For a while, I tried the bullet journal system of note-taking, in which you use icons to label the nature of every singl

He looked baffled. His mouth hung open, and his eyes searched the room and my face looking for the right thing to say. What phrase could he spit out that would make this moment stop so he could keep going about his game?

When my kids act out, I tell them that they need to change their behavior, not say the right words to appease the offended. But that isn’t enough; they have to sit with me and talk through how they will change their behavior. I listen. They have to talk about how their actions made another person feel. I listen. They have to tell me how they would feel if someone acted out similarly toward them. I listen.

The more extensive answer is: it depends. First of all, you should not spend your free time on topics that you do not enjoy. Life is too short to do so. In my opinion, the best project is the one with the highest long-term return and the one that you enjoy most.

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tegies such as predicted ranges or unsupervised detection have some significant advantages. They are easier to set up and easier to maintain over time. They can also be used to test any data for any condition, regardless of the current quality of the data.

So, imagine you are a Data Scientist and the position at your company requires you to develop machine learning models. For that, you get a specific research question and a well-organized data set by your data engineer. This means that you never really tackled the process from using raw data bases and applying feature engineering techniques in order to make the data usable for machine learning procedures. Therefore, an appropriate personal project could be for example to use web scraping techniques to store the resulting data in a database of your choice. Afterwards, you could develop a SQL pipeline in order to transform the tracked web data into data sets that are more comprehensive. Then, you could use these data sets as a basis for feature engineering and feed the extracted features into a machine learning model of your choice.

But the thing is, there was nothing right to say at that moment. My kid needed to take steps toward accountability and not depend on me to permit him to skirt the growing problem. I genuinely was sick and tired of intervening in my children’s drama, and it was time for me to correct my lazy parenting and hold them answerable for their own crappy choices.

“But Mom! You’re supposed to accept my apology because you’re the MOM, Mom!” His confusion that I stepped out of this dance said it all. I was part of the problem, and I was refusing to do my part.

It’s not a perfect system, and my kids still act like monsters, but I can see them thinking through their actions more closely now. Yesterday, my youngest was needling at her brother’s last nerve, and my oldest kiddo walked over to her and said, “Hey, how would you feel if he kept jumping on your legs and yelling your name really loud?” She was too young to rational this, but in a parenting win for me, I was proud to see that my oldest, my eye-rolling, knows-everything-about-everything-Mr-attitude tween, was trying to teach his little sister the lessons that I have been trying to teach him.

To prevent any misunderstandings: I really love to do so! One of the best things I discovered after transitioning from academia to the corporate industry is that the development time for specific features is extremely short and you are really forced to deliver features in a short amount of time. I only profited from that choice I made after finishing university, since I discovered many data science tasks, which I never would have related to a data scientist. I also would go so far and say that the industry is the best way to become a full-stack data scientist.

I recently started a new job, and I’m in that phase where I’m constantly bombarded with new and unfamiliar concepts, from company vocabulary to industry know-how. The learning curve is steep. At every meeting, I take furious notes, trying to soak in as much information as possible.

“I do not accept your apology, but I will accept changed behavior, now tell me what you plan to do.” These are the words that I find myself saying lately. The idle apologies that are more performative than teachable moments are no longer acceptable here, and my kids know it.

But the initially described circumstances keep in place. While you will grow as a data scientist in terms of getting faster by rounding developing cycles over and over again, you may have problems to discover new fields of data science methods. Therefore, it is important to have an open eye on new methods coming up by personal projects. The will to never stop learning will impress coworkers and managers, since you are able to develop new skills that you can re-use in your full-time job. It will open up new possibilities in your data science career because you gain skills by accomplishing project after project.

It’s not a perfect system, and my kids still act like monsters, but I can see them thinking through their actions more closely now. Yesterday, my youngest was needling at her brother’s last nerve, and my oldest kiddo walked over to her and said, “Hey, how would you feel if he kept jumping on your legs and yelling your name really loud?” She was too young to rational this, but in a parenting win for me, I was proud to see that my oldest, my eye-rolling, knows-everything-about-everything-Mr-attitude tween, was trying to teach his little sister the lessons that I have been trying to teach him.

If you are working in corporate data science, you are probably spending a lot of time on particular products. In some companies these product life cycles are nearly infinitely long. The consequence is that if you join large corporates, they will already have products in place. You are usually working in a developer team and will be adding particular features to these products. And if you transfer from one product development team to the next, you usually stay in your comfort zone, which means that if you were the NLP guy before, it is really likely that the next product feature you are supposed to develop will also demand NLP features. Further, you usually will have existing methods to solve specific problems such that a method is implemented for one feature and recycled for another. The focus lies more on trimming the existing method to a new set of questions and less on developing a new method from scratch. This is simply how the corporate world is working because this ensures fast results, which reduces internal development cost.

The consequent question that arises now is: “Okay, now I know that I should be working on private projects apart from my full-time job. But which projects should I choose?” The short answer is: choose whatever interests you, which you cannot tackle during your 9–5 daily job.