Tips, Tricks, and Tools of the Trade: 7 Popular Posts You Should Read
Some of our most-read recent articles focus on making your data science journey easier and smoother.
August is supposed to be a slow, lazy month—the dog days of summer and all that—but you wouldn’t know it just by looking at the posts that resonated the most with TDS readers last month. Both aspiring and seasoned data scientists seemed to focus on practicality and efficiency, and wanted to level up their skills—so they found their way to articles that offered actionable ideas and insights. From Python packages and visualization tools to useful practice datasets, let’s dive in to some of our most-read recent posts.
“How to Grow from Non-Coder to Data Scientist in 6 Months”
August’s runaway viral success was this comprehensive-yet-concise guide from Sharan Kumar Ravindran, who distilled the advice he’s been giving early-career data scientists into a step-by-step blueprint for getting your foot in the door. Will becoming a data scientist still require a lot of hard work? Certainly. But Sharan’s guide might help you make wiser choices and save time along the way.
“Visualization and Interactive Dashboard in Python”
Word-of-mouth is often the best way to discover new restaurants and secret hiking trails, and the same holds true for visualization tools—in Sophia Yang’s post about HoloViz, she explains how this less-known Python ecosystem (containing seven libraries) has helped her streamline her workflows time and again.
“5 Online Data Science Courses You Can Finish in 1 Day”
Time is a precious commodity for many people entering data science—they might be juggling a full-time job, a bunch of side hustles, family duties, or just the general stress of life in 2021. Sara A. Metwalli rounded up several online courses precisely for those of you with little free time to spare, including several options you can finish within hours.
“Nine Tools I Wish I Mastered before My PhD in Machine Learning”
Another person’s hindsight can become your time-saving roadmap—if the former shares their insights with you, of course. Luckily, Aliaksei Mikhailiuk is generous enough to discuss several of the tools and methods that could’ve made his life easier as a machine learning PhD student—and that can still make your life easier, whether you’re in academia or in industry.
“How I Would Learn Python for Data Science If I Had to Start Over”
Like in Aliaksei’s post, retroactive wisdom is also the name of the game in Nicholas’s collection of tips for anyone who’s about to start learning Python. He covers several key areas, from understanding the concept of CRUD to getting a head start on project work.
“All the Datasets You Need to Practice Data Science Skills and Make a Great Portfolio”
If you occasionally get stuck not knowing where to find the right practice dataset to hone a particular skill or work through a thorny concept, Rashida Nasrin Sucky’s got you covered. Her collection is deep, wide-ranging, and covers a diverse set of topics and problems.
“7 Cool Python Packages Kagglers Are Using Without Telling You”
Who doesn’t like to discover a secret (or, fine, less commonly used) Python package that might just solve the tricky issue you’ve been grappling with in your work? Bex T. compiled no fewer than seven of those for you to explore, from Lazypredict to Rapids cuDF.
Did you read (or write) a recent post that might help other data scientists in their work? Share it in the comments!
