Advanced Data Analytics Course: Become a Data Analyst 2024

سرفصل های دوره

Comprehensive Data Science Skills for Machine Learning, Deep Learning, Artificial Intelligence using Python Programming

1. Introduction to Python and its Environment

1. 1. Motivation for learning Python

2. 2. Python Introduction

3. 3. Introduction to Anaconda and Jupyter Note Book

4. 4. Anaconda Installation

5. 5. Introduction to Jupyter Notebook Interface

2. 2. Introduction to Python Programming

1. 1 Syntax of a Programming language

2. 2 Newline Character

3. 3 Elements Keywords Identifiers

4. 4.Comments Statement

5. 5 Variable Assignment

6. 6 Data Type I in Python Programming

7. 7. Data Type II

8. 8 Type Conversion of Data Type

9. 9 Output Formatting and Input Function

10. 10 Operators in Python Programming

11. 11 IF Statements

12. 12 While loop Statements

13. 13 For loop Statement

14. 14 Break and Continue Statement

15. 15 Lists-I

16. 16 Lists-II

17. 17 Tuples

18. 18 Strings

19. 19 Sets

20. 20 Dictionary

21. 21 Functions

22. 22 Function Arguments and parameters

23. 23 Built-in Functions

24. 24 Recursive Function

25. 25 Lambda Function

26. 26 Modules, Package and Libraries

27. 27 File IO Operation

28. 28 Working with Python directory and files

29. 29 Exception handling with python

30. 30 Comprehension in Python

31. Project 1- Twitter Data Analysis

32. Project 2- Twitter Data Analysis

33. Project 3- Twitter Data Analysis

34. Project 4- Twitter Data Analysis

35. Project 5- Twitter Data Analysis

3. Data Visualization using Matplotlib

1. 1. Introduction to Matplotlib

2. 2. Line Chart Plot

3. 3. Plotting a Bar Chart

4. 4. Histogram and Scatter Plot

5. 5. Stack Plot and Pie Plot

6. 6. Subplots

4. Numpy for Data Science

1. 1. Numpy Introduction

2. 2. Numpy Array Creation

3. 3. Numpy Arange Reshape

4. 4. Numpy Array Conversion

5. 5. Accessing Array Values

6. 6. Numpy Operations

7. 7. Fancy Indexing and Sorting Arrays

8. 8. Array Products and Concatenation

9. 9. Broadcasting

5. Pandas for Data Science

1. 1. Pandas Introduction

2. 2. Pandas Series

3. 3. Pandas DataFrames

4. 4. Handling missing data

5. 5. Conditional Selection and Reindexing

6. 6. Data Input and Data Output

7. 7. Data Processing

8. 8. Grouping and Pivot table

9. 9. Concatenating Dataframes and Inserting Rows

10. 10. Concatenation and Merging Logic

11. 11. Merging and Joining

12. 12. Cartesian Product Between DataFrames

13. 13. Handling Duplicates in a DataFrames

14. 14. String Handling

6. Handling Dates using Pandas

1. 1. DateTime - Datetime Creation

2. 2. DateTime - Pandas datetime functions

3. 3. DateTime - Reading Dates with Informats

4. 4. DateTime Series- DateRange and DateOffs

7. Probability and Statistics for Data Science

1. 1. Introduction to Statistics

2. 2. Introduction to inferential Statistics

3. 3. Measures of Central Tendencies

4. 4. Measures of Dispersion

5. 5. Introduction to Probability

6. 6. Types of Probability functions

7. 7. Probability density function

8. 8. Cumulative Distribution function

9. 9. Skewness and Kurtosis

10. 10. Boxplot

11. 11. KDE plot

12. 12. Covariance

13. 13. Correlation and Causation

14. 14. Introduction to Linear regression

8. Exploratory Data Analysis for Data Science

1. 1. Exploratory Data Analysis using Classroom DataSet

2. 2. Exploratory Data Analysis using IMD Rainfall DataSet

3. 3. Exploratory Data Analysis of Real Estate Dataset

4. 4. Exploratory Data Analysis using IPL player performance Dataset

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تولید کننده: Udemy