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Why learn Data Science in 2020?

Originally posted on Medium by Jaime Zornoza

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Data Science. Big Data. Machine Learning. Artificial Intelligence. These are all powerful pairs of words. Let’s see with real studies, numbers, and images why you should consider learning about any of them.

Why learn Data Science or Machine Learning?

Data Science has been defined as ‘The Sexiest Job of the 21st Century’ by Harvard Business Review. Glassdoor, a world-known website for job seeking, ranked Data Science as the best job in America for 2019. Bloomberg regards Data Scientists as the new Superheroes.

Data Scientists, Machine Learning Engineers, Data Engineers; all of these titles have two things in common: they are some of the most attractive professions at the moment, and they all imply dealing with Data.

– The demand –

These are roles whose demand has greatly increased in the recent years, and most likely will only continue to do so, like shown in the following figure.

Increase in the demand for Data Scientists in the recent years. Source topSource bottom.

With such a high demand for these kinds of players, people with enough knowledge in one of these fields can find job opportunities across a wide number of industries.

Interested in the energy market? Every known company in the sector is looking for data-driven profiles to improve their decision making and process efficiency.

Enjoy consultancy? All the big firms (e.g. Mckinsey, Bain, BCG) are recruiting Data Scientists and Data Engineers to help their clients make use of their data.

Would like to work in a tech company? Google, Amazon, Uber, Facebook, are all seeking more and more Data Wizards.

– The salaries –

According to Glassdoor, the average salary for a Data Scientist in the USA is $117,345/yr, way above the national average, and like shown in the following figure, other related positions are not very far off.

Average salaries for different job tittles in the USA. Source.

– The beauty –

Aside from all of this, these jobs are in charge of implementing and building technologies that are revolutionizing whole industries, at the core of incredible emerging ones like Autonomous Vehicles or Advanced Image Recognition tools, and these roles will be the key drivers in many upcoming advances in Industry and Academia.

The Market for Artificial Intelligence is one of the markets with the highest expected evolution, projected to hit $36 Billion by 2025, like shown in the following figure.

Artificial Intelligence Revenue, World Markets. Source.

All of these advances are expected to be highly beneficial for our societies, so having the chance to work in any of these fields means being able to make a lot of impact on the world and our quality of life.There are so many things that can and will be done with Artificial Intelligence, and those involved will be steering the wheel that shapes our future.


In conclusion, Data Science, Machine Learning and the others, are very promising fields that are exciting, fun , and have endless applications.

Despite there being a lot of practitioners, there is a shortage of qualified professionals in these areas. If you become one more name in that list of professionals, you can only expect quality and challenging jobs.

All these fields have a very high and growing demand, and the compensations are top-notch.

Okay, but why is this all happening now?

Machine Learning is not a new field. Its origins dates back to the previous century. Data Scientists have also existed for a while under different names.

So why now? Why has there been such a claim for these kinds of skills in the recent years? There are two main reasons:

First, the amazing increase in the amount of data that is being generated and consumed. Everyday more and more sensors gather all kinds of data, and we, walking around with our smartphones all day long are huge sources of data too. The continuous growth of the internet has also contributed a lot.

Expected growth of the amount of existing data in the world. Source.

There is a lot more data being generated than there was ever before. All this data is pretty useless if its not analysed or taken advantage of to provide value in businesses or organisations. Its correct treatment allows for better decision-making, process automation, insight discovery, and a lot more.

Because of this, the need for profiles that can make good use of this data, and leverage it to its maximum potential has grown dramatically in the recent years.

Secondly, the increase in the available computing capabilities has made it possible for us to build systems that can efficiently crunch all this data to obtain results in a reasonable amount of time.

Moore´s Law graph for Density of devices 1970–2020. Source.

Cloud computing, platforms like AWS, Cloudera, Microsoft Azure and many more, which allow us to deploy and build intelligent solutions on amazingly big clusters and machines from anywhere in the world, have also greatly contributed to the practical improvement of the feasibility of Artificial Intelligence Systems.

Can I really learn enough to build a career?

Of course you can. There are millions of resources — on and offline — where you can learn any of the building blocks that you might need to develop a career in one of these fields.

Don’t know how to program? Don’t worry, there are infinite places where you can learn, and to be honest, you don’t need to be a programming master in order to implement and build Machine Learning models.

Don’t know math or algebra? That’s fine! With some basic algebraic notions you are fit enough to survive, however a solid mathematical background can be helpful. Again, if you wish to learn there are tons of amazing text books and resources out there. Knowledge of probability is also of great use and it is also not hard to obtain a background solid enough to defend yourself.

Don’t know anything about Data Science or Machine Learning? Are babies born walking? As everything, learning about these fields is a process, which you can do by yourself buying books, doing online courses and programs and become a self-taught Data Scientists, or you can enroll an actual bachelor or master’s program.

Every day there are more and more official programs being offered, and the quantity of resources is almost unlimited. With so much material out there, it can sometimes be difficult to differentiate good from bad resources, so all of this has to be done with care.

The goal of this post is not to list the good resources or to advise anyone on how to learn, however, if you’re interested, feel completely free to contact me at the information left at the end of the article, and I will do my best to guide you.

Closing words

As always, I hope you enjoyed the post, and that I have convinced you to at least consider learning about Data Science or Machine Learning.

If you liked this post then feel free to follow me on Twitter at @jaimezorno. Also, you can take a look at my other posts on Data Science and Machine Learning here. Have a good read!

If you want to learn more about Machine Learning and Artificial Intelligence follow me on Medium, and stay tuned for my next posts!

Until then, take care, and enjoy AI!

Data Scientist (noun): someone who does precision guesswork based on unreliable data provided by those of questionable knowledge. See also: Wizard, Magician.