The Best Python Package for Data Visualization

An image of a laptop, playing a video that explains how to create a scatterplot in Seaborn

Data visualization is extremely important in data science. Although you often hear about the importance of data manipulation (i.e., “80% of data science is data manipulation”), data visualization is just as important. I explained why in a recent blog post about why you need to master data visualization. That blog post has a detailed explanation … Read more

7 Secrets To Help You Master Python Data Science

If you’ve been reading the Sharp Sight blog for a while, you’ll know that I’m a big fan of elite performers: Navy SEALs, grandmaster chess players, elite athletes, etcetera. I want to understand how elite performers operate across multiple disciplines, so that I can find what makes them special. The techniques. The training methods. The … Read more

The hard way is the best way

Here at Sharp Sight, we teach data science. And in particular, we show people how to master data science extremely fast. Exceptional results, as fast as possible. Our training methodology borrows heavily from elite performers and “ultra learners,” wherever we can find them. But our promise of “exceptional results, as fast as possible” sometimes confuses … Read more

How to Use the Numpy Linspace Function

A visual representation of the output of np.arange(start = 0, stop = 100, num = 5).

The NumPy linspace function (sometimes called np.linspace) is a tool in Python for creating numeric sequences. It’s somewhat similar to the NumPy arange function, in that it creates sequences of evenly spaced numbers structured as a NumPy array. There are some differences though. Moreover, some people find the linspace function to be a little tricky … Read more

You don’t need to know much math for data science

A pen on a piece of paper with math equations

Recently, a Sharp Sight blog reader emailed me and asked for advice about data science prerequisites. He was nervous about math. Someone had told him that in order to study data science, he needed to learn a long list of math topics first: Precalculus Calculus Multi variable calculus Trigonometry Linear algebra Differential equations Statistics Although … Read more