- Instructor: Eric Hulbert
- Students: 71
Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?
I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I’m not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.
This course is different than most for several reasons:
1. We start with problem solving instead of coding. I feel like starting to code before solving problems is misguided; many students are turned off by hours of work to try to write a couple of meaningless lines rather than solving real problems. The key value add data scientists make is solving problems, not writing something in a language a computer understands.
2. The examples are based on real client work. This is not like other classes that use Kaggle data sets for who survived the Titanic, or guessing what type of flower it is based on petal measurements. Those are interesting, but not useful for people wanting to sell more products, or optimize the performance of their teams. These examples are based on real client problems that companies spent big money to hire consultants (me) to solve.
3. Visual workflows. KNIME uses a visual workflow similar to what you’ll see in Alteryx or Azure Machine Learning Studio and I genuinely think it is the future of data science. It is a better way of visualizing the problem as your are exploring data, cleaning data, and ultimately modeling. It is also something that makes your process far easier to explain to non-data scientists making it easier to work with other parts of your business.
Summary: This course covers the full gamut of the machine learning workflow, from data and business understanding, through exploration, cleaning, modeling, and ultimately evaluation of the model. We then discuss the practical aspects of what you can change, and how you can change it, to drive impact in the business.