Are you actually busy, or are you just being unproductive?
Whenever it feels like one more task will sink my ship irreparably, I ask myself this question. It brings me down to earth and makes me consider whether I’ve made the best use of my time in the past week or if I’m just "busy".
We wear the badge of "business" with honor, like something of a status symbol in our deranged work-obsessed society. If you’re not busy, you’re not working hard enough to make it as a data scientist – there will always be someone who will work harder and take your place in a heartbeat. But as John Spencer so succinctly puts it: "You don’t get a trophy for packing your schedule with more projects and more accomplishments and more meetings". Of course, we subconsciously know this, but somehow we keep working like we will get some award or some bonus or some raise – even though we all know that our hard work will go unnoticed 9 times out of 10 because we’re just in the data department.
Therefore, I can’t think of a better goal for 2024, than to become a productive data scientist. Here are my 9 best tips for going from a "busy" data scientist to a productive data scientist.
1. Produce a task-prioritization matrix
You’ve heard it before and you’ll hear it here again: using a task-prioritization matrix will increase your productivity as a data scientist.
Whenever I’m feeling overwhelmed with the number of tasks piling up, I write them all out on pieces of paper and then begin to categorize them by their urgency and importance, using the same formula as the Eisenhower Matrix you see above. This immediately provides a clear picture of what needs to be done, what can wait, and what is irrelevant. These tasks might be unique to a specific data analysis you’re conducting for a client (so you would build a matrix on a project-by-project basis), or they might just be the tasks you have for an entire workweek.
Productivity stems from focusing on what is immediately urgent and getting rid of all the distractions and unnecessary tasks that crowd our desks. In Data Science where most of your work is wrapped up in producing deliverables for deadlines, this can be a handy way of ensuring that you’re prioritizing the right stuff.
However, it’s also important to not get too wrapped up in ensuring that everything falls into these categories as we think it should. Your boss will inevitably give you a task that you think is neither urgent nor important (like changing the chart bars from red to orange), but to them, it will be. This is not a war to fight. However, for the rest of your standard tasks, you can generally get away with organizing them this way with very effective results.
2. Set goals for each day, week, month, and project
Productivity stems from having a clear roadmap of what we want to accomplish and how we plan on getting there. Yearly resolutions are not going to get you through the days, weeks, months, and multitude of projects therein, which is why you need to set these types of goals accordingly.
You’ll rarely see someone accomplish their big yearly goal if they don’t have a framework of goals for each day, week, month, and project leading up to the end of the year. Similar to the phenomenon of people losing motivation to complete their New Year’s Resolutions by February, you won’t have a very productive year as a data scientist if you don’t have some daily, weekly, monthly, and project-oriented goals to push you forward.
In the data science work environment, most of these goals will be set for you by your project team, all of which will revolve around project deadlines. However, there will be some leeway in how you go about achieving the goals unique to your part of the project. To ensure you’re productive in achieving these over-arching goals, you’ll need to weave in your own set of goals to ensure that each day, week, and month leading up to a project deadline is full of productive work. These could be small, such as, "On Friday, I will have read the client-provided documentation and will have my notes and questions ready for the meeting next Monday", or large, such as, "By the end of the month, I will have refined my model to give statistically significant results to p<0.05".
Whatever your goals, you should have a monthly calendar filled with goals color-coded by whether they’re goals for the day, week, month, or are affiliated with a specific project deadline. This gives you a clear roadmap of what you need to be accomplishing each day to remain productive.
One last thing to note is that you should only pick 2–3 goals for each day. Be realistic with yourself about how long it will reasonably take to complete these goals. By keeping the number of daily goals achievable, you’re ensured to have achieved some level of success at the end of each day – and who doesn’t need that?
3. Track your time
How much time do you think you work every day? No seriously, like actually work?
The "screen time" feature implemented on many popular devices has been revolutionary for me in thinking differently about how much time I’m actually spending on work. You may think you’re busy at work, but the amount of time your phone says you’ve been on Instagram today begs to differ.
Tracking your time can be a game-changing tactic to help you determine how much time you’re spending on work each day, how much time you’re spending on each type of task, and whether or not that time is truly productive work. It can also give you a good idea to see if certain tasks are taking you longer than they should. For example, perhaps you’re spending too much time on making data visualizations. This could be fixed by creating a stylesheet and sticking to it to ensure that you’re not wasting time looking at fonts or making sure your colors are accessible to all viewers.
There are time-tracking apps available (one of my favorites being Forest) but I find that using the timer on your phone and the Notes app is the simplest way. But heck, you’re a data scientist, why don’t you just write the code to automate this task? All you need to do to get started is to start the clock when you begin working, stop the clock when you’re finished, and then make a note on your phone of how long you worked and what task you were working on.
It’s truly eye-opening to have your working hours right in front of your eyes. You may be contracted to work 9–5, but how many hours are you truly working? Further, how many of those hours would you deem to have been productive? The jury is still out on the golden number of hours we’re productive in a work day, with it currently residing somewhere between 2–6. From personal experience conducting data analyses and computer modeling, my limit on productive work seems to hinge somewhere between 5 and 6 hours – anywhere beyond that and my brain feels like it’s fried. However, it’s important to note that this varies depending on the work I’m doing. Technical work with Deep Dives into code or data (especially when it’s not cooperating as it should) drains me much quicker than if I’m writing a report or creating pretty visualizations. This will be unique for everyone, so it’s not a bad idea to also log how you feel at the end of each workday to see if you can begin to sense some patterns in the tasks you work on, your productivity, and your energy levels.
4. Time block
I don’t know about you, but my code comments make no sense if I’m also having a meeting, answering emails, and petting my dog while I’m trying to write them.
While everyone has a different working style that works for them, I think we can all agree in 2024 that multi-tasking is probably the most ineffective Productivity hack ever known. Instead, time blocking has begun to take its place as a true method for increasing productivity.
Time blocking is when you allocate certain chunks of time within your day to a specific task. For example, the first two hours of your workday could be allocated to administrative tasks, such as answering emails and attending your daily standup meeting. The next hour of your workday could be focused on dealing with your bug backlog. After lunch, you dedicate three hours to cleaning, preparing, and analyzing data. The last hour of your day is spent in a couple of meetings.
Why does time blocking make you more productive? It allows you to settle into a task or specific type of work by not allowing for distractions that might break your stream of focus. For example, you’re not going to get anywhere in debugging your code if you’re constantly having team members poke their heads into your office to ask you questions. Our best work gets completed when we can singularly focus on what is required, and our "busy" work happens when we have six different distractions demanding our attention away from what needs to be done.
Time blocking can be challenging to do in an office environment due to your accessibility to those around you. However, blocking off sections of time within your day in a shared calendar space and ensuring your office door stays closed during those deep work periods can be a great way to start this productivity habit.
I found time blocking around the time when I began working with data and code again and found it to be the best tool in my productivity toolbox. Giving yourself three uninterrupted hours to work with code or data allows you to live within it, get a deeper understanding of how the data fits together, and get a clearer picture of how the code is supposed to work. In other words, working for three uninterrupted hours will produce better, more efficient results than working for six interrupted hours.
5. Set up a workflow of productivity tools, i.e., Git for version control, Trello or Notion for organizing projects, Scribe Chrome extension to create visual step-by-step guides, website blockers, etc.
Your potential for productivity as a data scientist is only as strong as the tools you surround yourself with.
To start, pick three areas of your work that need some organizing and streamlining. For example, this could be your code version control, your daily schedule and project planning, and your tendency to get distracted and start doomscrolling when your code stops working. From there, decide whether you want to handle these areas in a digital or analog manner – in other words, do you need a tool on your computer or do you need a physical solution? For code version control, you’ll need a digital version control repository, such as that available through GitHub. To handle your daily schedule and project planning, you could go analog or digital. Going analog could mean buying yourself a daily planner for your scheduling and a wall calendar for your project management. Digitally, some of the go-to tools include Notion, which could be used for both daily schedules and project planning, and Trello, a handy little tool for project planning. Finally, dealing with your doomscrolling can be as easy as installing website blockers on your work devices and setting your phone to "Do Not Disturb". There, you just set up a productivity workflow!
Productivity workflows are unique systems, which means that trial and error will be involved. For example, I found that I loved using GitHub Desktop to make my code commits, but couldn’t be bothered to use Git from the command line. In the same way, I use Notion to plan my projects but just use the calendar on my phone to schedule my day. Further, I need my phone to be in a completely different room if I need to get some deep work done. It’s a great idea to look up other people’s productivity workflows on sites like Medium and YouTube to get some inspiration and ideas for which tools might work best for you, but it’s never a good idea to base your workflow on someone else’s. It may take a few months to perfect your system, but you’ll know that it’s the right one for you when you don’t even have to think about it.
6. Improve your communication skills and re-consider what actually requires a meeting
While many job-hunting-related articles here on TowardsDataScience stress the importance of communication skills for data scientists, many of us are admittedly less accomplished at these skills than we like to admit. We may be masters at communicating data stories to a client, but we may fall short at communicating where it can really make a difference: with our teams.
Nothing stalls productivity more for a data scientist than team members poking their heads in or sending Teams messages asking them to re-explain a task that needs to be done. Even worse, is working within a team who doesn’t have a good concept of what topics or crises require a meeting. While there are exceptions to both of these scenarios, such as working with interns or actually having something blow up, more often than not, these small productivity inconveniences can be eliminated. It all starts with strong, clear communication.
At a personal level, begin practicing your communication skills when it comes to explaining tasks, concepts, and results. This can be as easy as talking to your desk duck or your mom, writing blog posts and articles online, or creating YouTube tutorials. Whatever your method, practice communicating until you can get your point across once without follow-up questions. If you do get a follow-up question, see if you can answer it without getting a second. Clear, concise communication only once per question = more flow state productivity for you.
At a team level, set some team standards (and more importantly, enforce and stick to them) for what constitutes the need for a meeting. Virtual or in-person, meetings are more often than not a drain on productivity and energy when they’re not spent tackling an honest problem. This simple task is a quick way to produce constructive teamwork and communication that can easily be modified to fit the workflows and projects that may come up.
In the same vein, do endeavor to also become a better listener this year. Asking someone to repeat themselves because of a lack of understanding of what they’re asking or lack of knowledge of how to do it is perfectly acceptable; asking someone to repeat themselves because you weren’t listening is not.
7. Do the hard tasks first thing in your workday and do the easy tasks later in your workday
The beginning of your workday, whether that’s 6 am or 6 pm, is the time when you will feel the most focused, persistent, and energized. That is the time when you need to tackle the hardest to-do on your list for the day.
Whether that’s getting through the task of cleaning data, developing presentations, or fixing data pipelines, you need to begin your workday with your hard tasks and save the easy stuff for later.
We’re all familiar with the mid-workday slump, where your eyes can barely stay open and it feels like you’ve been staring at the same bug for 30 minutes without having really tried to fix it. This is not the time when you should be attempting to fix your computer model because, more often than not, you’ll probably make things worse, or more likely yet, won’t get anything moving in the remote direction of "fixed".
The best way to make sure your hard tasks get done first thing is to start your day off by making a master to-do list of everything you need to get done that day. Referencing your task-prioritization matrix (see tip #1 above) comes in handy here. Pick 1–2 tasks that you know would just drain the life force out of you and work on those first thing – the rest of your tasks can be worked on later. Save mindless tasks or tasks you find easy, for the mid-to-late point in your workday where you begin feeling more sluggish and uninspired. You’ll find after a couple of days of doing this that your days start more productive and remain more productive until the time when you log off.
8. Become a better team player to optimize collaboration and task delegation
I once worked on a team where one of the members was new to team leadership on this scale and had a lot at stake regarding how well the team performed. This resulted in this team leader not trusting their team members to do their work, so while task delegation occurred, it always resulted in the team leader being deeply involved, often right up until the time the tasks were due. Naturally, this devolved into everything being completed only at the last minute, with the team leader always asking for last-minute changes to be made down to the last second before task submission. This often looked like a lot of "busy" work followed by one or two 12-hour days trying to make changes before the submission deadline. Not a very healthy work environment where every single project comes down to the last second, right?
From this story, I hope you gathered that optimized teamwork is one of the best ways to ensure your productivity as a data scientist. Whether you’re a single data scientist at a small startup or part of a team of data professionals at a large corporation, setting up effective inter- and intra-team systems is paramount to your productivity.
It all boils down to team norms for collaboration and task delegation – both of which require that each party trust that the other can complete their work to a set standard by a set deadline. While many of us can feel as though we’re on an island while working, it’s important to remember that our results and deliverables are often depended upon by other members of an organization to complete their tasks. As such, teams should get together early on to establish standards for deciding who needs to be working together, which tasks need to be delegated where, and how long before project deadlines these tasks need to be completed to ensure sufficient time for edits.
Now these will seldom perfectly stick because, let’s face it, this is the real world – if we’re not constantly moving towards a state of disorder, then we’re disobeying the laws of physics in a concerning way. Data can be harder to wrangle than expected, your teammate may get egregiously ill while they’re supposed to be visualizing data only they understand, your predictive model may be returning results that are suspect at best, and your supervisor may give you last-minute corrections seconds before the deliverable is supposed to be sent out. However, even just having the above-suggested standards in the back of each team member’s mind can be enough to make everyone a little more cognisant of how to work together more productively. The way to make this stick a little better every time is to keep reading to see how monthly reflections can be used to evaluate the effectiveness of your productivity strategies.
9. Take some time at the end of every month to reflect on what’s working, what isn’t, and how things could be done more effectively
It’s not a bad thing at the end of a month to sit there, be honest with yourself, and go: "What the heck happened in there?". However, you shouldn’t make it a habit of ending every month like this. How you respond and move forward is what matters.
Stress is a quick eroder of productivity, which is why it’s a good idea to take a step back at the end of every month (a period in which many stressful things have likely occurred and inevitably placed you back in the "busy" camp) and see what worked, what didn’t, and how you could be more productive next month. Maybe this means you need to develop a better folder system for your data storage, or maybe you need to delegate more explanatory tasks to your analysts to allow the scientists to focus on more predictive tasks. Maybe you just need to put your foot down and put your phone in a time-locked security box to keep from "doomscrolling" instead of dealing with the bug-filled pile of spaghetti your code is currently resembling.
Whatever your setbacks or gremlins, take some time at the end of each month to honestly write down what increased your productivity, what took away from it, and what you’ll try to implement or keep doing in the next month to keep yourself from getting "busy".
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