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Introduction to Python

In this introductory training, participants will build a core foundation in the Python programming language and how to use it for various projects. This is a hands-on course where attendees will be in development environments used by practioners and actively programming. The course concludes with a hands-on project that cohesively ties together all the covered concepts.

This course is ideal for professionals who already understand Excel and want to understand how programming can help boost their productivity.

  • Course duration
    ~12 hours
  • Level
  • Prerequisites


Hands-On Programming Experience

An overview of foundational Python and computer programming concepts that will help you learn any new computer language.

Problem Solving Practice

Deep problem-solving practice that involves taking complex problems and breaking them down into smaller chunks.

Project Portfolio

Coding projects and snippets to showcase if you are job-hunting.

Support Beyond the Classroom

All Cognitir participants receive course notes, a certificate of completion, and instructions on how to add the course to their LinkedIn profiles.


This class is relevant for business, finance, and nonbusiness professionals who want to learn Python for data science & machine learning projects, web development, software development, and product management. The business professionals who would most benefit from taking our course include: finance, marketing/advertising, corporate strategy, corporate development, VC/PE, business intelligence, and operations professionals in corporations as well as management consultants and entrepreneurs. Nonbusiness professionals who would benefit from taking this training include professionals in: law, medicine, politics, public health, public policy, and sports analytics.

While not a requirement, this course is ideal for professionals who already understand Excel and want to understand how programming can help boost their productivity.

No prior Python programming experience is required.

Not sure if this is the right course for you? Contact us.


Chapter 1: Course Overview

Meet your instructor and learn what to expect from this course.

Chapter 2: Setup & Tips for Success

We will walk through the setup for this course and provide a few useful information to maximize learning outcomes for this course.

Chapter 3: Introduction to Python

Learn why Python is so relevant in today's world and how it can help you become more effective in your role. We will also make a few practical comments to get you started with programming more generally.

Chapter 4: Numbers, Variables, and Strings

This chapter provides a concise and practical overview of the most common data types in Python.

Chapter 5: Data Structure Fundamentals

This chapter is really a "programming 101" condensed to give you everything to get started. We will introduce the most important data structures such as numbers, strings and lists. You will start to see how powerful Python is and how to solve programming tasks in a practical manner.

Chapter 6: Comparisons and Conditionals

In this chapter, we will look at how to make our programs a bit more "intelligent". For example, we will learn how we write scripts that change behavior depending on what input data they are fed.

Chapter 7: Loops

We will learn about one of the key programming techniques that have the potential to increase your productivity by a tremendous amount. This chapter will look at how to implement "loops" which will allow us to write programs that perform repetitive tasks.

Chapter 8: Functions

Building on the previous chapters, this chapter will teach us how to structure our scripts and programs in more effective ways. We will learn how to create functions and use them effectively.

Chapter 9: Importing Python Modules

One of the great aspects of Python is its community. Hundreds of thousands of developers write libraries (modules) which we can use to speed up our own developments. In this chapter, we will learn how to make use of them.

Chapter 10: Debugging Python Code

What do we do if our code doesn't do what we want it to do? This chapter highlights a few practical debugging strategies that will not only help find bugs more easily but also make it easier to understand code written but others.

Chapter 11: Project 1

In this final project, we will work with quarterly actual earnings per share (EPS) figures and analyst estimates. You will have a chance to apply your new Python skills and work through a few practical problems.

Chapter 12: Project 2

In this second project, we will be implementing a simple game to reinforce the Python concepts that you have learned in this course. Not interested in games? Don't worry! They still serve as an excellent "playground" and all the concepts are 100% relevant in other practical settings that you will encounter on the job.

Chapter 13: Conclusion

We will wrap up this course by revisiting some of the fundamental concepts taught in this course and provide ideas for next steps.

Course Specific Questions

Do I need experience with Python programming to join this course?

No. That's why you would take this class!

Do I need experience with data science, web development, or software engineering to join this course?

No. The course is taught in a use-case agnostic manner. What you learn can be applied to any use-case that warrants Python programming.

How is this course different from your Introduction to Data Science courses?

The Python taught in our data science courses is purely to help you execute your data science and machine learning tasks. The focus of this course is not on any one use case. It is designed for anyone who wants to obtain a strong foundation in the Python programming language as a general-purpose tool.

Do I have the right laptop for this course?

You will need a PC (Windows Vista or newer), Mac (OS X 10.7 or newer), or Linux (Ubuntu, RedHat and others; CentOS 5+). Please contact us if you are planning to use a different setup for this course.

Do I need to install software for this course?

Yes, you will need to download and install free Python software on your laptop. In case you are planning on using a company laptop, please make sure that you have the necessary rights to install the tools. You will receive detailed information on the download and installation procedures during the course and Cognitir will assist you with any setup issues.


Yu Chen

Yu is currently a data scientist at Everyday Labs, an edtech firm, where he focuses on building data-driven products that help public school districts improve attendance and academic outcomes. He has built machine learning attrition models, financial data ETL pipelines, and natural language processing features for technology startups, financial services companies, global entertainment distributors, and large multinationals.

Yu is also an Adjunct Professor of Data Science and Operations at USC, where he teaches graduate courses on natural language processing and business analytics.

Yu received his MBA from UCLA Anderson and his Masters in Computer Science from Syracuse University.

Neal Kumar

Neal is one of Cognitir's cofounders. As part of his responsibilities at Cognitir, he teaches all Cognitir courses globally and regularly creates new courses for the company. He is one of the authors of this Introduction to Python course. He has successfully taught Python, data science, SQL, web development, product management, and other topics to thousands of professionals and students across the world via his classes. Neal has an MBA from London Business School and a BBA in Finance from the University of Notre Dame. He is also a CFA Charterholder.