Learning Python for Data Analysis and Visualization

Learning Python for Data Analysis and Visualization
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