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Python for Analytics

Python for Analytics

This course will teach you the basic Python skills and data structures – how to load data from different sources and aggregate it, and how to analyze and visualize it to create high-quality products


In this online course you’ll learn everything you need to get started using Python for data analysis.  Python is a general-purpose programming language that’s powerful, easy to learn and fast to code. It is rapidly becoming the language of choice for scientists and researchers of all stripes. Python code can be written like a traditional program, to execute an entire series of instructions at once; it can also be executed line by line or block by block, making it perfect for working with data interactively.

Following completion of this course, you will also be positioned to move on to the Predictive Analytics series using Python.

  • Introductory
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Upon completion you will be able to read and write data, group, aggregate, merge and join data frames, create visualizations and more. By the end of this course you will be well positioned to move on to learning predictive analytics using Python.

  • Construct conditional statements and loops
  • Work with strings, lists, dictionaries, and variables
  • Read and write data
  • Use Pandas for data analysis
  • Group, aggregage, merge and join
  • Handle time series and data frames
  • Use matplotlib for visualization
  • Create format, and output figures

Who Should Take This Course

Data scientists, statisticians, software engineers who need to use Python for data analytics, including web scraping, pulling data, data cleaning, data prep and data analysis.

Our Instructors

Course Syllabus

Week 1

Getting Started With Python

  • Using the Jupyter notebook
  • Python basics: variables, conditionals, loops
  • Data structures: lists and dictionaries

Week 2

Data Handling and Strings

  • Reading data into memory
  • Working with strings
  • Catching exceptions to deal with bad data
  • Writing the data back out again

Week 3

Python and Pandas

  • Using Pandas, the Python data analysis library
  • Series and Data Frames
  • Grouping, aggregating and applying
  • Merging and joining

Week 4


  • Visualization with matplotlib
  • Figures and subplots
  • Labeling and arranging figures
  • Outputting graphics


You should have familiarity with programming, even if it is not Python.  If you are a newcomer to programming, you should take Python Programming Introduction before taking this course.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Register For This Course

Python for Analytics

Additional Information

Time Requirements

About 15 hours per week, at times of your choosing.


The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.

This course also has example software codes, supplemental readings available online, and an end of course data analysis project.

Course Text

No text is required; all materials will be provided online.

If you want a reference, Python for Data Analysis is recommended.


The required software is Python Programming Language.

Register For This Course

Python for Analytics