Explore our comprehensive offering

Watch every data science concept explained

Over 30 hours of video lessons, filled with concepts and live Python examples covering all major areas of data science and machine learning.

Apply every lesson with challenging exercises

Tons of exercises and projects that replicate the real-life data science process. You will be fully equipped for the demands of the job market.

Get feedback from TAs and other students

Ask questions day or night. Post your projects to get feedback from teaching assistants. Share resources and learn from your fellow students.

Bounce ideas and questions off an industry data scientist

1-on-1 mentorship with a professional data scientist. You will learn best practices, take your projects to the next level and be ready for technical interviews.

Download the entire curriculum for offline access

All the lecture videos, guided Jupyter notebooks, and assignments are available for offline use. You also have lifetime access to the curriculum, which is regularly updated.

Embark on a learning journey

Student examines politics, democracy and wealth.


(100 hours)

This is where you start from if you have minimal or no coding experience.

Topics include: CS fundamentals, basic Python programming, SciPy stack, statistics & probability.

Work on projects with tech companies

Get help from your data science mentor

Learn fast with an experienced data scientist there to guide you each step of the way.

These are example questions you might ask:

  • "I'm a bit fuzzy on concept X, can you share practical use cases for it?"

  • "Here's a problem I want to solve. How would you approach it?"

  • "Let's talk more about scaling machine learning workflows / model evaluation / etc."

  • "Here's a machine learning project I'm working on. Can you review it and give me pointers on what to change or improve?"

Anything that helps you become a better data scientist is fair game.

Read what alumni have to say about us

What you need to get accepted and hired

3+ years of work experience in an analytical or technical role

This could be as a data analyst, software engineer or applied scientist, among many other careers.

A quantitative academic degree

Most companies prefer candidates with strong academic coursework and research experience. A MS degree or PhD is usually required for most positions.

Experience with computer programming

You do not need professional experience, however, you should have spent time on your own learning and building programs.

Live in or be willing to move to a major tech hub

Approximately 80% of data science positions are located in the metropolitan areas of San Francisco and New York City. Another 15% are located in and around Boston, Chicago, Seattle, Washington DC, and Southern California (Los Angeles and San Diego). The remaining 5% are scattered throughout the country at large corporations, mid-sized companies, consulting firms and tech startups. If you are not in a large tech hub, you should be open to relocation in order to secure a data science role.

These are not strict criteria. We always evaluate each applicant individually and look out for motivated, non-traditional candidates. Check our student page to see the variety of backgrounds.

Here's how much it costs


  • Lifetime access to curriculum
  • TA support and Slack community
  • 30 1-on-1 mentor sessions

Get your questions answered here

How long is the course?

At a minimum, 700 hours of learning, completing exercises and building projects.

It can take anywhere from 4-12 months to finish, depending on your prior knowledge and weekly commitment.

How do I pay for the course?

You can pay the tuition upfront or pay over 6 months.

The payment plan costs 20% more than the upfront tuition.

What are the prerequisites?

Everyone must complete the coursework and exercises in the Foundations program.

In addition, employers are looking for applicants with the following characteristics:

  • 3+ years of experience in an analytical/technical role
  • A quantitative graduate degree
  • Strong grasp of programming
  • Located in a major tech hub

How does mentorship work?

Once you start the course, you are paired off with a data scientist who will serve as your mentor.

You meet with your mentor every week or every other week via a video call.

Most students discuss new concepts they learned and the challenges they are facing with open-ended projects.

Preview a section of our curriculum

Our curriculum is structured with a video lesson introducing a topic at a high level, a Jupyter notebook showing how to use that topic in Python and an assignment with a solution walkthrough.

Here is the set of videos and files for Times Series Analysis & Forecasting:

  1. Time Series Lecture
  2. Time Series Notebook and Assignment
  3. Time Series Solution Walkthrough

Meet the team behind the course

We all enjoy teaching and mentoring the next generation of data scientists.

We all have engagements outside K2 that help us keep the curriculum in tip-top shape.

Benjamin Bertincourt
Curriculum Contributor
Data Scientist @ Teachable

Nelson A. Colon
Curriculum Contributor
Applied Scientist @ Microsoft

Samuel Turner
Curriculum Contributor
Data Scientist @ Upwork

Michael Crown
Curriculum Contributor
Data Scientist @ Upwork

Ross Blanchard
Teaching Assistant
Software Engineer @ Helix