How to Optimize Your Data Science Learning Journey
When it comes to mastering complex topics in data science or machine learning, two things are generally true: there are no real shortcuts, and people’s learning rhythm can vary dramatically. Even with these constraints in place, though, there are ways to make the process more efficient and to achieve the goals we’ve set for ourselves within a reasonable timeframe.
The articles we highlight this week offer pragmatic approaches that data scientists can apply throughout their learning journey, regardless of their experience level or career stage. If you’re looking for study hacks and problem-solving tricks that could fit into a tweet, you’ll likely be disappointed (see above: no shortcuts!). Instead, the focus here is on developing better habits, building robust info-gathering workflows, and maximizing the knowledge you already possess.
- The lessons of a career switch. After working as a laser physicist for several years, Kirill Lepchenkov decided to become an industry data scientist, and his post about adapting an existing skill set to a new role is particularly useful for others considering an academia-to-industry transition. Its insights on skill transferability, however, are applicable for any data practitioner who needs to close a major knowledge gap in order to advance their career.
- Develop a solid system for retaining information. With long lists of algorithms, formulas, and Python libraries to navigate, data science learners can sometimes feel like they’re lost in a dark, impenetrable forest (random or not). Madison Hunter is here to help with a practical, six-step roadmap for organizing your study notes—and you can refine and customize it depending on the particular topic at hand.
- Find the learning path that works for you. Just as there are multiple ways to tackle specific areas within data science and machine learning, you’ll find countless opinionated takes on the right sequence to follow as you progress from one topic to the next. Cassie Kozyrkov’s new post presents a compelling and modular option, based on her deep archive of tutorials and explainers.
- There’s nothing wrong with some hand-holding. If you’re less of a choose-your-own-adventure learner and are more likely to benefit from a structured, cumulative approach, Angela Shi’s detailed machine learning curriculum is one you shouldn’t miss — it sorts algorithms into three categories, and provides clear advice on what elements to prioritize.
If this week’s focus on efficiency has already left you with some free time, a great way to spend it would be with a few of our other reading recommendations:
- Our latest Monthly Edition is out! Don’t miss this collection of fascinating articles on the data of urban spaces.
- Who doesn’t like a fun (and useful) project walkthrough? Jacob Marks, Ph.D.’s debut TDS article details the process of turning his company’s unwieldy documentation into an accessible and searchable database.
- After a year as the Director of Data Science at a non-tech company, CJ Sullivan shares a fresh batch of insights on hiring, budgeting, and communicating across teams.
- If you couldn’t attend PyCon DE in Berlin last month, Mary Newhauser’s writeup will help you stay up-to-date with some of the most interesting and thought-provoking talks.
- To round out your global perspective on the Python ecosystem, read Leah Berg and Ray McLendon takeaways from the U.S. edition of PyCon, which also took place in April.
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Until the next Variable,
TDS Editors
