**Learning Python for Data Analysis and Visualization**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 21 Hours | 3.97 GB

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!

You’ll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!

By the end of this course you will:

- Have an understanding of how to program in Python.
- Know how to create and manipulate arrays using numpy and Python.
- Know how to use pandas to create and analyze data sets.
- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.
- Have an amazing portfolio of example python data analysis projects!
- Have an understanding of Machine Learning and SciKit Learn!

What you’ll learn

- Have an intermediate skill level of Python programming.
- Use the Jupyter Notebook Environment.
- Use the numpy library to create and manipulate arrays.
- Use the pandas module with Python to create and structure data.
- Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets.
- Create data visualizations using matplotlib and the seaborn modules with python.
- Have a portfolio of various data analysis projects.

**Table of Contents**

**Intro to Course and Python**

1 Course Intro

2 Course FAQs

**Setup**

3 Installation Setup and Overview

4 IDEs and Course Resources

5 iPythonJupyter Notebook Overview

**Learning Numpy**

6 Intro to numpy

7 Creating arrays

8 Using arrays and scalars

9 Indexing Arrays

10 Array Transposition

11 Universal Array Function

12 Array Processing

13 Array Input and Output

**Intro to Pandas**

14 Series

15 DataFrames

16 Index objects

17 Reindex

18 Drop Entry

19 Selecting Entries

20 Data Alignment

21 Rank and Sort

22 Summary Statistics

23 Missing Data

24 Index Hierarchy

**Working with Data Part 1**

25 Reading and Writing Text Files

26 JSON with Python

27 HTML with Python

28 Microsoft Excel files with Python

**Working with Data Part 2**

29 Merge

30 Merge on Index

31 Concatenate

32 Combining DataFrames

33 Reshaping

34 Pivoting

35 Duplicates in DataFrames

36 Mapping

37 Replace

38 Rename Index

39 Binning

40 Outliers

41 Permutation

**Working with Data Part 3**

42 GroupBy on DataFrames

43 GroupBy on Dict and Series

44 Aggregation

45 Splitting Applying and Combining

46 Cross Tabulation

**Data Visualization**

47 Installing Seaborn

48 Histograms

49 Kernel Density Estimate Plots

50 Combining Plot Styles

51 Box and Violin Plots

52 Regression Plots

53 Heatmaps and Clustered Matrices

**Example Projects**

54 Data Projects Preview

55 Intro to Data Projects

56 Titanic Project – Part 1

57 Titanic Project – Part 2

58 Titanic Project – Part 3

59 Titanic Project – Part 4

60 Intro to Data Project – Stock Market Analysis

61 Data Project – Stock Market Analysis Part 1

62 Data Project – Stock Market Analysis Part 2

63 Data Project – Stock Market Analysis Part 3

64 Data Project – Stock Market Analysis Part 4

65 Data Project – Stock Market Analysis Part 5

66 Data Project – Intro to Election Analysis

67 Data Project – Election Analysis Part 1

68 Data Project – Election Analysis Part 2

69 Data Project – Election Analysis Part 3

70 Data Project – Election Analysis Part 4

**Machine Learning**

71 Introduction to Machine Learning with SciKit Learn

72 Linear Regression Part 1

73 Linear Regression Part 2

74 Linear Regression Part 3

75 Linear Regression Part 4

76 Logistic Regression Part 1

77 Logistic Regression Part 2

78 Logistic Regression Part 3

79 Logistic Regression Part 4

80 Multi Class Classification Part 1 – Logistic Regression

81 Multi Class Classification Part 2 – k Nearest Neighbor

82 Support Vector Machines Part 1

83 Support Vector Machines – Part 2

84 Naive Bayes Part 1

85 Naive Bayes Part 2

86 Decision Trees and Random Forests

87 Natural Language Processing Part 1

88 Natural Language Processing Part 2

89 Natural Language Processing Part 3

90 Natural Language Processing Part 4

**Appendix Statistics Overview**

91 Intro to Appendix B

92 Discrete Uniform Distribution

93 Continuous Uniform Distribution

94 Binomial Distribution

95 Poisson Distribution

96 Normal Distribution

97 Sampling Techniques

98 T-Distribution

99 Hypothesis Testing and Confidence Intervals

100 Chi Square Test and Distribution

101 Bayes Theorem

**Appendix SQL and Python**

102 Introduction to SQL with Python

103 SQL – SELECTDISTINCTWHEREAND OR

104 SQL WILDCARDS ORDER BY GROUP BY and Aggregate Functions

**Appendix Web Scraping with Python**

105 Web Scraping Part 1

106 Web Scraping Part 2

**Appendix Python Special Offers**

107 Python Overview Part 1

108 Python Overview Part 2

109 Python Overview Part 3

**BONUS SPECIAL DISCOUNT COUPONS**

110 BONUS Special Offers

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