4.18 out of 5
4.18
19409 reviews on Udemy

Learning Python for Data Analysis and Visualization Ver 1

Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video!
Instructor:
Jose Portilla
200,319 students enrolled
English [Auto] More
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.

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!

  With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science! 

Please make sure you read the entire page to understand if the course is the correct version for you.

Intro to Course and Python

1
Course Intro

Get a basic overview of what you will learn in this course.

2
Course FAQs

Setup

1
Installation Setup and Overview
2
IDEs and Course Resources

More course info

3
iPython/Jupyter Notebook Overview

Learning Numpy

1
Intro to numpy

Take a quick glance at the links in the text and then move on to the next lecture for the video lessons!

2
Creating arrays

Learn to create arrays with numpy and Python.

3
Using arrays and scalars

Learn how to perform operations on multiple arrays and scalars!

4
Indexing Arrays

Learn how to index arrays with numpy.

5
Array Transposition

Learn several universal array functions in numpy.

6
Universal Array Function

Learn how to transpose arrays with numpy.

7
Array Processing

Learn different methods of processing arrays.

8
Array Input and Output

Learn how to import and export your arrays.

Intro to Pandas

1
Series

Learn about the Series data structure in pandas.

2
DataFrames

Learn about the DataFrame structure in pandas.

Important Note: If copying directly from Wikipedia does not work, paste the data into a word processor or NotePad Editor and then copy it from there and then run pd.read_clipboard()

3
Index objects

Learn how to index Series and DataFrames in pandas.

4
Reindex

Learn how to reindex in pandas.

5
Drop Entry

Learn how to drop data entries in pandas.

6
Selecting Entries

Learn how to select particular entries in a pandas data structure.

7
Data Alignment

Learn how to align your data in Python.

8
Rank and Sort

Learn how to rank and sort data entries.

9
Summary Statistics

Learn how to quickly get summary statistics in pandas.

10
Missing Data

Learn different ways of dealing with missing data in pandas.

11
Index Hierarchy

Learn how to create hierarchical indexes in pandas.

Working with Data: Part 1

1
Reading and Writing Text Files

Learn how to import and export text files with pandas.

2
JSON with Python

Learn how to import and export JSON files with pandas.

3
HTML with Python

Learn how to import HTML files with pandas.

NOTE: Install the following before this lecture, using either conda install or pip install:

pip install beautifulsoup4

pip install lxml

4
Microsoft Excel files with Python

Learn how to import and export MS Excel files with pandas.

Working with Data: Part 2

1
Merge

Learn the basics of merging data sets.

2
Merge on Index

Learn how to merge using an index.

3
Concatenate

Learn how to concatenate arrays,matrices, and DataFrames.

4
Combining DataFrames

Learn how to combine DataFrames in pandas.

5
Reshaping

Learn how to reshape data sets.

6
Pivoting

Learn how to create Pivot tables with Python.

7
Duplicates in DataFrames

Learn how to take care of duplicate data entries.

8
Mapping

Learn how to use mapping with pandas.

9
Replace

Learn how to replace data in pandas.

10
Rename Index

Learn how to rename indexes in pandas.

11
Binning

Learn how to use bins with pandas.

12
Outliers

Learn how to find outliers in your data with pandas.

13
Permutation

Learn how to use permutation with numpy and pandas.

Working with Data: Part 3

1
GroupBy on DataFrames

Learn how to use advanced groupby techniques.

2
GroupBy on Dict and Series

Learn how to use the groupby method on Dictionaries and Series.

3
Aggregation

Learn about Data Aggregation with Python and pandas.

4
Splitting Applying and Combining

Learn about the powerful Split-Apply-Combine technique and how to use it in pandas.

5
Cross Tabulation

Learn about cross-tabulation in pandas, a special case of pivot table!

Data Visualization

1
Installing Seaborn

Quick overview on installing seaborn. Use "conda install seaborn" or "pip install seaborn".

2
Histograms

Learn how to create histograms using seaborn and python.

3
Kernel Density Estimate Plots

Learn how to create kernel Density Estimation Plots with seaborn.

4
Combining Plot Styles

Learn how to combine histograms, KDE , and rug plots onto a single figure.

5
Box and Violin Plots

Learn how to create box and violin plots with seaborn.

6
Regression Plots

Learn how to create regression plots in seaborn.

7
Heatmaps and Clustered Matrices

Learn how to create heatmaps with seaborn.

Example Projects.

1
Data Projects Preview

Quick Preview for those interested in enrolling in the course!

2
Intro to Data Projects

Get an introduction to Github, Kaggle, and great public data sets!

3
Titanic Project - Part 1

Learn how to analyze the Titanic Kaggle Problem with Python, pandas, and seaborn!

4
Titanic Project - Part 2
5
Titanic Project - Part 3
6
Titanic Project - Part 4
7
Intro to Data Project - Stock Market Analysis
8
Data Project - Stock Market Analysis Part 1
9
Data Project - Stock Market Analysis Part 2
10
Data Project - Stock Market Analysis Part 3
11
Data Project - Stock Market Analysis Part 4
12
Data Project - Stock Market Analysis Part 5
13
Data Project - Intro to Election Analysis

Please Note: The second presidential debate was Oct 16 and not Oct 11. Oct 11 was the date of the Vice Presidential Debate!

14
Data Project - Election Analysis Part 1
15
Data Project - Election Analysis Part 2
16
Data Project - Election Analysis Part 3
17
Data Project - Election Analysis Part 4

Machine Learning

1
Introduction to Machine Learning with SciKit Learn

Learn about the Pydata Ecosystem and SciKit Learn and what Machine Learning is all about!

2
Linear Regression Part 1

Learn about the Math behind Linear Regression then implement it with SciKit Learn!

3
Linear Regression Part 2
4
Linear Regression Part 3
5
Linear Regression Part 4
6
Logistic Regression Part 1
7
Logistic Regression Part 2
8
Logistic Regression Part 3
9
Logistic Regression Part 4
10
Multi Class Classification Part 1 - Logistic Regression
11
Multi Class Classification Part 2 - k Nearest Neighbor
12
Support Vector Machines Part 1
13
Support Vector Machines - Part 2
14
Naive Bayes Part 1
15
Naive Bayes Part 2
16
Decision Trees and Random Forests

Learn how to Use SciKit Learn for Decision Trees and Random Forests

17
Natural Language Processing Part 1

Learn about Natural Language Processing!

18
Natural Language Processing Part 2

Learn about Natural Language Processing!

19
Natural Language Processing Part 3
Learn about Natural Language Processing!
20
Natural Language Processing Part 4
Learn about Natural Language Processing!
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Includes

21 hours on-demand video
3 articles
Certificate of Completion
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