The world’s leading publication for data science, AI, and ML professionals.

6 Habits to Include in Your Daily Routine for a Long, Happy Career as a Data Scientist

All of these habits take less than 10 minutes out of your day

Data science Reddit is an interesting place right now where many data scientists are describing feelings of burnout and lack of fulfillment in their Careers.

Many are describing how they don’t feel the inspiration to advance in their careers or are feeling like they’ve plateaued and need a new challenge.

I think everyone feels this way at least once in their careers, which perhaps is why statistics suggest that people are averaging over 10 different jobs in their lifetime.

While it’s easy to look at disenchantment with work as an excuse to change careers, it’s vital to remember that you’ve endured a long tough slog to become a data scientist in the first place. Therefore, it’s also important to make sure you’re doing everything possible within your daily routine to ensure that you’re feeling fulfilled, energized, and inspired to keep working as a data scientist.

Daily routines can play a large part in the happiness you feel while working, which is why it’s important to establish some constructive habits that take little time but leave you feeling refreshed and ready to take on whatever is thrown at you on a given day.


1. Look for one menial task to automate

Some famous saying suggests that we should aim to improve ourselves by 1% each day. I like to think of it as we can become 1% more efficient each day if we simply look at the processes around us and try to think of how they can be done better.

One of the biggest complaints or realizations of data scientists, when they enter their careers, is that the majority of the work is menial. Data scraping, cleaning, and preparation probably take 80% of the time required for a project – 10% is required for project planning and the other 10% is needed for the actual analysis and visualization work. This means that 80% of the work you do, while incredibly vital for the success of the project, is repetitive, mind-numbing work that is relatively akin to scraping a cheese grater against your forehead for 8 hours a day.

Automated Data Cleaning with Python

Many data scientists have found respite by automating some of these kinds of tasks so that more time can be spent doing the "fun" stuff. One of the greatest realizations that occur when learning how to code is the sheer number of things you can do just by writing a few commands. This realization can lead you to get creative with all sorts of menial tasks that no one has time for.

By looking for one menial task a day to automate, you’ll find a slew of free time on your hands that can be used for other aspects of projects. They say that 20% of your work produces 80% of your results, so by getting to dedicate more time to that vital 20%, you may just produce some amazing results. From personal experience, the tasks you automate can be as simple as alarms that remind you to blink every once in a while or that automatically sort your emails, not to mention data cleaning and processing. The important thing is that it takes care of work you would otherwise have to do manually.

Besides, automating tasks is a great mental break and gymnastic rolled into one fun activity that not only gives you a chance to stretch your legs as a programmer but will also pay dividends very soon.

2. Work for 10 minutes a day on a new data-science-related skill

How Microlearning Can Help You Improve Your Data Science Skills in Less Than 10 Minutes Per Day

Microlearning is a verifiable way to pick up new Data Science skills in less than 10 minutes per day.

Developing this habit is a great way to keep you interested in advancing your skills as a data scientist by picking up new technologies or ways of doing things. Medium, Reddit, Substack, and various podcasts (see below) are great sources of information about new advances in data science that may inspire you to try learning something new.

The key for adult learners is to keep the learning short and pointed toward a specific, tangible goal. This means keeping the learning to short 10-minute blocks with objectives that are easily achievable within that time. Not only does this keep you motivated to keep moving forward in your studies because of the short time they take to complete but they also ensure that you’re advancing your skills after a study session. Furthermore, it doesn’t seem like a hardship to complete a habit that takes less time than you need for a coffee break.

In my experience, taking 10 minutes a day to work on a skill doesn’t provide huge gains immediately, but compounds slowly over time to produce something you can be proud of at the end of a year. Some of the uses I’ve seen for 10-minute data science studies include learning R, knocking the rust off of multi-variable calculus skills, gaining a deeper understanding of Excel’s various functions, and increasing business acumen by reading industry newsletters.

3. Make progress on a passion project that is unrelated to your daily work

The most gung-ho data scientists I’ve ever met are often those who enjoy working on their own data science projects outside of work. This true passion of theirs for data science never seems to wane on account of them working on personal projects that are completely unrelated to their day-to-day work.

The 7 Data Science Projects I Plan on Completing in 2021

I’m not going to waste your time listing all of the possible projects you could work on as many before me (myself included) have done this already. However, what I do want to do, is provide you with a bunch of fun places where you can retrieve free data to work on:

NASA | Open Data | NASA Open Data Portal

World Bank Open Data

Google Public Data Explorer

Products

These passion projects should be something you can pick up every day for ten minutes to add a feature or attempt a new kind of analysis or visualization. Nothing too strenuous or taxing, but just enough to encourage you to try new skills. While your daily work as a data scientist may be monotonous, this project doesn’t have to be and can be where you truly stretch your legs as a data scientist.

4. Seek out one company process that can be done more efficiently

Many data scientists lately have been saying that they feel as though they’ve maximized their impact on the company they’re currently working for. I remember reading one post from a data scientist describing how they had optimized every process they could, revealed every possible insight into how the company could work more effectively, and implemented as many new processes as they could think of.

I’m always optimistic that there are always more things that a company can do better. Even if you started small, with say, a more efficient way of retrieving customer data, you will have improved the effectiveness of your company by a small percentage that will add up to a much larger percentage the more customer data you work with. Alternatively, you could get more creative and design a plugin that formats everyone’s code to company standards.

This daily habit not only proves your continued value with the company but also gives you some extra mental exercise during the day that reinforces your inspiration to advance and improve your skills and the processes of the company you work for.

5. Listen to a data science podcast or audiobook that inspires you

It’s fair that you may not want to surround yourself 24/7 with the stuff that you do for work. However, there are some great data science podcasts and audiobooks you can listen to that aren’t as mentally taxing as trying to force a weak data set to show you answers.

The podcasts suggested below tackle everything from ethical AI to breaking glass ceilings to professional development as a data scientist. I prefer listening to stories about non-technical aspects of data science that dig into the nitty gritty of ethical issues, interviews with data scientists I look up to, and how to become a better data scientist because they offer a slight break from the rigors of Programming and mathematics.

The Banana Data Podcast

WiDS Podcast

The Artists of Data Science

Data science audiobooks are a great way to get in some extra learning when few of us have time to read anymore. I like to listen to audiobooks that tackle more academic or rigorous topics because listening to something explained to you often yields a clearer understanding of the topic than if you were to read it for yourself. However, on more than one occasion, I’ve found myself scrambling for a pen to write down a pertinent idea, so make sure you’re prepared with pen and paper to ensure you don’t miss any ideas that just click with you.

Weapons of Math Destruction

Big Data: How Data Analytics Is Transforming the World

Naked Statistics

Algorithms of Oppression

6. Share your experience and knowledge with others to pay it forward

I got my first job in tech thanks to a relationship I maintained with a former school colleague. I paid this kindness forward by contacting another school colleague about a position that was perfect for them at the company I was now working for.

Helping talented friends and colleagues around you get jobs is just one of the ways you can share your experience and knowledge and pay your success forward. This can be as elaborate as helping someone else get a job to as simple as sharing a tip with a co-worker who asked you for help at the water cooler.

For example, I share my experience and knowledge with the Towards Data Science community by writing articles on how to get jobs in data science, how to become better data scientists, and how to learn data science more effectively. I’m also considering turning these teachings into a book that can be shared and accessed by a wider audience.

Sharing what you know with others is a surprisingly inspirational and fulfilling daily habit because it not only shows you how far you’ve come as a data scientist but also helps others around you to attain the same success. Data science is one of the hardest fields to break into, which is why helping others with learning Python, sharing tricks on how to automate data cleaning, or helping others prepare for presentations is such a vital thing to get in the habit of doing.

Some other ways to help others and share your knowledge include writing blog posts, starting a data science podcast or Twitter account, responding to questions on r/datascience or Quora, or starting a Youtube channel as many data scientists have already done.


Subscribe to get my stories sent directly to your inbox: Story Subscription

Please become a member to get unlimited access to Medium using my referral link (I will receive a small commission at no extra cost to you): Medium Membership

Support my writing by donating to fund the creation of more stories like this one: Donate


Related Articles