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How to Determine Causal Relationships in Observational Studies

Introduction In the previous blog post, we looked at controlled experiments and saw what techniques we can use to properly analyze them. Unfortunately though, we don’t always have the option to use a controlled experiment. There are times when data has already been collected and we still want to properly analyze it… …or other times when a controlled experiment is unfeasible or unethical (e.g. unregulated refusing of treatments for patients). Of course, there’s still a

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Max S

How to Evaluate A/B Tests (aka Controlled Experiments) for Causation and Correlation

In the previous blog post, we looked at the terms causation and correlation and developed a deeper understanding of what exactly each of those terms means and what the difference between causation and correlation is. In this post, we’re going to continue from where we left off last time and dive into how we can approach different types of datasets and go about analyzing them correctly. More specifically, in the next two blog posts, we’re

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Max S

Correlation vs Causation: What’s the Difference? (+ Examples!)

Once and for all – what are correlation and causation? How do you differentiate between correlation vs causation?

In this blog post, we discuss what correlations and causations are, some properties and types of correlations plus what noise is, and of course, you’ll find some examples to guide you along the way!

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Max S

What are Bar Graphs? & How to Use Them

Good ol’ bar graphs are some of the easiest and simplest forms of data visualizations, but let’s take it beyond what we learned in fourth-grade math and get into some of the more advanced questions when it comes to bar graphs, including when to use bar graphs, how to use them, and how to make them in Python.

Plus, let’s also talk through what the differences between bar graphs vs. histograms vs. box plots are and when to use which type of visualization.

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Max S

6 Steps to Storytelling Your Data

Being able to analyze data properly has always been important, even before we went into the digital and big data era.

Data analysis is a very important skillset for scientists, because models are built on the results that we see in experiments, and if we are able to properly analyze our experimental data, we are able to formulate models that better represent reality.

But data analysis is only half the story.

The other half, the one that is most often forgotten, is that you also need to be able to:

  1. Communicate your findings to others
  2. Convince others that what you’ve found is indeed correct

This is also known as….. Storytelling.

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Max Schallwig

Vectorization: The Secret to Shortening Your Python Code and Making it Run 150x Faster

Today, I want to talk about something that honestly blew my programming mind. I was taking a course on Neural Networks and Deep Learning – the one by Andrew Ng, former head of Baidu AI Group and Google Brain.

And I stumbled upon a method I’d never encountered before to shortening your code and making it run 150x times faster.

Yes, 150 times – sometimes even 200 times faster. (I have screenshots in this article.)

You: “Get outta here, Max! That’s crazy talk.”

Don’t worry, I thrive off of disbelief. Let’s pack that skepticism into our carry-on, and get directly into the article. 

P.S. There’s a free treat for all that make it to the end of this article (come on, it’s not that long).

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Max Schallwig
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