Learning Python for Data Analysis and Visualization Ver 1
About Course
This **free** Python data analysis course from Udemy will teach you how to analyze and visualize data using Python, making it a great starting point for a career in Data Science.
Learn how to program with Python and use scientific computing modules and libraries to analyze data. This course includes lifetime access to over 100 example Python code notebooks, updated videos, and future additions of various data analysis projects that you can use to build your portfolio.
By the end of the course you will:
- Understand how to program in Python.
- Create and manipulate arrays using NumPy and Python.
- Use Pandas to create and analyze data sets.
- Create beautiful data visualizations using Matplotlib and Seaborn libraries.
- Build a portfolio of example Python data analysis projects.
- Gain an understanding of Machine Learning and SciKit Learn.
With over 100 lectures and 20 hours of information, you will be well-prepared for a career in data science. This course is completely **free** and is available on Theetay, which offers top-rated courses from platforms like Udemy, Udacity, Coursera, MasterClass, and NearPeer.
What Will You 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.
Course Content
Intro to Course and Python
-
A Message from the Professor
-
Course Intro
03:52 -
Course Material Download Link
00:00
Setup
-
Installation Setup and Overview
07:16 -
IDEs and Course Resources
10:56 -
iPythonJupyter Notebook Overview
14:57
Learning Numpy
-
Creating arrays
07:27 -
Using arrays and scalars
04:41 -
Indexing Arrays
14:19 -
Array Transposition
04:07 -
Universal Array Function
06:04 -
Array Processing
21:48 -
Array Input and Output
07:59
Intro to Pandas
-
Series
13:58 -
DataFrames
17:46 -
Index objects
04:59 -
Reindex
15:54 -
Drop Entry
05:41 -
Selecting Entries
10:22 -
Data Alignment
10:14 -
Rank and Sort
05:38 -
Summary Statistics
22:35 -
Missing Data
11:37 -
Index Hierarchy
13:32
Working with Data Part 1
-
Reading and Writing Text Files
10:03 -
JSON with Python
04:12 -
HTML with Python
04:36 -
Microsoft Excel files with Python
03:51
Working with Data Part 2
-
Merge
20:31 -
Merge on Index
12:36 -
Concatenate
09:19 -
Combining DataFrames
10:20 -
Reshaping
07:51 -
Pivoting
05:31 -
Duplicates in DataFrames
05:54 -
Mapping
04:12 -
Replace
03:15 -
Rename Index
05:55 -
Binning
06:16 -
Outliers
06:52 -
Permutation
05:21
Working with Data Part 3
-
GroupBy on DataFrames
17:41 -
GroupBy on Dict and Series
13:21 -
Aggregation
12:42 -
Splitting Applying and Combining
10:02 -
Cross Tabulation
05:06
Data Visualization
-
Installing Seaborn
01:44 -
Histograms
09:19 -
Kernel Density Estimate Plots
25:58 -
Combining Plot Styles
06:14 -
Box and Violin Plots
08:52 -
Regression Plots
18:39 -
Heatmaps and Clustered Matrices
16:49
Example Projects
-
Data Projects Preview
03:02 -
Intro to Data Projects
04:34 -
Titanic Project – Part 1
17:06 -
Titanic Project – Part 2
16:08 -
Titanic Project – Part 3
15:49 -
Titanic Project – Part 4
02:05 -
Intro to Data Project – Stock Market Analysis
03:13 -
Data Project – Stock Market Analysis Part 1
11:19 -
Data Project – Stock Market Analysis Part 2
18:06 -
Data Project – Stock Market Analysis Part 3
10:24 -
Data Project – Stock Market Analysis Part 4
06:56 -
Data Project – Stock Market Analysis Part 5
27:40 -
Data Project – Intro to Election Analysis
02:20 -
Data Project – Election Analysis Part 1
18:00 -
Data Project – Election Analysis Part 2
20:34 -
Data Project – Election Analysis Part 3
15:04 -
Data Project – Election Analysis Part 4
25:57
Machine Learning
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Introduction to Machine Learning with SciKit Learn
12:51 -
Linear Regression Part 1
17:40 -
Linear Regression Part 2
18:21 -
Linear Regression Part 3
18:45 -
Linear Regression Part 4
22:08 -
Logistic Regression Part 1
14:18 -
Logistic Regression Part 2
14:25 -
Logistic Regression Part 3
12:20 -
Logistic Regression Part 4
22:22 -
Multi Class Classification Part 1 – Logistic Regression
18:33 -
Multi Class Classification Part 2 – k Nearest Neighbor
23:05 -
Support Vector Machines Part 1
12:52 -
Support Vector Machines – Part 2
29:07 -
Naive Bayes Part 1
10:03 -
Naive Bayes Part 2
12:26 -
Decision Trees and Random Forests
31:47 -
Natural Language Processing Part 1
07:20 -
Natural Language Processing Part 2
15:39 -
Natural Language Processing Part 3
20:48 -
Natural Language Processing Part 4
16:16
Appendix Statistics Overview
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Intro to Appendix B
02:44 -
Discrete Uniform Distribution
06:11 -
Continuous Uniform Distribution
07:03 -
Binomial Distribution
12:35 -
Poisson Distribution
10:55 -
Normal Distribution
06:24 -
Sampling Techniques
04:54 -
T-Distribution
05:09 -
Hypothesis Testing and Confidence Intervals
20:08 -
Chi Square Test and Distribution
02:53 -
Bayes Theorem
10:02
Appendix SQL and Python
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Introduction to SQL with Python
09:59 -
SQL – SELECTDISTINCTW
09:58 -
SQL WILDCARDS ORDER BY GROUP BY and Aggregate Functions
08:25
Appendix Web Scraping with Python
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Web Scraping Part 1
12:14 -
Web Scraping Part 2
12:14
Appendix Python Special Offers
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Python Overview Part 1
18:52 -
Python Overview Part 2
12:18 -
Python Overview Part 3
10:13
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