we're going to practically demonstrate all of the visuals that we discussed earlier. So the very first graph that we have is a biker off the data that I'm going to use as sales pipeline data to demonstrate this and the category that we're going to use in our pie chart. Let's just select the pie chart from Yo and go to visualize here, I'm going to select the region. So by default we have the count of records which represents the percentages. And when I hovered over one particular agenda, gives me the count. But by default, it doesn't provide me with any count or any such metric information. So if you want to change that, we can go to settings. And here we can go to Data Labels. From there we can go show matrix. And here in metric level and style, we have an option that whether you want the value and percent to be displayed, which in this case you can see the value and the person's age. Or you only want the person's age, or you only want the values. And also whether you want the position of these values to be outside or inside. That is how we can play around with this. But this is by a count of the records by region. Let's just select this. We can change the value as well so we can add a measure, do it. So if I go to visualize, I have to feel it list. So if I want the measure to be forecasted monthly revenue, I'll just click on the fields well, and from her drag and drop into the value. So here are our values have changed according to the forecast admin, monthly revenue according to that particular region. So the aggregate that by default is the Sama and v can change the aggregation as well to count, to count distinct or for instance max. So this will change the view of our pie chart. 34. Lab 023 Bar and Column Chart: Hello and welcome to lab number 23 of our course. And in this lab you're going to see that how can we create a bar and column chart? So let's just use a different dataset for that. I'm going to click on this pencil icon in order to add new dataset. Click on added a button and select Business Review, which is one of the built-in dataset. And click select. So we have our new dataset for visualization. Let's just select segments. So we have to segment and we have the found of the records by segment. And now let's just select the horizontal bar chart. And then we have revenue goal, which is going to be selected as a value. So instead of count off the records, we have the sum of revenue now as the value measure. And the by default aggregation for this is the sum and this is according to the segments. So we can change its view to a vertical bar chart as well by clicking this icon. And we can add the color dimension as well in order to just given more insights in details into our chart. So let's just do that. Add a channel. So here you can see that now we have channel, which is in the legends. We have a VIP channel, Mobile and API. So from this chart we can deduce that the web channel has been found to be most effective in order to retrieve the highest revenue goal by this segment of startup. By looking at this graph, we can easily see that web proves out to be most effective in all of the segments. So this is for the bar chart and the column chart. In the next video, we're going to see how we can use a line chart. 35. Lab 024 Line Graph: Hi and welcome to lecture number 24. And in this lab we are going to see that how can we add a line graph? So keeping intact the previous visual that we have, we just going to add another visual by clicking this ad option and clicking on add visual button. So it's autographed by default, but we want it to be a line chart in the fields will be have the option of x-axis value and color. And since the line graph shows the trends over time, so we're going to select a date as an x-axis. And next we're going to select a value. Let's say we want to select revenue goals and also we want to select the cost. Let's just expand this for a better view. So, so here we have the trend line and we can click on this date option and change the aggregate to month, quarter, year because D is giving a lot of information and the information is cluttered. So let's just go to year. So here we can see that in 20122013, our revenue goal and cost is almost similar. But in the years to come that is 20141516, we have a higher revenue goal than the cost. So we do have some margin of profit over here. This is our cost and this is our revenue goal, and this is births a year. You can also see it as per the quarter. So now this is on the basis of quarters. So this is how we can see the trends with respect to a line graph that aggregate for now is some for both the revenue and the cost. And we can change the aggregation to averages well, for both. So now this is based on the average aggregation. There's just go back to you. And now you can see in terms of average, then again, our cost is built on the lower side than the revenue goal. So this is it for the line graph. 36. Lab 025 Area Graph: Come to lab number 25. And in this lab we are going to see that how we can work around the area chart. So I'm going to click on add an add visual and going to select Area line chart. So it's selected. So on the x-axis we want date in order to show trends, since it's the combination of the line shot and the area chart. And the value that I want to add is the build amount and the color is from the channels. So let's just expand this and change the aggregate to you. So now you can see that the area is she did for all of the channels. So we have a shaded area in blue for API, in little bit darker blue for mobile and beaches, orange for web. So this shows that the highest build amount in terms of some has been for web. And that has increased over the years and has remained highest amongst all of the three channels. Second is far more vile. And lastly, we have the bill amount for API. So this is according to the year. We can change it with respect to quarter as well, still stays the same. Let's just see what the trend is for the month. So it's almost giving the same information is that web has the most Build amount by date. So this is the information that we can gather from an EEG urea and line chart combination. 37. Lab 026 Stacked bar and column chart: Hello everyone and welcome to lab number 26. And in this lab we are going to see that how we can work around with a stack bar or a column chart. So I'm just going to add a new visual. This is autograph. For now. You're going to select this. I can put a horizontal stack bar chart and this one little vertical stacked bar chart. So let's just select the vertical stacked bar chart. Now we have the fields, well, we have x axis value and the group Alcala in the x-axis. I'm going to add the customer region and expand this in the value or the y axis, I'm going to add the revenue goal. So for now we can see the revenue goal and the sum for the revenue goal for the customer login. But let's just add the solvus line to it and see the change. So now we have the revenue goals, some, but this is now divided amongst the syllabus lines. So the billing department has this much amount of revenue goal, which is bird the region, us and dispatch is a full EMEA and this is for ABC and Soyuz for marketing. And then also we have for each off. So we can also change the aggregation to let say average. So now you can see that on average are bars have changed slightly. So on average, HR has this much amount of revenue goal per region, APSAC, and this is for marketing and this is forbidding. Let's just go to the sitting off this chart. So here we do have an option of showing the data labels or disabling them. And we can also add a reference line. Reference line could be calculated or it could be constant in a calculated line, you need to select a column. So if I select Cost and average, so this is my reference line for the cost. So this is how you can also add an average line to your chart. 38. Lab 027 Combo Charts: Hello and welcome to lecture number 27. And then this lab, we're going to see that how we can walk around with a combo chart. So I'm going to add a visual. So Combo Chart is basically a combination of two or more charge. For instance, we saw a combination of line and area chart. And now we're going to see a combination of the stacked bar chart along with the line chart. So in the field, well, you can see we have an x-axis, we have the bars graph and also an additional field off lines. So let's just add the x-axis. Let's just say that we adding segments to be sixes and on the bars we are adding the revenue goal. And in the color, I'm going to add the channel. So here we have the jhanas and we have the revenue goal, the sum, we can change it to average, and we have the segments. So we don't have anything added into lines, but we can add lines to show the trend for build amount. And we can also see this for multiple fields. So if I want to add cost to it, I can go ahead and add another line. So we have a line off build amount which is green and one for the cost which is purple. So by default it's some, we can change the aggregate to max for the cost. Let's change it to max as well. So here, if I am going to expand this to get a better view, and if I hover over this area, you can see the web, mobile and API values for average revenue goal. And you can also see the maximum build amount and the cost for the startup. And if I go to goods, SMB, the same information will be visible for SMB. And lastly, if I go to enterprise, you can see the information for enterprise. So now we have some additional information. He was in this combinational job too. If you want more information to be displayed for your jobs, you can always move towards combinational jots. Thank you. 39. Lab 028 Tree maps: Hi guys. In this lab we are going to see that how can we create a tree map? But let me just tell you what a tree map actually is. So to visualize one or two majors for the dimension, we use treemaps. Each rectangle on the tree map represents one item in the dimension. The rectangle size represents the proportion of the value for the selected measure that the item represents compared to the hole for the dimension. So let's get started. So the data I have is of Business Review and I am going to add a new visual and I'm going to select tree map as the visual. So in the field, well, we have the group by dimension according to which the rectangles will be drawn. We have the size, which will determine the size of the rectangles. And if we don't give the size measure by default, it will be the count. And lastly, we can also add a measure which will change the color of the rectangles. So the brightest color will be the high intensity of that particular number that will set as the color. And if the color is light, that means that that value for that particular rectangle is low. In case of this measure that we'll add in the color, the dimension that we're going to use in the group BY is the segment. Here we have three segments and we're going to give this the size by the revenue goal. So by default it's the sum. And lastly, in the color, we can add cost. Let me just expand this. So here you can see that the revenue goal is highest for startup then for enterprising, lastly for assembly. And so is the cost because the lightest color is off cost is for SMB and that colors slightly varies in, in density for Enterprise. And then for startup we have the darkest color. So that is how we can make a TreeMap. Let's just make another visual. So let me collapse this. Let's just change the data set now, the dataset that I'm going to use as for web and social media. And we are going to create a visual and we're going to create a visual for events. And this is going to be a treemap. So all of the rectangles, they represent the event. So here we have the empty event as well, which means that there is no event assigned to 655 records. So your task is to just go ahead and to filter and change this and filter the empty ones out since we have dealt with the filter. So I hope that you'll be able to finish this task. The next thing is the size. So let's just add the website visits to size and to color. I'm going to add the vector mentioned, expanding this to see the entire view. So here is our graph. We have the empty box, which has this much amount of the website visits. And then we have the CPC campaign and then target outreach campaign, and then new website promo and so on and so forth. And then density varies according to the Twitter mentions. So this was all about treemaps. In the next video, we're going to see how we can deal with geographical plots. 40. Lab 029 Geographical plots: Hi guys. In the last lab of the visualization and graphs, we're going to see that how can we create a geographical plot? So we're going to import new data. I'm going to click on this icon because the steady offset as not built-in, I'm going to click on this dataset option, and I'm going to click on the new data set button and upload the file. So I have the sales data which I'm going to select and open. The file has been uploaded and I'm going to select this and edit in preview. So here we have this branch data and this is the sales data for a pizza chain. And here I am going to change my branch datatype from string to state. You have to beat Zai Di behalf quantity. That time is also in string and the time range, I'm not going to change any of these, and I don't want this EDI number. So I'm going to disable duct and I am going to save this. Now let's just go back to analysis. And we have our last webinar social media analysis that we worked on. And I am going to add a new sheet, could include sort of graph. So let's just add the dataset and Add Dataset hair, select the sales data and select. So we have sales data available for visualization. And I'm going to select this. Actually just going to remove this visualization. Now I'm going to change my dataset. And now let's just click on the branch data and change this to the points on maps in a geographical plot, we have the Jewish special column in which we enter the location, the longitude or the latitude, and we have the size. Let me just close this, which is the measure for these bubbles. And we can also add color and give more meaning to the visual. So the size is, right now it's based on the account. So you can see that there's not a lot of differences. So the size, hence is not very prominently varying. So I'm going to add a calculated field now because we have price and we have quantity. So we're just going to multiply the price and quantity in order to get the sales. And this seals is going to be popping genotype. So I'm going to click on add and then add a calculated field. So we're going to name this field as seals. And the formula that we have is quantity times the price. Now, I am going to save this. So here you can see that in the Fields list and other field has been added and the name of the spill as seals. Now let's just drag and drop it in the size field, the size dab, Okay, so here, so the sum of the sales over here is 39340 and here 37,332. And here it's about 35,884. So according to that, you can see that the dots have changed. So we can also add the color dimension for the pizza type. Here is our legend. So according to the legend, when we hover over the dot, we can see all of the data according to that particular geospatial location. So in Iowa we have barbecue Phyllis desc, margarita Horton, hot pepperoni feast, and barbecued chicken according to these sales sum. So this is how we create a geographical plot. 41. Lab 030 Dashboards: Welcome to the last section of this course. In this section we are going to discuss what a dashboard is. We are going to see how we can create a dashboard, how we can publish it, and how can we share it with appears. So a dashboard basically is a tool which has to do with what information management and business intelligence. It's much like a dashboard of a car. Data dashboards organize, store, and display important information from multiple data sources into one easy to access place. We are at our homepage of quick side. And from here we're going to go to the Dashboard tab. So here we don't have any dashboard currently. Let's just go to analysis. And this is the last analysis we worked on. We can rename this from here. So let's just rename it as my analysis. It will auto save automatically. So we have an option of share. We can publish a dashboard and we can share the analysis for now I'm going to publish this dashboard. I'm going to publish all of these visuals under the name my dashboard and publish the dashboard. So here we have an option of sharing the Dashboard. We can type in the username or email of appears. So we can also manage the dashboard accesses. And so this is our dashboard that contains all of the sheets that we worked on and contains all of the visuals that will in the sheet. So we have an optional vigor of data. From there we can see all of the data sets that have been used in this dashboard. We can also save it under a new analysis name and we can share our Dashboard. We can email the report. Lastly, we can print it in a form of landscape or portrait orientation. We can also change the paper size accordingly. So that's it for our course. Thank you for watching. 42. Capstone Project updated: Hi everyone and welcome to the capstone project of our course. And in this section we're going to use a data set and make an analysis out of it. At last, we are going to publish this analysis as a dashboard. So without further ado, let's get to it. Very firstly, I am going to go to datasets. The datasets that we've already imported includes financial seal samples, yields data, and some built-in datasets. The dataset that I'm going to use is financial sample new. Let me just click onto it and edit the dataset. So this is our data set. We have the segment country, product discount band. Units sold, manufacturing price, sales price cross sales discounts, sales, cost of goods sold, profit did month name, number. And so what are we going to do with this data is first of all, we're going to prep it. So we have the segments as strain the countries also a String product is also a string. And then we have the discount band unit sold is saved as decimal, but sales we have as a string. So you can see that all of the numbers from here onwards, since they contain the currency sign, they're taken to be as strings rather than as number or decimal. So the first thing that we're going to do is change these two decimal. So for that, I am going to add a calculated field. Name, this calculated field as Thiel's New. And here I am going to replace in the Sales field the dollar sign with an empty string. Close the parenthesis, and I am going to parse this as a decimal. So all of the functions are key sensitive. Everything looks good. Let me just remove these braces and save it. A bracket is missing. Now let me just save this and write, we're done. So here we have our calculated field which has seals new. And you can see that sales knew as decimal. This is the sales field which has saved as a string. And this is the sealed new field which is now saved as a decimal. So let's just do it one more time for the sealed price and went to Article cluttered field, name it as seal prize. New. We are going to parse decimal. We are going to replace the sale price. Pattern see sign with an empty string and safe. So we have our new calculated field added. And let's just do it with cost of goods sold as well. So I'm going to add another calculated field. Taking the abbreviation Cost of Goods Sold new, and seem to parse and replace costs sold. Dollar sign with an empty string. And so I think this is enough for us to deal with. We can select the manufacturing price if we want that. We can easily get inequality if you don't want that. So I am just going to save and visualize. So this is the page that is opened in front of us in which we are going to perform the analysis. Let's just name the analysis as financial report. The first thing that we're going to do before adding visual areas that we're going to add a title to this analysis. And let's say our title is financial report. So this is our autograph and just actually selected this. And I want to see country versus the total sales. So here we have the count of records by country, and I want to see it bolsters the total sales, which is going to be from sales new. So I've selected that. That is our field weld. So on our x-axis we have the country. On our y-axis we have the value of sales, which is the total sales. And this is aggregated as the sum. We can change that to average or to count or count distinct. So let's say we want to see the average. Let's just rename this average sales. Country. Looks good. So we're going to add another visual. In this visual, let's just see the segments. And let's just see them by the units sold. And I want to see the units sold with respect to the average. Now let's just convert this into a pie chart. So we have the average of units sold. And if we want the integers to be visible, we're going to go to the settings of this fish will. And in the data labels, we are going to. Select show metric. And here we only want the person. And how about we wanted inside? Looks good. So now let's just go ahead and add another visual. In this visual we're going to see the discount band and the cost of goods sold. And we are going to keep it as account. And let's just change this to a donor chart. So in total we have 700 products which are sold. And this is the count for all of them and the percentages accordingly. Let me just change the title. Discount band versus cost of goods sold. Alright. So lastly, let's just add another visual. And here, let's just keep it as a vertical bar chart. Now here we are going to add the product, which is our x axis. And next we are going to see the total sales for each product. So sales is the value and the aggregation over here is some. So we have the total sales by product. And lastly, let's just add a visual, which is going to be a line chart. And here we are going to see the trend with respect to the date. So for now it's count of records by default. But the value that we want to see is the total sales over a trend line. And let's just say we want to see the average total sales or whether trend line. And we are to change the date aggregation to E0. So we have two years. Let's just say we are going to change it too much. So over the months, we have this trend line right here. Let's just expand this. So for now we only have one single trend line, which is the total sales trend line that we have. And let's just change. Rename this. And let's just rename this to average sales. So this is our trend line for the average sales. But how about we want to see the average sales for the segments as well so we can drag and drop the segment into color. So now we have multiple trend lines and these trend lines, and according to the segments, you can see the legend over here. And this is for the average sales less, just rename this average sales per segment. So here we have Everett seagulls in behalf of all other visuals, and it gives us a good insight into the financial report. We have the average sales per country, and we have the average of each unit sold by each segment. Next, we have the brand discount versus the cost of goods sold. So the total goods sold for 700 and the rest are displayed over here. Next we have the total sales by product. And lastly we have the trends over time per segment for the average sales. So we have our report ready and now let's just go ahead and publish this as a dashboard. So I'm going to click on Share and click Publish dashboard. I'm going to name this dashboard as the financial Dashboard. And I'm going to publish the dashboard. So at the moment I don't want to share it with anyone. And this is how our dashboard looks like. Pretty good rate. So thank you for watching the capstone project and I'm sure that it taught you a lot.