Mastering Data Science with Pandas

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CARLOS QUIROS

6:26:21

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1. Introduction

1. Introduction.mp4

02:22

2. Windows Setup - Anaconda.mp4

08:36

3. Google Colaboratory.mp4

06:15

2. Building Blocks

1. Data Structures - Series.mp4

08:40

2. Data Structures - Dataframes.mp4

10:09

3. Indexing and Selecting.mp4

08:11

4. Range Slicing.mp4

11:17

5. Multi Indexing.mp4

08:08

6. Group by - Part 1 - MultiIndex for Columns.mp4

11:26

7. Group by - Part 2 - MultiIndex for Rows.mp4

09:14

3. Pandas Tools

1. Explorative Data Analysis (EDA).mp4

05:54

2. Utility Functions - Part 1.mp4

10:44

3. Utility Functions - Part 2.mp4

10:08

4. Custom Functions.mp4

12:17

5. Data Cleaning.mp4

14:53

6. Data Visualization - Part 1.mp4

14:50

7. Data Visualization - Part 2.mp4

16:35

8. Statistics.mp4

10:51

9. Categorical data and One Hot Encoding.mp4

07:52

10. Text Manipulation.mp4

09:52

11. Regular Expressions.mp4

07:43

12. Sorting and Filtering.mp4

12:53

13. Concat.mp4

10:10

14. Append.mp4

04:28

15. Merge.mp4

12:34

16. Pivot.mp4

13:20

17. Stack-Melt.mp4

10:10

18. Wide too long-Crosstab.mp4

11:48

19. Data IO - Part 1.mp4

13:28

20. Data IO - Part 2.mp4

14:42

21. Datetime functions - Part 1.mp4

12:31

22. Datetime functions - Part 2.mp4

10:44

23. Time series.mp4

17:14

24. Capstone Project - Part 1.mp4

19:56

25. Capstone Project - Part 2.mp4

15:42

26. End Lesson.mp4

00:44

Description

Learn Pandas since scratch to Ninja!

What You'll Learn?

Learn about data science tools
How Pandas library works with it's building blocks
Know a complete Panda's set of tools for data analysis, data manipulation and data visualization
Practical examples including time series and the analysis of finantial market
Learn advanced tools from Pandas such as Time Series, Text Manipulation, Regular expressions and more

Who is this for?

Beginners or intermediate users who are interested in develop a data science career

Data scientist students who want to consolidate previous knowledge or increase it

What You Need to Know?

Python, Matplotlib, Numpy

More details

Description
Pandas is the most demanding python library for Data Science, it comes with a plethora of tools. It provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

If you are planning to develop or improve your career in Data Science or Machine Learning, is a must to learn about Pandas.

This Course of Pandas offers a complete view of this powerful tool for implementing data analysis, data cleaning, data transformation, different data formats, text manipulation, regular expressions, data I/O, data statistics, data visualization, time series and more.

What you'll see in the course?
- Data Series and Dataframes,
- Indexing and Multi Indexing,
- Range slicing,
- Group data by condition,
- Concat, Append, Join,
- Pandas and Categorical Data,
- One Hot Encoding,
- Explorative data analysis,
- Utility functions and custom functions,
- Data cleaning,
- Data visualization,
- Statistics,
- Text Manipulation,
- Regular expressions,
- Data transformation,
- Pivot Tables,
- Stack and Melt,
- Wide_to_long,
- Crosstab,
- Data I/O,
- Datetime functions,
- Time series and more.

This course is a practical course with many examples, because the easiest way to learn is practicing!, then we'll integrate all the knowledge we have learned in a Capstone Project developing a preliminary analysis, cleaning, filtering, transforming and visualize data using the famous IMDB dataset.
Who this course is for:
Beginners or intermediate users who are interested in develop a data science career
Data scientist students who want to consolidate previous knowledge or increase it

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Industrial Engineer with more than 20 years in developing and managing business, with vast experience on process analysis and developing business information systems for data science. He has an Industrial Engineering degree from Pontificia Universidad Catolica del Peru (Lima-Peru) and Master in Business Administration (MBA) from ESAN Graduated School of Business (Lima-Peru).He is also an experience developer of machine learning and data science models in many fields of the industry and services like Marketing, Logistics, Finance, Manufacture, Quality Control, Computer Vision, NLP, Deep Learning apps and many others.He wants to share his experience teaching you on a simple and practical way, illustrating concepts based on graphics for better understanding.

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