Many articles talk about how professionals can make their workdays extra productive. However, for people like data scientists, whose jobs are extremely demanding, some tips are more valuable than others. For instance, it is important that you analyse how you spend your time. In the same breath, it would be in your best interest to organise your time into blocks, as these can help you focus on tasks – one at a time and without any interruption – and automate any process that you repeat. Of course, attaining a certain level of productivity requires more than just abiding by the aforementioned tips. That being the case, here are some other productivity tips you can follow and take inspiration from.
1. Improve Your Iteration Time
As Dr. Phil Winder suggests in his book Reinforcement Learning, your efficiency is predominantly controlled by how fast you can iterate. Stress levels, the number of bugs, and the quality of the code are all related to the length of the feedback cycle. Luckily, there are many ways you can improve your iteration time. One way is by treating each iteration as an experiment. Like in any other experiment, in order to gain valuable insights in a systematic manner, you have to make full use of the scientific method: observe, formulate a question, experiment, and analyse.
While you are at it, monitor the duration of your iterations and try to determine the moments when you are “sat waiting”. Once you’ve picked out those moments, examine them. Try to understand the things that are eating up your time and to eliminate them. If that’s not possible, you can opt to break them down into shorter steps. For example, you could instead train smaller models or turn big ones into hierarchical problems.
2. Data Wrangling
As a data scientist, about two-thirds of your time will most likely be spent on working on and improving data. Unfortunately, after taking every other process into consideration, a lot of this time will most likely be spent blocked waiting on others. One of the best ways to make the most of your time and significantly reduce this blocked waiting period is by getting organised.
As much as possible, try to anticipate the kinds of data you will need at a given time and get your hands on them beforehand. You could use data dependency graphs to expose application dependencies.
Aside from getting organised, you can also develop your own tooling that can aid in the automation of the analysis phase and a domain-specific toolkit that will enable you to generate everything you would need for future analysis.
3. End-To-End Projects: Improve Your Software Engineering and MLOps Skills
Dr. Phil Winder strongly believes that engineers should proactively gain lots of experience in complementary fields and try to become something of a “jack-of-all-trades”. After all, these efforts can help you deliver value at a faster rate without having to depend on anyone else. Furthermore, it’s important that you work on your ability to write production-ready code and deploy your code into an environment that gives users early access to your work. Doing so will help dramatically reduce the probability of project failure.
If you are looking for a training program that provides software-focused practical workshops, is free and is accompanied by a selection of online videos, the Free Data Science Training we offer here at Winder.AI is a great start. Operational machine learning (MLOps) is an emerging development and deployment paradigm that will become huge in the future; invest in MLOps now using our expertise. TechCrunch detailed how learning MLOps can help you leverage the benefits of machine learning in the real world and enable you to create a reactive development process that can help you arrive at a handful of measurable values, boosting your effectiveness and efficiency.
4. Only Learn What You Need for Your Job
Despite being a “jack-of-all-trades” kind of engineer, you should never be a “master of none”. In fact, you should become an expert in your job and take the time to learn whatever you need to in order to get your work done in a more effective way. Let your work guide what you need to learn, don’t waste time learning skills that you will never need. Try to predict the additional skills you would most likely need to continue flourishing in the future.
In the technology industry, change is rapid and unavoidable, which is why Forbes highly recommends staying at the forefront of new trends and innovations. Doing so will not only provide you with foresight within your field, but also allow you to easily take advantage of change. Actively add new technologies, languages or techniques into your repertoire.
5. Don’t Forget the Soft Stuff
For the longest time, working long hours has never been synonymous with greater productivity, or taken as a sign of one’s dedication and efficiency. But this notion is slowly being challenged. A study cited by Verizon Connect has shown that you can be just as productive by only working for 50 hours instead of 70 – it all comes down to working smarter, not harder. Set a schedule for each and every one of your tasks and remember to take breaks. And remember: if you’re just staring at your computer screen, that’s not considered a break. Make sure to go to a different location – or, at the very least, stand up from your seat and do some stretching. Try to attain work-life balance and set aside some time for hobbies and exercise.
Thrive Global’s article on the relationship between work-life balance and productivity explored how doing these things will help you attain better mental and physical well-being, prevent the likelihood of burnout, reduce stress and improve motivation.
As the global demand for data science grows at a rapid pace, the responsibilities of data scientists increase by the day. This makes it all the more important for data scientists to focus on the different ways they can grow more productive.