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K2 Data Science

Passionate Mentors + Challenging Curriculum

Our Data Science Immersive is a career accelerator for driven individuals.

Through a comprehensive curriculum and project-based structure, students learn a wide array of modern data science techniques and technologies. The Data Science Immersive prepares graduates for data science and engineering roles at technology companies, large corporations and consulting firms.

Our Python-driven curriculum immerses you in the world of exploratory analysis, data mining, predictive modeling and infrastructure building. You bring the energy, curiosity and dedication — we'll provide everything else you need to succeed. We have rolling admissions, so you can join anytime.

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

Fidel Cuevas
Quantitative Developer
UBS

"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


Foundations Course

(100 hours, free)

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.

Introductory Course

(200 hours, $1000)

Once the Foundations is completed, fill out an application and we'll have an admissions call to learn more about you. If accepted, you can enter the Introductory Course. You'll join other students with more programming background, and get help from teaching assistants when needed. Topics include: intermediate Python programming, data encoding & formats, databases, web scraping, using APIs, data munging, regular expressions, summarizing data and data visualization.

Main Course

(400 hours, $5000)

This is the heart of the K2 program. You will meet your data science mentor and learn more advanced concepts. You will build data pipelines and products with guidance from your mentor as well as prepare for the interview process. Towards the end, you will build a final capstone project around a business problem you have identified. Topics include: statistical inference, regression models, time series analysis, supervised & unsupervised learning, feature engineering, hyperparameter tuning, ensembles, clustering, recommender systems, data engineering, chatbots and much more.

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

Details

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

Tasks

  • 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

Details

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.

Tasks

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

Gabriel Cypriano
Data Scientist
Creditas

Product Taxonomy Classification for Retailers

Details

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.

Tasks

  • 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


60+

Video Lessons

30

Mentor Sessions

6

Applied Projects

$6,000

Course Tuition

4 - 12

Month Commitment

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

Interested?