K2 Data Science

A robust video-based curriculum complemented with applied assignments and guided projects tackling real business problems.


Direct 1-on-1 mentorship with senior data science professionals to learn industry best practices and add depth to your work.


Graduate with a portfolio that shows your experience in exploratory data analysis, applied statistics, machine learning and data engineering.

Course Experience

Video Lessons

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

Expert-Based Learning

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.

Applied Challenges

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.

Downloadable Curriculum

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

Community Discussion

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

Daniel Cardella
Portfolio Manager
KLR Group

"I was in the first cohort and have witnessed the development of the Curriculum since I first began and believe it to be continuing to improve from a very good to an excellent base. The highlight of the program, undoubtedly, was the mentorship experience. Meeting with my mentor twice a week for numerous months while I coded my projects has proven to be invaluable in shortening the learning cycles. The program surpassed my already high expectations."

"They have produced an excellent curriculum which teaches data science effectively — lectures and exercises get you familiar with the material, and then project-based work helps you apply it. The mentors and TAs were responsive and helpful, and proactive in offering help and advice. By the end of the program, you’ll be familiar with pretty much every tool and technique used by data scientists in their day-to-day work."

William Ryan
Research Associate
U.C. Berkeley

Fideal Cuevas
Quantitative Developer

"You'll work with Python and learn to use machine learning to predict a variety of outcomes in computer vision, forecasting, clustering, and classification. For some perspective: I took approximately 4 months to finish the curriculum and found a new role with a substantial increase in compensation after just 22 days of searching. This is a great option for any student or professional with the motivation to do great work."

Course Curriculum


Start your journey in this prerequisite beginner's course by going over the fundamentals of data science and exposing you to the breadth of skills and tools in the industry professional's arsenal. In these first units, you will be introduced to the scientific programming environment, as well as the key concepts of both programming and statistical analysis.

Getting Started

Local Setup and Development Environment

Python Programming & Computer Science

Types, Flow Control, Data Structures, Functions, OOP and Time Complexity

SciPy Stack

NumPy, pandas and matplotlib


Statistics, Probability, Calculus and Linear Algebra

40 - 100


Data Analysis

Students will tackle a wide variety of topics under the umbrella of exploratory data analysis. Getting, cleaning, analyzing and visualizing raw data is the main job responsibility of industry data scientists. Here you will learn how to discover patterns and trends that influence your future modeling decisions.

Getting and Cleaning Data

Static Files, SQL, Web Scraping, APIs and Messy Data

Statistical Inference

Event Space, Probability, Distributions and Hypothesis Testing

Summarizing and Visualizing Data

Descriptive Statistics, Univariate and Multivariate Exploratory Data Analysis

100 - 160


Machine Learning

Students will learn how to explore new data sets, implement a comprehensive set of machine learning algorithms from scratch, and master all the components of a predictive model, such as data preprocessing, feature engineering, model selection, performance metrics and hyperparameter optimization.

Predictive Modeling

Regression, Classification, Data Preprocessing, Model Evaluation and Ensembles

Data Mining

Dimensionality Reduction, Clustering, Association Rules, Anomaly Detection, Network Analysis and Recommender Systems

Specialty Topics

Data Engineering, Natural Language Processing, and Web Applications

200 - 260


Applied Projects

After mastering the curriculum, the Project Phase is all about applying what you've learned on both mock and live industry projects. You'll be given the opportunity to work with a wide variety of outside companies and create real-world deliverables.

Independent Project

Project based on an interest or identified business problem


Project with current employer or our industry partners

250 - 400


Career Prep

The support and mentorship doesn't end at graduation. We're personally committed to working with every one of our alumni for as long as they need to continue evaluating work, providing mentorship and guidance, and facilitating their job search.


Resume, Cover Letter, LinkedIn, GitHub Portfolio and Networking Advice

Interview Preparation

Behavioral Questions and Mock Technical Challenges

50 +


What We Look For

2 - 5 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.

At the minimum a B.S., however, a M.S. is required for the majority of jobs. Of course, a Ph.D. is the most sought-after qualification.

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, 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.

Featured Projects

Automate Updates and Enrichment of Client CRM


Discrete Data Solutions provides software and data solutions to clients facing technology hurdles. In this instance, their client was a consulting firm with a large CRM, filled with thousands of outdated contacts. The client asked for a method to automate the updating of the contact information and addition of data from various social media sources.

In order to address this problem, Michael developed the following:

  • Several automated web scraping tools
  • Data processing pipeline that integrates data from multiple sources
  • A series of data matching ML models that leverage word vectorization and Levenshtein or Mallow distance as well as several other methods
  • Email format simulator based on the supervised learning models and trained with email addresses from others in the company and classified by when they joined the company


  • Web Scraping
  • Data Cleaning and Processing
  • Record Linkage
  • Social Media Mining
  • Email Format Prediction

Michael Nemke
Data Science Consultant
Discrete Data Solutions

Examining Treatments and Healing Outcomes


Parable reduces the cost & inconvenience of wound care through a platform that allows healthcare providers to better measure, monitor, and manage their wound patients. The platform facilitates care coordination and offers a mechanism to catch wound complications early. Specifically, Parable checks for clinical indicators of expected healing/development, captures a visual time lapse of the progression, and reminds patients (in ambulatory settings) about proper care instructions to ensure adherence.

For each case, a patient might check-in multiple times. We used data wrangling to capture the improvement of the wound between check-ins and later modeled that with the XGBoost implementation of gradient boosted trees to try to classify potential treatments either as an improvement or worsening of the wound, as well as to try to quantify what the improvement would be in an attempt to be able to recommend the best possible treatment for each case.


  • Data Wrangling
  • Exploratory Data Analysis
  • Problem Formulation
  • Time Series Analysis
  • Classification

Gabriel Cypriano
Data Scientist

Product Taxonomy Classification for Retailers


Havenly provides remote interior design solutions for users through their online platform. The design process includes product selection from the catalogs of partner retailers. Often the product classification provided is not consistent. This is due to many retailers maintaining their own hierarchy for product classification.

To address this problem, a process was developed for taxonomy classification using product descriptions provided by partner retailers. Natural language processing tools were used in conjunction with a support vector machine classifier to achieve an accuracy score of 0.99 on the test data set.


  • Natural Language Processing
  • Taxonomy Classification

Christopher McLaughlin
BI Analyst
Pinnacle Agriculture

Industry Mentorship

If you need to learn data science fast, there’s no better way to do it than with personal advice and one-on-one project feedback.

Industry mentorship is for students to level up their skills as quickly and efficiently as possible. While all students will have access to feedback in the Slack channel and code reviews from a teaching assistant, industry mentorship will give you 30 one-on-one sessions with an experience data scientist, where you’ll screen share projects you’re working on and focus in on the topics that will personally help you the most. If you have never tried mentor-based learning, it adds an entirely new dimension to the experience.

What do you cover in these sessions? Let me give you some examples, based on past sessions:

  • "Here's a machine learning project I'm working on. Can you review it and give me pointers on what to change or improve?"
  • "Here is a data science problem I want to tackle. How you would approach it?"
  • "I'm a bit fuzzy on concept X, can you share practical use cases for it?"
  • "Let's talk more about scaling machine learning workflows / model evaluation / etc."

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

The 1-on-1 mentor sessions have:

  • No deadline: Now, next month, or whenever is best for you.
  • No format restrictions: You can structure the sessions however you want.
  • No off-limits topics: Ask you mentor anything that will help you improve your data science skills.

Do you wish you worked with an experienced data scientist who you could pepper with questions and get some real, in-depth feedback from? Think of this as your chance.

Program Specifics


Video Lessons


Mentor Sessions


Applied Projects


Course Tuition

4 - 12

Month Commitment

Admissions Process


Fill out the application form and let us know why you want to learn data science.


Learn more about the program and discuss your background further via a phone interview.


Complete the Foundations. This is a free, structured program covering all the basics.

Course Structure

  • Pay $1,000 deposit.
  • Tackle Exploratory Analysis unit.
  • Get help from Teaching Assistants.
  • Interact with students in the Slack community.
  • Pay $5,000 balance.
  • Tackle Machine Learning & Capstone Project.
  • Get help from Teaching Assistants.
  • Interact with students in the Slack community.
  • Mentorship from a Data Scientist.
  • Access to Job Preparation resources.

About Our Company

We aim to educate working professionals through high quality instruction and individualized mentorship.

Our goal is to eliminate the need to leave your job and attend costly graduate programs. We want to provide an affordable and accessible education for everyone.

Data science is more than just a highly sought-after workplace skill. Data science is a rewarding endeavor, an intellectual challenge, and a means of becoming a more rational, more productive thinker. It is a skill that everyone in the digital age should possess, regardless of profession or background.

Benjamin Bertincourt
Curriculum Contributor

Nelson A. Colon
Curriculum Contributor

Samuel Turner
Curriculum Contributor

Michael Crown
Curriculum Contributor

Ross Blanchard
Teaching Assistant

Ty Shaikh
Program Manager