Welcome to this my new article on how to become a Data Scientist. Here I will try to answer all your questions and tell you about my experience of studying.

Premise: while I am writing this article I am not working as a Data Scientist but I can tell you what to study to work in the field.

So if you are a student or a person who wants to change his job, this article is for you. What you’ll have to do is read until the end so you know what will be waiting for you during this path (if you decide to take it).

At last, before I begin, I want to tell you why I am sharing this information with you.

Since I chose to change my career path from marketing to the field of AI, I’ve come across several articles and courses. Some of them have helped me understand the subject better, while others have just wasted my precious time.

Ok having made this second premise, let’s start with the basics and together let’s try to understand what Data Science is.

What is Data Science and why it is important

Data Science is an interdisciplinary subject, we can say (in non-technical jargon) that it is a soup of skills needed to analyze, understand and manipulate data. And thanks to these analyses and manipulations, predictions can be made that will help the company grow.

It is important for a company because thanks to the countless data it collects it is possible to make predictions about the business, so it helps to understand what will be the best move to make in the coming months and/or years.

Who is the Data Scientist and how much does he/she earn?

The answer to this question is very intuitive, but I want to specify it.

The Data Scientist is the person who in the company, along with his other colleagues, deals with Data Science and therefore puts into practice all his technical knowledge acquired during the practice and study of various subjects.

Thanks to the horizontal or specialized knowledge of all the subjects that make up Data Science, this professional is well paid by companies. His salary can reach up to $118.851 annually depending on the experience and the company he works with.

WOW Antonio, but so what does the soup you mentioned above contain? What are these disciplines that Data Science is composed of?

Read also: Data Scientist who is he/she?

Data Science disciplines

If you decide to study this subject, you will encounter several subjects in your study path that you will need to become a good Data Scientist. Here they are:

• Linear Algebra
• Statistics
• Programming with Python or R
• Database Management with SQL/NoSQL
• Data Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Communication skills

These are the necessary skills that a Data Scientist must-have, and I know they may seem like too many but don’t get discouraged, I’ll tell you why.

Why become a Data Scientist: growing job market

This is a newborn profession and it is only in the last few years that it is exploding and undergoing strong growth.

Many companies are looking for more than one Data Scientist, including Junior and Senior figures, and this demand will increase more and more as more data and companies are born.

That’s why it’s important for you to start understanding and studying the subjects that make up a Data Scientist. It’s not too late.

But now that you understand why it’s important to study Data Science I’ll point you to a path you can follow to become a Junior Data Scientist.

WARNING: The ones I’m going to show you are totally online and OnDemand so you can follow them whenever you want; only afterward we’ll talk about universities.

A path to a career as a Data Scientist

During these years of training, I have taken many courses and have become quite knowledgeable about what this discipline is all about.

My advice is to study slowly and take your time to learn everything, especially if you don’t have a background in any of those subjects.

Nowadays there are several courses that promise to make you a professional DS, but I’m sorry to disappoint you because it is not that simple. In order to become a DS, you will have to study a lot and most importantly, practice a lot in the field.

The best online courses (made by those who are really competent in the field) are gathered in a single platform: Coursera.

With Coursera, you can easily take the courses I’m going to show you and practice at the same time to learn the concepts better. Also you can get all the certificates for the courses.

Usually, on this platform to practice and get certifications, you need to subscribe to the course or specialization. The more time you take to study, the more you pay a certain amount monthly.

What to do about this?

My advice is to take the annual subscription to Coursera Plus that allows you to save money and attend all the courses that the subscription makes available, among them, there are also those that I’m going to recommend.

The first course to take (actually it’s a specialization, meaning multiple courses) is Programming with Python if you don’t know how to program.

Why Python?

Today Python is the most popular language and more and more companies are adapting to using this programming language.

The syntax of Python is very simple if I have to say it is the simplest among the programming languages. Moreover, in this specialization offered by the University of Michigan, they will also introduce you to the Database management part in the course “Using Databases with Python “.

The second specialization I recommend is “Applied Data Science with Python Specialization“.

Here you will know and practice with the foundations of Data Science, learning about the libraries needed to do data analysis and manipulation, as well as drawing graphs and learning about Machine Learning.

Finally, to improve what you have learned in the 3rd course of the specialization that I recommended above, I suggest the famous course by Andrew Ng that explains in detail Machine Learning and its algorithms.

Unfortunately, the course is done with Matlab a programming language a bit old, but in this case, it’s up to us to know what is behind the algorithms that we are going to use.

Bonus

Another specialization that I want to recommend but you don’t have to do to become a Data Scientist is the ***Deep Learning Specialization***, again explained by Andrew Ng that will help you understand Deep Learning techniques that a company could apply to their business.

At least, let’s come to a crucial question that many are asking.

Antonio, but is it necessary for me to attend university?

Unfortunately, I can’t give you a correct answer, I can only tell you that the answer is subjective.

The path I indicated allows you to have the necessary skills to become a Data Scientist within a year. The skills and practical projects that you will carry out independently will help you to open doors.

This obviously does not exclude that in the meantime you want to follow university courses that integrate well with this path.

Obviously, the opposite is also true. If you are already a university student, the path I have indicated could help you to strengthen your university knowledge.

This is your decision! For me, it matters today to demonstrate your skills, not to have an extra certification to hang on the wall.

Conclusion

Becoming a Data Scientist is not easy at all, you have to be constant in learning and if you want to become a real expert you have to do a lot of practice and have experience in the field, on real problems!

The important thing is not to get discouraged at the first difficulties, to keep repeating and practice more if the topic you are studying is not clear to you.

I hope this article has been useful to you, also I would love to hear your opinions here in the comments :D.

See you soon,

Antonio.