Data Analysis in Python with Pandas

Getting an introduction to doing data analysis with the Python pandas library with hours of video and code.
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  • Lectures 34
  • Length 5 hours
  • Skill Level Intermediate Level
  • Languages English, captions
  • Includes Lifetime access
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    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 2/2015 English Closed captions available

Course Description

Ever wonder how you can best analyze data in python? Wondering how you can advance your career beyond doing basic analysis in excel? Want to take the skills you already have from the R language and learn how to do the same thing in python and pandas?

THEN THIS COURSE IS FOR YOU!

By taking the course, you will master the fundamental data analysis methods in python and pandas!

You’ll also get access to all the code for future reference, new updated videos, and future additions for FREE! You'll Learn the most popular Python Data Analysis Technologies!

By the end of this course:

- Understand the data analysis ecosystem in Python.

- Learn how to use the pandas data analysis library to analyze data sets

- Create how to create basic plots of data using MatPlotLib

- Analyze real datasets to better understand techniques for data analysis

At the end of this course you will have learned a lot of the tips and tricks that cut down my learning curve as a business analyst and as a Master’s Student at UC Berkeley doing data analysis. I designed this course for those that have an intermediate programming ability and are ready to take their data analysis skills to the next level.

You’ll understand cutting edge techniques used by data analysts, data scientists, and other data researches in Silicon Valley.

Complete with working files and code samples, over 5 hours with 40+ lectures you’ll learn all that you need to know to turn around and apply data analysis strategies to the data that you work with. You’ll be able to work along side the instructor as we work through different data sets and data analysis approaches using cutting edge data science tools!

What are the requirements?

  • Students need to have Python installed on their computer.
  • Students should be familiar with basic data analysis concepts.
  • Students should have experience writing, at a minimum, basic programs in python.

What am I going to get from this course?

  • Perform data analysis with python using the pandas library.
  • Understand some of the basic concepts of data analysis.
  • Have used n-dimensional arrays in NumPy as well as the pandas Series and DataFrames to analyze data.
  • Learned the basics of plotting with matplotlib

What is the target audience?

  • This course is best suited for people that need a deeper understanding of data analysis tools available today.
  • This course is not suited for those that want to learn how to program and have no prior programming experience.
  • This course is great for introductory to intermediate python programmers or those that come from a statistical software background like R or SPSS.
  • Analysts who want to better understand a technical approach to analyzing data.
  • Scientists who want to step away from more academic programming languages and use a general purpose language like python.
  • Programmers who are coming from a technical background but want to understand the pydata ecosystem a bit better.
  • Those that are interested in learning a bit more about data analysis.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to the Course
03:18

This video is a general video introduction to the course.

00:49

This video gives you basic instruction on how to install what you'll need for this course.

Article

These are the written directions for how to setup your environment.

Article

This is a link to all the data and files that are used in this course.

Section 2: IPython Notebooks and Raw Python Data Analysis
10:06

This video introduces you to the IPython Notebook.

07:07

This video introduces you to the concept of mapping and how it's done in raw python.

05:45

This video introduces you to the concept of filtering and how it's done in raw python.

06:40

This video introduces you to the concept of list comprehensions.

09:43

This video introduces you to the concept of lambda functions and how they work in python.

Section 3: The Basics of NumPy
06:09

This video introduces you to the basic usage and concept of NumPy.

10:20

This video introduces you to the basic usage and concept of boolean selection in NumPy.

12:59

This video introduces you to the basic usage of some helpful methods in NumPy.

07:08

This video introduces you to the basic usage and concept of vectorization in NumPy.

14:12

This video introduces you to the basic usage and concept of multi-dimensional arrays in NumPy.

12:42

This video introduces you to the basic usage and concept of querying, slicing, and combining arrays in NumPy.

Section 4: Pandas Basics
10:40

This video gives you an overall introduction to the basic usage and concepts of pandas.

Section 5: pandas Series
07:25

This video gives you an overall introduction to the basic usage and concepts of the pandas Series.

11:01

This video gives you an overall introduction to look ups, selections, and indexing in pandas Series.

06:56

This video gives you shows you how to perform advanced indexing in the pandas Series.

12:06

This video shows you how to handle NaN values, perform reindexing, and Filling Methods.

06:05

This video shows you how to perform Series multiplication, reindexing, and mapping.

Section 6: pandas DataFrame
10:04

This video gives you an overall introduction to the basic usage and concepts of the pandas DataFrame.

10:45

This video teaches you how to read files, plot, and understand some basic methods with the DataFrame.

11:53

In this video you'll learn more about plotting, performing joins, datetime indexing, and writing files.

07:14

In this video you'll add and reset columns and performing mapping with functions.

10:17

In this video you learn how to do more mapping, handle some NaN values, understand more plotting and how to perform correlations.

05:52

In this video you learn how to do more plotting, learn about rolling calculations, and more datetime indexing.

09:20

In this video you'll learn some analysis concepts, how to fill in NaN values, and get cumulative values.

08:18

In this video you'll learn about data maintenance, and how to add remove columns.

09:08

In this video you'll learn about basic grouping concepts and some concepts of aggregate functions.

Section 7: BONUS: Advanced pandas Topics
11:42

In this video you'll learn about pandas.io.Data, and some advanced indexing concepts.

12:43

In this video you'll learn more about reading csv data, html data, performing binning and understand categorical data.

07:56

In this video you'll learn about advanced grouping and aggregation functions.

12:49

In this video you'll learn about advanced grouping and apply and transform functions.

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Instructor Biography

Bill Chambers, Data Scientist at UC Berkeley

Bill Chambers is currently pursuing a Master’s in Information Management and Systems at the UC Berkeley School of Information. Before pursuing this degree, he focused on data architecture and systems scaling at his last employer. He re-architected the company’s entire internal systems operations including redeploying Salesforce internally, implementing Hubspot’s marketing automation software, and integrating Totango’s customer analytics platform. Bill was also responsible for providing operational metrics through statistical analysis using Python (specifically the pandas data analysis library). After UC Berkeley, Bill hopes to help other businesses improve the way their businesses work through data.

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