# Data Science Web nugget Roundup, Jan 29: Calculating Derivatives in PyTorch; Hacking Wordle

In this week's data science web nugget roundup, see articles from around the web on using PyTorch to calculate derivatives, using data science to find the best start words for Wordle, why math poses a problem for data science newbies, using breakpoints for debugging in Python, and software engineering skills that data scientists.

In our second roundup of data science nuggets from around the web, we will look at articles on using PyTorch to calculate derivatives, using data science to find the best start words for Wordle, why math poses a problem for data science newbies, using breakpoints for debugging in Python, and software engineering skills that data scientists.

Enjoy some of our favorite articles of the week that didn't appear on KDnuggets.

**Calculating Derivatives in PyTorch** by Muhammad Asad Iqbal Khan

Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for user-defined functions. [...] [C]oncept learned in this article will be used in later posts on deep learning for image processing and other computer vision problems.

**Hacking Wordle** by Kyle Pastor

I am not a word person (as you can tell) but I do deal a lot with math, programming and statistics in my day to day life, so naturally I wanted to see if I could math up Wordle and beat my friends. In this article I am going to show you how I choose my first and second word when tackling a new Wordle puzzle.

**Math is Still a Hurdle for Data Science Beginners** by Benjamin Obi Tayo

One of the top questions from beginners new to data science is:

Can I succeed in data science without a strong math background?[..] If you are interested in the field of data science, you may need to first build some background in foundational concepts in math before starting your data science journey. Data science is heavy in math and to better implement any data science algorithm, you need to understand the inner workings, otherwise you’d be using the algorithm as a blackbox.

**Setting Breakpoints and Exception Hooks in Python** by Stefania Cristina

There are different ways of debugging code in Python, one of which is to introduce breakpoints into the code at points where one would like to invoke a Python debugger. The statements that one would use to enter a debugging session at different call sites, depend on the version of the Python interpreter that one is working with, as we shall be seeing in this tutorial. In this tutorial, you will discover various ways of setting breakpoints in different versions of Python.

**3 Software Engineering Skills That Can Benefit Data Scientists** by Matt Przybyla

This article is intended for data scientists who entered their roles from a non-tech background, or who are simply interested in other ways that software engineering can be helpful. For me, I did not study engineering/technology in my undergraduate degree, so I was surprised how much software engineering I would have to perform in my first data science role, as well as how helpful it could be. Data science is all about algorithms, but in order to use them, you will need to be somewhat proficient in software engineering. So, below, let’s discuss 3 areas in software engineering that can help to better your data science processes.