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However, I think I made my point clear: side projects will improve your data science skills, since you use your free time enhancing your weaknesses. Choose your side projects wisely and enjoy them.

In other words, you should always aim to tackle all possible tasks of a full-stack data scientist, for which you may not have enough time in your company at the moment but you really wish to understand.

Web portals are often created for internal purposes for a business, organization, school, or institution. They usually involve a login with a personalized experience based on the user. For example, a university might have a web portal allowing students to access information about their courses or a company might use a portal to allow their employees to request time off and view pay stubs.

After completing the described project you will definitely know more about the data engineering process, which is from my point of view also a very important stage in the data science project roadmap. You can use the gathered knowledge also at your company when you discuss the data engineering process for the next tasks. This yields a high return for the whole company, since the better coordination between data engineering and machine learning development will make the implementation more efficient.

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.

We are all working very hard and feel like we do not have enough time for activities, which we enjoy or for people that we love. And data science is not everything. So commit to personal data science projects outside your job only if you truly want to accomplish them. If you do not feel like doing so that is totally fine. I am sure your corporate job will provide you the opportunity to grow anyway. But if you want to have a slight boost and have a great project idea, simply go for it!

Similar to social media websites, once the skeleton of these websites is designed, most of the upkeep is on the tech side. With so much traffic and bandwidth streaming, these websites require a lot of backend maintenance. Streaming companies tend to have their own in-house design and development teams that work on long-term updates.

This is not a call that you should work longer and longer for your company, this is simply the suggestion to choose your side projects wisely. Consider the example that your position requires you to develop and evaluate different neural network parameterizations and you really enjoy to work on it. In my opinion the better choice to work on these topics is to find an even better neural network for the use case at your company instead of developing and solving a different use case with the same set of methods by yourself.

We are all working very hard and feel like we do not have enough time for activities, which we enjoy or for people that we love. And data science is not everything. So commit to personal data science projects outside your job only if you truly want to accomplish them. If you do not feel like doing so that is totally fine. I am sure your corporate job will provide you the opportunity to grow anyway. But if you want to have a slight boost and have a great project idea, simply go for it!

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eo is hot again — professionally, speaking. Stories can help grab the attention of LinkedIn users who are online at ungodly hours, trying not to open Twitter again. Don’t, however, treat Stories like Instagram or TikTok. Here’s a teachable moment: One night, I chugged three bottles of red and posted a video to LinkedIn of me “Netwerking.” I received several unprofessional messages as a result, and guess what I did? I responded to every single one of them because you never know where your next opportunity might be.

Video streaming sites like YouTube, Netflix, Hulu, Amazon Prime, and many other competitors have surged in popularity over the last decade. Some are free, while others offer paid services.

Companies that run these sites, like The New York Times, rarely update the design or work with individual designers. They mostly work with agencies or companies that help with design, development, and maintenance.

Working on the same topics in private as you are working on at your company can be very problematic, which brings me to my second point: Do not use internal company’s information for your private projects! Often you are using for private projects open source version management tools as git and even if you do not, I am pretty sure that you are probably not allowed to apply internal developed models of your company in a private way, or even worse use company internal data for your private projects.

In the last section, I described which projects you should choose. However there are some red lines in the sense of projects you should definitely not choose. In general, I think that you should not be working on side projects, which you are already working on during your job. Following are two reasons, why I think so:

First of all, a corporate company is a great opportunity to grow as a data scientist. If you really love exactly what you are doing during your job, you maybe should simply extend your working hours. This will probably make a better impression in comparison to walking out of the office at exactly 5pm, go home and spend the evening with the same things you could have done equally if you stayed an hour longer at the office.