Learning data science quickly and effectively

This blog post has been updated and was originally authored by Metis.


There’s no shortage of debate in the data science community about where to best learn data science. However, there’s little discussion of a topic arguably more important – how to learn data science.

Taking the right approach to learning matters. For all the differences between programs and platforms, the reality is you get out of any educational experience what you put into it. Approaching your data science learning journey with the right mindset and game plan can help you get the most out of your studies, ultimately making you a more effective data scientist in the long run.



So, how should you learn data science? Here are three crucial actions you need to take, no matter what platform, bootcamp, university or educational program you’re learning from.


Three keys for learning data science


1. Work on personal projects

Learning all the technical skills associated with data science is a lengthy process. It’s one you’re unlikely to reach unless you find an effective way to keep yourself motivated and integrate your interests into your learning.

The easiest way to do this is to find some free time for personal data science projects throughout your studies. For example, if you’re passionate about climate change, find a unique angle for analysis and dive into some climate data in your free time. If you love football, find a statistics site, learn to scrape it, and start working on an analysis of your favourite players.

The key is to pick topics for these projects that genuinely interest you. Find a question you’re curious about and then set out to use data to answer it. You’ll feel motivated to keep working, even when the actual work you’re doing isn’t particularly thrilling.

Why’s this important? Even if you love working with data, there are aspects of learning data science likely to frustrate or bore you. For example, if you dislike data cleaning, a critical but not particularly fun data science skill, it can be challenging to motivate yourself. If you’re working on a personal project you care about, try answering a question that really interests you. This makes it easier to motivate yourself on days when you don’t feel like practising, just for the sake of practise.

Working on personal projects in your free time also has the pleasant side effect of preparing you for the process of job applications. If you don’t have any work experience, your applications for entry-level data jobs are going to mostly rely on the strength of the projects you’ve completed. If you’ve been working on personal projects throughout your studies, you should reach the beginning of the job application process with a portfolio of thoughtful and unique projects. This may save you some time having to prepare new projects. It will also prevent you from applying for jobs with the same five ‘homework’ projects everyone else in your class has on their GitHub, too.

2. Apply what you’ve learned frequently

Repeated studies have shown students who apply what they’re learning fail at significantly lower rates than students who don’t. It’s critically important wherever you’re learning data science, you’re also taking the time to apply it practically as you learn.

This can be a tipping point for some data science students, particularly if your course of study is primarily lecture-based. It’s easy to watch a video lecture and feel like you’ve understood the material, especially if the presenter is a good teacher. However, understanding something on an intellectual level, and being able to apply it in the real world, isn’t the same thing. Data scientists need to be able to do both.

Working on personal projects can positively support you to apply what you’ve learned. If your learning platform doesn’t integrate more frequent, shorter, hands-on sessions, then you’ll want to ensure you’re getting critical practise yourself. If you don’t practise applying concepts quickly after learning them, you may find by the time you get to the relevant section of your personal project, you’ve already forgotten what you learned.

For example, if you’ve just watched a video lecture on ‘for loops in Python’, you should follow up by opening a Jupyter Notebook of your own, importing some data, and writing some for loops. Ideally, you should practise applying a concept directly after learning it then several more times throughout the week to ensure you’ve cemented how to apply it into your long-term memory.


3. Stay engaged with peers and mentors

It’s important to make interaction and communication part of your data science study. It’s easy to get technical tunnel vision and focus on tweaking your algorithms until they’re as accurate as possible. In real-world data science work, building a great model is only half the battle. Your highly-accurate model will only be useful if you’re a skilled communicator who can explain what it means and convince your colleagues to act on your results. Working with peers and mentors as you study data science can help you learn how to talk about these topics effectively and convincingly.

Finding a mentor has other benefits. A good mentor will help keep you on the right track and identify areas for improvement you might not see on your own. They can also help you make important connections and assist you in your job search, once you reach this stage in your studies.

Working with peers is important too. Teaching a concept to a peer is one of the most effective ways to test whether you truly understand something. Plus, working together with other students on data science projects will give you experience cooperating as part of a data science team. This can help you practise workflow-related data science skills such as using Git and GitHub effectively for collaboration.

How you engage with peers and mentors will probably depend on how you’re studying. If you’re enrolled in a bootcamp or university program, this interaction may have been arranged for you. If you’re working on an online platform or completing self-study, you may have to be more proactive in seeking it out. Luckily, there are many online data science communities. You should be able to find data science and/or programming meetups in most cities. You can even start one yourself.

Don’t forget about social media. There are data science groups and communities on most major platforms. If you get involved, you’re likely to make some useful connections as you’re interacting with, and learning, from the other people in the group.



How to study more effectively


While those big-picture solutions can help you to be successful in your data science studies, there are also some smaller-scale actions you can take to ensure you’re learning at peak efficiency:


Make clear, explicit plans (with contingency plans)

Studies have shown people are more likely to follow through on their plans if they’re clear and specific. “I’m going to learn data science” is a vague plan, whereas “I’m going to study data science for five hours per week” is slightly better. “I’m going to study data science at my desk from 8pm to 11pm every Tuesday and Thursday each week and make up any session I’ve missed on Saturday morning from 8am to 11am” is better still.

Having a contingency plan as a back-up is particularly important. In the long term, it’s likely you may miss study sessions occasionally due to everyday life. If you don’t have a back-up plan, you’re less likely to catch up on the work.


Take notes

Regardless of how you’re learning, note-taking is a worthwhile endeavour that can help you retain what you’ve learned. There’s some evidence writing your notes longhand is better than typing them. However, you’ll benefit from note-taking even on a computer, so long as you:


  • Don’t transcribe verbatim or copy-paste things. A big part of what makes note-taking effective is you’re writing out what you’ve learned in your own words. If you copy-paste, you lose this cognitive benefit.
  • Review your notes regularly over time to keep them fresh in your mind.
  • Test yourself against them. For example, cover up the ‘for loops’ section of your notes and see if you can remember the syntax. Then check your notes to be sure you remembered correctly.


Leave your phone elsewhere

It doesn’t matter how disciplined you are, studies have demonstrated a ‘phone proximity effect’ can impact your cognitive performance when your phone’s nearby, even when it’s out of sight and switched off.

The lesson here? When you’re going into a study session, leave your phone far away when practical. Students who left their phones in a different room scored better on memory capacity and fluid intelligence tests, compared to students who left their phones on their desks, in their pockets, or in handbags as they worked.


Beat procrastination

Some people procrastinate more than others, but even serial procrastinators can improve their study habits by making a few key tweaks:

  • Breaking down big tasks into smaller ones
  • Giving yourself rewards for completing tasks
  • Setting clear deadlines, or having them set for you
  • Doing your best to keep things light and fun
  • Forgive yourself for mistakes


While we all have unique learning styles and preferences, these are some of the key techniques for learning data science and studying more effectively. Have any of these worked for you throughout your data science journey? Perhaps you found other techniques beneficial. Share your learning approach to help inspire others!