Data Science: Mastering Python Programming for Data Analysis | Ahmed Mo IB | Skillshare

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Data Science: Mastering Python Programming for Data Analysis

teacher avatar Ahmed Mo IB, Software Engineer | Data Science

Watch this class and thousands more

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Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Watch this class and thousands more

Get unlimited access to every class
Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Lessons in This Class

74 Lessons (6h 10m)
    • 1. Introduction

      1:22
    • 2. Download and Install the working tools

      1:20
    • 3. Jupyter Overview + Markdown in Jupyter tutorial

      6:46
    • 4. Using Jupyter Notebook for coding with Python

      5:41
    • 5. Use Anaconda Prompt

      3:14
    • 6. Variables and Types Tutorial

      10:37
    • 7. Describe what's inside the code

      4:44
    • 8. Define Blocks and Avoid IndentationError

      4:19
    • 9. String full tutorial

      13:43
    • 10. Numbers, Math and f-string tutorial

      15:13
    • 11. Handling inputs and outputs

      4:29
    • 12. Structure Data using lists

      21:02
    • 13. Structure data using tuples

      10:31
    • 14. Structure Data using Dictionaries

      8:04
    • 15. Structure Data using sets

      7:21
    • 16. Comparing Values

      8:36
    • 17. Output from Logics

      6:48
    • 18. Conditional Statements

      8:55
    • 19. The while loop

      2:56
    • 20. The for loop in Python

      7:18
    • 21. Python Library Functions

      11:21
    • 22. User-Defined Functions

      7:30
    • 23. The Lambda Power

      8:50
    • 24. The break statement

      4:04
    • 25. The continue statement

      4:34
    • 26. for else statement

      2:14
    • 27. Project: App to Put all together

      1:43
    • 28. Core Python OOP: Classes and Instances

      12:01
    • 29. Core Python OOP: Exploring Inheritance

      10:10
    • 30. Comprehensions

      5:51
    • 31. Constructed modules and random

      6:37
    • 32. Doing mathematics

      4:46
    • 33. Doing statistics

      4:17
    • 34. Errors Exploration

      4:27
    • 35. Exceptions Playground

      6:44
    • 36. IO data in memory

      7:11
    • 37. Interacting with operating system data

      3:03
    • 38. Moving data files between directories

      4:41
    • 39. Data will be in the trash bin

      4:16
    • 40. Zipping and Unzipping Data

      6:49
    • 41. NumPy Level 1

      7:09
    • 42. NumPy Level 2

      5:03
    • 43. NumPy Level 3

      2:25
    • 44. NumPy Level 4

      3:44
    • 45. NumPy Level 5

      5:38
    • 46. NumPy Level 6

      3:49
    • 47. NumPy Level 7

      3:46
    • 48. NumPy Level 8

      3:18
    • 49. NumPy level 9

      3:07
    • 50. Pandas data analysis level 1

      3:49
    • 51. Pandas data analysis level 2

      4:41
    • 52. Pandas data analysis level 3

      2:18
    • 53. Pandas data analysis level 4

      3:31
    • 54. Pandas data analysis level 5

      2:52
    • 55. Pandas data analysis level 6

      4:30
    • 56. Matplotlib data visualization part1

      2:57
    • 57. Matplotlib data visualization part2

      1:31
    • 58. Matplotlib data visualization part3

      3:05
    • 59. Matplotlib data visualization part4

      3:13
    • 60. Matplotlib data visualization part5

      1:36
    • 61. Matplotlib data visualization part6

      3:01
    • 62. Matplotlib data visualization part7

      2:32
    • 63. Seaborn statistical graphs level 1

      3:41
    • 64. Seaborn statistical graphs level 2

      1:56
    • 65. Seaborn statistical graphs level 3

      1:16
    • 66. Seaborn statistical graphs level 4

      1:39
    • 67. Seaborn statistical graphs level 5

      2:30
    • 68. Seaborn statistical graphs level 6

      2:30
    • 69. Seaborn statistical graphs level 7

      1:49
    • 70. Python Programming resources

      0:39
    • 71. NumPy resources

      0:28
    • 72. Pandas resources

      0:24
    • 73. Matplotlib resources

      0:35
    • 74. Seaborn resources

      0:28
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About This Class

Hello and welcome to the Comprehensive Training Data Science: Mastering Python Programming for Data Analysis.

Data science is a very wide field, and one of the promising fields that is spreading in a fast way, also, it is one of the very rewarding, and it is increasing in expansion day by day, due to its great importance and benefits, as it is the future.

data science enables companies to measure, track, and record performance metrics for facilitating and enhancing decision making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability.

And the employment of data science and its tools depends on the purpose you want from them.

For example, using data science in health care is very different from using data science in finance and accounting, and so on. And I’ll show you the core libraries for data handling, analysis and visualization which you can use in different areas.

One of the most powerful programming languages ​​that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data.

This course you will cover:

  • Python tools for Data Analysis

  • Python Basics

  • Python Fundamentals

  • Python Object-Oriented

  • Advanced Python Foundations

  • Data Handling with Python

  • Numerical Python(NumPy)

  • Data Analysis with Pandas

  • Data Visualization with Matplotlib

  • Advanced Graphs with Seaborn

  • Instructor QA Support and Help

HD Video Training + Working Files + Resources + QA Support.

In this course, you will learn how to code in Python from the beginning and then you will master how to deal with the most famous libraries and tools of the Python language related to data science, starting from data collection, acquiring and analysis data, to visualize data with advanced techniques, and based on that, the necessary decisions are taken by companies.

I hope that you will join us in this course to master the Python language for data analysis and Visualization like professionals in this field.

Meet Your Teacher

Teacher Profile Image

Ahmed Mo IB

Software Engineer | Data Science

Teacher

Data and ML Software Engineer | Software Artist | | Online Course Creator/Developer.

I taught for more than 300,000 developers and engineers from over 175 countries around the world.

- Programming Languages: Python, R, JavaScript, Java and Go.

- Data Science: Data Analysis and Visualization tools and Libraries with Python, R, SQL and Spark.

- Databases: Relational and Non-Relational.

- Applied experience with many programming languages and tools, also a proficient knowledge and experience in Software Engineering and Data Science with skills to analyze, design and develop.

- Bachelor's degree of Electrical, Communications and Computer Engineering.

I'm always have a passion to develop my work and I like to simplify and clarify Software and ... See full profile

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