Want to become a Data Scientist in 2024 ( onwards )? Do these first to build the foundation!

There is no doubt that Data Scientist is one of the coolest professions. Building machine learning models should be one of the coolest things in the tech world. However, This is not the only thing a data scientist does, though. Building ML models is one of the several areas. So what does a data scientist do? What are the focus areas? We will get there in a bit.

But for a moment, let’s focus on what a scientist does generally.

A scientist asks high-quality questions about a problem he/she is working on to find a solution. In that quest, a scientist builds hypotheses, conducts experiments based on the theory, and finally draws a conclusion based on data and facts. A data scientist does the same but the data is on the center. A data scientist asks high-quality questions to solve a problem / seeks truth from the data of a domain.

It is essential to note that a scientist and a data scientist share a similar problem-solving approach. They both ask high-quality questions to discover a solution. A scientist builds hypotheses, conducts experiments based on the theory, and draws a conclusion based on data and facts. Similarly, a data scientist solves problems related to data by asking high-quality questions to find a solution concerning data.

The spectrum of responsibility for a data scientist is wide. Exploratory data analysis, statistical modeling, Data visualization, building machine learning models as well as contributing to establishing an AI system. The branches where a data scientist could work are also quite wide. Predictive analytics, Process optimization, Operation research, Natural language processing, robotics, image analysis, etc. are some of the branches. But how do you become a data scientist?

Let me share with you my perspective if you just starting.

A Study program at the University

Yes, a study program at the university. It is not an online course at the first step if you do not have any background in engineering or IT.

Surprised? Let me explain.

Although it depends on which part of the world you are living in, generally speaking, the value of studying at the university and getting a degree in your field of interest is incomparable. Studying Data Science or Computer science or any relevant engineering program is the first and most important step of this process. There is no alternative to that to become a data scientist. In the study program, you get a chance to learn and practice some of the basic theories and programming. Furthermore, you also develop problem-solving skills at the university. Even there are study programs that focus only on the data science field.

You may notice, that in many universities, the professors are conducting a lot of research in data science. That would be an amazing opportunity to learn from them. You get the chance to discuss your curiosity in data science with your professors. You may get a chance to work with the professors on their projects where you could learn even more and see the maths in use. Furthermore, some universities have cross-faculty projects in their curriculum. That is another opportunity to reinforce your knowledge along with fellow students. You develop your skills in describing and discussing the results scientifically.

There is no boundary for learning. Of course, additionally, you could also do some online courses in any learning platform to get another way of explanation. That could be helpful as well. However, you should not rely on online courses to become a data scientist. You build the foundation at the university.

That being said, if you are studying other engineering courses or already have an engineering background like electrical engineering, you will develop or already have problem-solving skills using math and physics. In that case, further (online) courses and doing some practice projects about data science, machine learning, deep learning, etc will be very helpful.

Internships

Now data is one of the foundational pillars for every industry. Companies are always looking for interns. This is a great opportunity for the students to get real-life experience. Furthermore, the internship creates potential opportunities for future full-time employment.

In most universities, doing an internship is mandatory. In some universities, this is voluntary. Either way on 4th or 5th Semester you have to / can do the internship. Make sure you have completed all the required courses. Else it won’t be that used. Look for the industry that you find interesting. Manufacturing, Pharmaceuticals, E-commerce, Internet, etc., are some of the areas where companies are using data more intensively. Look for companies who are solving real problems using data to generate values for the customer. You will learn a lot about exploratory data analysis, data preparation, data visualization, applying machine learning methods, and most importantly framing a business problem to apply these. Most of the time it is also possible to do your thesis at the same company. It would be much more optimal if you could do the thesis on one of the topics from your internship. This will help you focus more time on the scientific side since you are already aware of the topic.

Coding is one of the mandatory tools to learn and master. If you are not interested in coding but want to be a data scientist, still, the road will be very rough, to be honest. I can’t emphasize enough how important coding is. Learning the theory will help you to understand and solve a problem more scientifically. But you have to solve the problem using code at work. Therefore, learning to code and getting better at it over time is extremely important.

You will have the chance to learn about it during your studies; however, you have to practice more to get better at it. Python is the most used programming language in this field. To get more experience, do additional projects. Get familiar with libraries like Pandas, Numpy, Matplotlib, etc, and machine learning frameworks like Tensorflow, PyTorch, Scikit-Learn, etc. Learn and implement clean code principles. Learn design patterns and object-originated programming. Learn about version control systems like git. Add your projects to the git public repository. Learn how to navigate technical documentation. If you already have coding skills, learning about cloud platforms and containers will be more valuable

In summary, building a strong foundation for becoming a data scientist requires a combination of scientific problem-solving, statistical analysis, coding proficiency, and effective communication skills. Skipping any of these steps will make it difficult to succeed in this field.

I hope you find this information helpful. Cheers!