How do I become a Data Scientist?

Want to become a Data Scientist but don’t know where to start? Discover the skills you need to acquire and the existing training courses to achieve your goal!

If you’re looking at this file, it’s probably because you want to become a Data Scientist. In fact, you probably already have an idea of what this profession is all about.

We will therefore not dwell here on the definition of this profession. However, for those of you who have come here a little by chance and have no idea what it is all about, here’s a quick reminder.

Simply put, a Data Scientist is responsible within an organization for analyzing data to solve problems or answer questions. Unlike the Data Analyst, however, the Data Scientist uses Machine Learning and statistics to do this.

That’s what allows him to produce predictive and/or explanatory models. For further explanations, do not hesitate to consult our complete file at this address. Now, let’s get down to the heart of the matter: how to become a Data Scientist?

What is the profile of a Data Scientist?

The first Data Scientists were mainly developers, computer scientists, engineers. They created Machine Learning models, optimized processes, analyzed unstructured data, created specific programs for each problem and performed manual map/reductions.

Fortunately, with the advent of high-performance programs and packages, most of these operations are now greatly simplified or automated. Today, a Data Scientist spends more time on modeling… than engineering.

As a matter of fact, the learning of the trade is also made easier. Different profiles can become Data Scientists today.

One of the main reasons is the rise of the Python language, which is easy to master and relatively intuitive. In addition, some of the Data Scientist’s tasks are now delegated to other experts. For example, the Data Engineer takes care of data preparation.

The production of algorithms is simplified by tools such as SageMaker, and even the creation of complex engineering functions is simplified. automated by AutoML. As a result, there are fewer and fewer “standard profiles” of Data Scientist.

What are the skills required to become a Data Scientist?

Now let’s look at the skills required to become a Data Scientist. First of all, it is necessary to learn computer programming.

Some of the most commonly used languages in Data Science include Python, R or Scala. However, the priority is to learn Python. For good reason, this language is the one that federates the largest community of data analysts. It will therefore be easier to find sample analyses on platforms like Kaggle, sample code on Stackoverflow, and even job offers.

Of course, a data scientist has to master the basics of Machine Learning. In particular, you will need to get to know and understand the different models of machine learning, and choose which ones to apply based on the problems to be solved.

In addition, the Data Scientist must also be statistics expert. This is what distinguishes it from the Machine Learning Engineer. You need to learn how to perform explanatory data analysis, know the basics of probability and inference, and understand the concepts of selection bias, Simpson Paradox, variable association, and experiment design.

How to acquire Data Scientist skills?

To master the Python language, you can turn to the most popular courses. The MIT offers a course This is a 120-hour “Introduction to Computer Science and Programming Using Python”.

L’University of Michigan It offers a 30-hour “Python for Everybody” course. Both of these online courses are very popular, and several thousand people have already completed them.

There are no pre-requisites, so even beginners can turn to these two options. If you already master other programming languages, and wish to discover the Pythong language, you may prefer thehe 4-hour course offered free of charge by DataCamp.

The most popular course for acquiring Machine Learning skills is the one offered by on Course by Data Scientist Andrew NG from Stanford University. This 60-hour course offers you the opportunity to discover machine learning in a technical way using the Octave language. However, knowledge of linear algebra and statistics is preferred.

You can also turn to the Machine Learning course proposed on Course by the University of WashingtonThe duration of the project is approximately 180 hours. Similarly, a nanodegree Machine Learning of approximately 120 hours is available on Udacity.

About statisticsMIT offers a free 160-hour Fundamentals of Statistics course. This course is extremely comprehensive, and will teach you how to use a model for each data set, how to choose variables in a linear regression, or how to model non-linear phenomena.

However, this course may be a little too technical if you don’t have a solid grasp of mathematics. There are several alternatives, such as the Probability course offered for free on edX by Harvard. This training of less than 12 hours focuses on probability.

Harvard University offers, always on edX and always for free, a 12-hour “Inference and Modelling” course. to learn how to create statistical models and to understand the reliability of these predictions.

For a French-language training, we recommend DataScientest: the French leader in distance learning to data science. Its certified training courses are highly recognized in the sector.

Several books can also help you acquire Data Scientist skills. Some of the best are Data Science from Scratch by Joel Grus, Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython by Wes McKinney, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systemsor Think Stats: Probability and Statistics for Programmers.

Which websites to visit to follow the news of Data Science

The books and online training courses mentioned above are good references for acquiring a solid foundation, but Data Science is a discipline in constant evolution. It is therefore important to keep up to date, by frequenting platforms on which professionals converge.

The Kaggle platform, owned by Googleis an excellent source of examples and discussion of data science. Many competitions are organized, and will be an opportunity to get your hands on the rewards that may come your way.

The KDnuggets website, created in 1997The Data Scientists’ Newsletter, also includes numerous publications and other content written by Data Scientists. Here you will find valuable tips and applications. Thousands of Data Scientists also gather on the AnalyticsVidhya and TDS blogs to share content.

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