We’re in that special stretch of August where for many of us, work projects might be moving at a more leisurely pace, while a hectic September is lurking around the corner. Isn’t that just the perfect time to give your data science-focused programming skills a boost?
Regardless of how much coding experience you have, you’ll find something new to try out in this week’s highlights. The nine articles we selected range from beginner-friendly guides and resources to advanced topics, so we encourage you to create your own bespoke reading list.
- Explore the powerful features that come baked into Python. Before branching out into the many libraries and packages that extend Python’s abilities, Katy Hagerty recommends that less-experienced programmers familiarize themselves with its essential built-in functions.
- No coding experience? Start here. For any aspiring data scientist who finds variables, data types, and other fundamental concepts daunting, Philip Wilkinson‘s beginner-oriented introduction is a great one-stop resource to get past the initial confusion.
- JSON and Python: a potent combination. Lynn Kwong‘s helpful primer shows how Python-first data professionals can make the most of the JSON (JavaScript Object Notation) format, which is commonly used to interchange data between different applications.
- Big Data? Your code can handle it. As datasets get bigger and bigger, handling them can become more complicated and time-consuming. If your CSV files are getting clunky, Leonie Monigatti proposes four alternative file formats to consider—especially if you still wish to work in the familiar comfort of Python’s Pandas library.
- Need a new learning approach? Go old school. If you feel uninspired looking up one StackOverflow answer after another, Benjamin Nweke suggests you turn to books. He shares five recommended options based on his own experience—they’re all Python-focused, and cover a wide range of topics that are relevant for data scientists’ daily workflows.
- When you’re in the mood for a machine learning project. If you’re ready to roll up your sleeves, Giovanni Valdata‘s deep dive presents an end-to-end sentiment-analysis project, and shows how to implement model selection and hyperparameters in Python.
- Get comfortable with the elements of a machine learning system. Kyle Gallatin‘s new post is a good place to start if you’d like to learn about the practical aspects of MLOps and the work that goes into productionizing models – and it covers the process using nothing but Python!
- What encapsulation is, and why you should care. "strictly speaking, everything defined in a Python class is public," explains Yong Cui, who goes on to explain how you can still create nonpublic (or private) methods in your code.
- How to streamline unit testing in Python. Kay Jan Wong returns to the always-relevant topic of unit testing, and walks us through a streamlined process for creating unit-testing workflows using the pytest package.
We hope you found this week’s selection helpful! If you did, we’d be grateful if you considered supporting the authors who wrote these articles by becoming a Medium member.
Until the next Variable,
TDS Editors
