Launch Your Career in Data Science helps busy people streamline the path to becoming a data scientist.

You'll discover how to shorten the learning curve, future-proof your career, and land a high-paying job in data science.

Download the DS Career Guide

Free: Data Science Career Guide

How to Learn Data Science & Machine Learning, Land a High-Paying Job, and Future-Proof Your Career

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Learn Data Science in Half the Time helps you shorten the path to data science. We help you skip the non-essential and laser-focus on the skills and tools that will move the needle in your career.

We help time-constrained professionals

You might already see the rising role of data in today's economy. You might already be determined to learn DS and future-proof your career...

Yet it feels like there's a looming mountain of topics you're "supposed" to learn for data science. And with only 24 hours in a day, where do you even begin?

Cloud of Uncertainty
Path of Certainty

Simplify the path to data science helps you connect the dots between DS and real-world business value. You'll learn only the most relevant, commercially-viable tools and best practices.

We skip the non-essential theory and math, and instead take you through the most direct path to applying DS and ML at a professional level.

The Top-Down Strategy flips the traditional approach to learning DS on its head. Instead of spending months on math and theory, we focus on the shortest path to getting your foot in the door.

  • Step 1: Start by understanding the big picture

    First, learn how all of the moving pieces fit together… Knowing the inputs, outputs, and core steps is the essential to driving real-world value with DS and ML.

  • Step 2: See the DS workflow from start to finish

    Next, see the workflow from start to finish. That includes exploratory analysis, data cleaning, feature engineering, and training models with machine learning.

  • Step 3: Learn today's most in-demand tools

    Don’t start from scratch… Learn the real tools that companies are paying top dollar for. Remember, the industry values effectiveness and efficiency above all else.

  • Step 4: Hone your skills with end-to-end projects

    The best way to master these skills is through realistic projects. Projects allow you to learn “in context” and connect the dots between concept and application.

  • Step 5: Build a portfolio that proves your ability

    A portfolio is the #1 way to prove your skills and win the trust of employers… And as you complete those projects, you’ll be building your portfolio too.

  • Step 6: Launch your high-growth career in DS

    There’s always more to learn when it comes to data science… But by following these streamlined steps, you’ll be more than qualified to get your foot in the door.

  • Raphael Paim
    I was looking for the link between the concepts and the real steps of a data science job. The way you approach the models/techniques is a far better way than long and exhaustive statistical explanations. I feel motivated to keep learning more detailed and complex models. This is the "easy and gentle" way we can learn and apply data science in our day to day problems.
    Raphael Paim Financial Analyst
  • Jessica Stahl
    ML is generally presented as though advanced math is required. Yet, I knew [ML professionals] who did not have an advanced math background. This course is helpful for those with IT/analyst background who are not looking to invest several years in math courses, but would like to engage in ML. The really valuable skill in the near future will actually be understanding the process and the results.
    Jessica Stahl University Professor
  • Ben Wilson
    This is at least my 5th pass at learning this material (Thinkful bootcamp, Andrew Ng Course, Data Camp, and a multitude of books). Walking through the full process was so valuable. A lot of other programs just focus on the algorithm implementation. It's easy when you are starting with the ABT. Now I'm confident to take on predictive analytics projects at the office that no one has had the expertise to tackle before.
    Ben Wilson BI Analyst