In this course you are going to learn about following topics

LEARN DATA ANALYSIS FROM SCRATCH
   Part I : Tools For Data Analysis
       Python Refresher
           01 Course Pre-Requisite
               Learn Coding From Scratch With Python3
           02 Ipython Interpreter
           03 Jupyter Notebook
            04 Python Refresher - Basic DataTypes
           05 Python Refresher - Collection Types - Lists
           06 Python Refresher - Collection Types - Dictionaries
           07 Python Refresher - Collection Types - Sets
           08 Python Refresher - Collection Types - Tuples
           09 Python Refresher - Functions
           10 Python Refresher - Classes And Objects
       Numpy Core Concept For Data Analysis
           Step 1 : Concept : Numpy Introduction
               What is Numpy?
               Why Use Numpy?
           Step 2 : Concept : Arrays Revisited
               Types Of Arrays
           Step 3 : Lab : Ways to Create Arrays
               1. Create Arrays Using Python List
               2. Using Numpy's Methods
           Step 4 : Concept + Lab : Numpy Array Internals
               Dimensions
               Shape
               Strides
           Step 5 : Concept + Lab : Data Types and Casting
           Step 6 : Concept + Lab : Slicing And Indexing
               1. Understand Slicing and Indexing 1-D Array
               2. Understand Slicing and Indexing Multidimensional Array
           Step 7 : Concept + Lab : Array Operations
               1. Common Operations On Arrays
               2. Commonly Used Functions for Numpy Array Operations
           Step 8 : Concept + Lab : Broadcasting
               Array Broadcasting Principle
               Understand Usage of Broadcasting
           Step 9 : Concept + Lab : Understand Vectorization
       Pandas Core Concept For Data Analysis
           Step 1 : What is Pandas
           Step 2 : DataFrames
           Step 3 : DataFrames Basics
           Step 4 : Handling Missing Data
           Step 5 : GroupBy
           Step 6 : Aggregation
           Step 7 : Transform
           Step 8 : Window Functions
           Step 9 : Filter
           Step 10 : Join Merge And Concat
           Step 11 : Apply Method
           Step 12 : DataFrame Reshape
           Step 13 : Calculate Frequency Distribution
   Part II : Data Analysis Core Concepts
       What is Data
       What is DataSet


       Types of Variables
           Definition

           Types of Data Types

           Why Data Types are important?
       How do you collect Information for Different Data Types
           For Nominal Data Type
           Ordinal Data
           Continuous Data
       Descriptive Statistics Concepts
           Types Of Statistics
               Descriptive statistics
               Inferential Statistics
           What it is?

           Concept 1 : Understand Normal Distribution
           Concept 2 : Central Tendency
           Concept 3 : Measures of Variability
               Range
               Interquartile Range(IQR)


           Concept 4 : Variance and Standard Deviation

           Concept 5 : Z-score or Standardized Score
           Concept 6 : Modality

           Concept 7 : Skewness

           Concept 8 : Kurtosis
               How it look like

               Mesokurtic
               platykurtic
               Leptokurtic
   Part III : Tools For Data Visualization
       Matplotlib Introduction
       Matplotlib Architecture
       Seaborn Plot Overview
       Parameters Of Plot
       Types Of Plot By Purpose
           1. Correlation

               Type Of Graphs In Correlation Category
                        Scatter plot
                       Counts Plot

                        Marginal Boxplot
                        Correlogram

                        Pairwise Plot

           2. Deviation
                    Diverging Bars

                    Diverging Dot Plot

           3. Ranking
                    Ordered Bar Chart

                    Dot Plot

           4. Distribution
                    Histogram for Continuous Variable

                    Histogram for Categorical Variable

                    Density Curves with Histogram

                    Box Plot

                    Dot + Box Plot

           5. Composition
                    Pie Chart

                    Treemap

                    Bar Chart

           6. Change
               Time Series Plot
                   The below time series plots all the the peaks and troughs and annotates the occurence of selected special events.
               Time Series Decomposition Plot
                   Time series decomposition plot shows the break down of the time series into trend, seasonal and residual components.
   Part IV : Step By Step Exploratory Data Analysis and Data Preparation Workflow With Project
       What is Exploratory Data Analysis (EDA)?
       Value of Exploratory Data Analysis
       Steps of Data Exploration and Preparation
           Step 1 : Variable Identification
           Step 2 : Univariate Analysis
           Step 3 : Bi-variate Analysis
           Step 4 : Missing values treatment
           Step 5 : Outlier Detection and Treatment
                    What is an outlier?
                    What are the types of outliers ?
                    What are the causes of outliers ?
                    What is the impact of outliers on dataset ?
                    How to detect outlier ?
                    How to remove outlier ?
           Step 6 : Variable transformation
           Step 7 : Variable creation

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Mukesh Ranjan

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