Data Science is one of the hottest topics in tech these days. And for good reason. Data can lead to insights that otherwise could never have been discovered. It also allows for decision making based on facts and metrics rather than based on gut feeling.

It’s therefore no wonder that data has been called the new gold. However, data is often scattered among multiple databases. And merely bringing lots of data together is getting you nowhere, it will not magically create any business value.

Actually, we would rather compare data to a newly discovered gold mine, rather than already excavated gold. You might be absolutely certain that there’s gold to be found in the mine. Yet without putting in the work with the right tools, equipment and people, you’ll never see any it.

The same goes for data. Without the right tools and people to dig through all of it, there’s no point in collecting it. And this is where Data Scientists come into the picture. They’re the ones responsible for running the models that sift through the dirt in search for your golden data nuggets in the data. So, you could say that Data Scientists are the gold miners of the 21st century.

Eager to take up the role of a data scientist yourself? In this article you can learn more about the following topics:

1. What is Data Science? And what does a Data Scientist do?
2. Why become a Data Scientist?
3. What skills do you need to be a Data Scientist?
4. What education you need to be a Data Scientist?
5. Can anyone learn Data Science?
6. How to get your first Data Science job?

1. What is Data Science?

You now understand the importance of having the right tools and talent to extract value from large amounts of data. But who are these people called 'Data Scientists' and what is 'Data Science' exactly?

As the term hasn’t been around for long, definitions vary slightly. Let’s take a look at a couple of them:

Investopedia describes Data Science as follows: “Data science provides meaningful information based on large amounts of complex data or big data. Data science, or data-driven science, combines different fields of work in statistics and computation to interpret data for decision-making purposes.

According to Wikipedia, “Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.”

Techopedia defines the term as “Data Science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data.”

In essence, every single one of them comes down to the same thing:

"Data Science is all about bringing large sets of data together and analyzing the data with the goal of getting insights from it."

Sure, analyzing data is something companies or researchers have been doing for a while. But the real breakthrough only happened recently, as organisations started to endure digital transformations.

Organisation effortlessly digitized their business processes. Shops, for instance, opened similar e-commerce stores, creating tons of digital traces. They enriched this data with customer movement data through sensors, customer-centric journeys, etc. In other words, they succeeded in generating huge amounts of data.

Luckily, storing data had become utterly cheap with the advent of cloud storage solutions. Throw in the effect of the 'law of Moore', which sees computational power growing exponentially with the rise of reasonably priced & easy-accessible cloud computing power, and complete it with significant breakthroughs in Machine Learning, Deep Learning and Artificial Intelligence research.

Combining these four aspects, big data - cheap storage - fast computing - modern mathematics, enabled data science to become such an important field.

There is clearly a need for people who know how to extract knowledge and insights from that data. Because without experts who can find patterns in the data, no true insights could be generated as the data itself is meaningless, no matter how big it is.

The people who are responsible for choosing or creating the right model, bringing it into production and analyzing the results, i.e., the people extracting value from the data, are called Data Scientists.

And what does a Data Scientist do?

Data Scientists work with data. But the job shouldn’t be confused with other positions in the industry that also involve working with data: Data Analysts and Data Engineers.

Since these are different jobs, their job descriptions also differ. And although the distinction isn’t always clear, in general, this is what each one of the jobs comes down to:

Data Analysts

Data Analysts typically focus on gathering, processing and obtaining statistical information out of existing datasets. They thoroughly understand their business practice and are looking to guide decision-making with specific insights.

That is why they provide reports and visualizations to their management, explaining which insights the data has revealed.

They lack, however, a knack for automatization through scripting in both collecting and analyzing.

Data Scientists

The job of Data Scientists is a bit more technical. Data Scientists also handle the statistical and mathematical models that are applied to the data, but add extra steps of automatization in the used scientific methods.

This results in a more independent data analysis that is steered by Computer Science and Machine Learning to make predictions and solve key business questions.

Data Engineers

The third job role that is often encountered is that of a Data Engineer. They're the computer engineers who craft data pipelines. They sketch, shape and fortify the required infrastructure for analysis.

They are focused on the production readiness of data, enabling Data Scientists to work their magic.

To continue our previous analogy, they are the prospectors who discover the mine, so the Data Scientists can start digging.

2. Why become a Data Scientist?

Becoming a Data Scientist is an excellent choice. As mentioned above, Data Scientists create a massive amount of value since they’re the ones responsible for finding the gold.

Creating value in a company is not the only reason, read more on why you should become a Data Scientist right now. Here are some of the reasons:

1. Data Scientists are in high demand but in short supply.

There’s so much eagerness to shift businesses towards data-driven decision-making, that stating that the demand for skilled Data Scientists is booming is an understatement.

Yet the number of graduates in Data Science and Analytics Masters is too small to close the gap between supply and demand. Due to the fact that Data Scientists with the right know-how are scarce, they can place themselves very favourable in any wage negotiation.

2. Working as a Data Scientist is gratifying.

Money isn’t the only reason why a career in Data Science is attractive. The field of Data Science is rapidly evolving.

As a Data Scientist, you get to work in an interesting environment where new ideas are supported and encouraged.

You will perform a wide variety of daily tasks that test your knowledge in math & computer sciences and appeal to your talent for creative and resourceful thinking.

Your career will be a journey in uncharted territory.

3. Be ensured of a prosperous future.

The good prospects of the job are also being acknowledged by LinkedIn. That’s why it put Data Scientist on top of its list of most promising jobs of 2019.

If you’re still not convinced of the advantages of becoming a Data Scientist, the fact that Harvard Business Review has called it ‘the sexiest job of the 21st century’ might persuade you. Because who wouldn’t want to have the sexiest job of the 21st century, right?

3. What skills do you need to be a Data Scientist?

A Data Scientist’s job requires a diverse mixture of skills. And the fact that Data Scientists possess various competences makes the job exciting. There are five main skills you need to master in order to qualify:

1. Have the right programming skills

In order to become a first-class Data Scientist, you need advanced coding skills to efficiently extract, clean, analyze, and visualize data. Python and R are the dominant programming languages used in Data Science.

Especially Python is gaining importance and recently became the most common programming language applied by Data Scientists. Other popular languages used by Data Science are SQL, MATLAB, Java and C/C++.

Don’t know where to start your coding journey?
Hackernoon made a top 10 of free online Python courses for beginners. But before starting a course, reading Learn to Code With Me’s beginner’s guide to Python for Data Science might be helpful.

2. Have an affinity with Statistics

Remember the main aim of turning data into knowledge? As that is what we’re after, quantitative skills are very important. Without familiarity with the right descriptive and inferential Statistics, translating all that data into insights is difficult.

Josh Wills, the Director of Data Engineering at Slack puts it this way:

“A Data Scientist is a person who is better at Statistics than any Software Engineer, and better at software engineering than any Statistician.”

Here is a link to an article with resources that you can use to brush up on your Statistics.

3. Be familiar with the most modern Machine Learning algorithms

The Data Science industry is thriving but also evolving. A modern Data Scientist who doesn’t know Machine Learning is nothing more than an old-school statistical analyst.

However, merely understanding the use of Machine Learning isn’t enough. If you want to stand out as a Data Scientist, your knowledge of the most modern Machine Learning algorithms has to be up to date.

Since the industry will keep evolving, having the willingness to keep educating yourself is a must.

This guide by EliteDataScience is an interesting read to get started with Machine Learning.

4. Be interested in the data you’ll be working with. See beyond the numbers and understand the business value behind them.

Businesses in different industries have different goals with their data. A retailer, for example, is looking for totally different insights than someone who deals with medical data. You need to be aware of the types of issues the company you wish to work for, wants to tackle driven by data.

An affinity with the medical world or an eagerness to get more familiar with the industry is a good idea if you want to be a data scientist in that field. This is important as you’ll have to understand the questions that, for example, doctors might have.

You’ll also have to be able to report your results to them. But surely that doesn’t mean you have to become a physician yourself.

A general interest in the sector, an open mind and ability to quickly discover new fields are key.

5. Know how to tell a story with data

As a Data Scientist, it’s important that you’re able to clearly explain your fascinating insights to relevant people. It’s likely that they did not acquire an advanced Data Science vocabulary. But you still need to find a way to convince them.

Even though being capable of tailoring your message to a non-Data Scientist audience is not a technical skill, it might be the most important one!

You have to make sure that people don’t feel overwhelmed by too many details. The ultimate goal of your work is to let your organization benefit from your work to the fullest.

4. What education do you need to become a Data Scientist?

Most Data Scientists have master’s degrees or even PhDs in the fields of Computer Science, Statistics, Mathematics, Engineering or Economics. And these days, universities are even starting to offer Data Science degrees. Ghent University, for example, has a master’s program in Business Engineering with Data Analytics as the main subject.

Just because most Data Scientists have degrees in the fields above doesn’t mean that you can’t become a Data Scientist without such a degree. You can even learn Data Science by yourself without going to college or university.

You have two options to learn Data Science on your own. You can take online courses or you can subscribe to in-person offline bootcamps. Both online and offline, you can find short and longer courses and most of them offer certifications. They do, however, have different pros and cons.

1. Become a Data Scientist through online courses

Online courses allow you to learn whenever and wherever you want. This self-paced way of education is one that comes with a lot of freedom.

This does require a commitment to follow through with the course. And it might be harder to know where to start because of the abundance in the number of bootcamps.

KDnuggets has made a list of the best online Data Science courses to help you choose.

2. Become a Data Scientist through offline training

Offline training has different advantages. A set time and place might limit your freedom but it makes it impossible to procrastinate. Consequently, the chances of you sticking to the course are much higher.

Another plus is the approachability of your teachers. In-person education also makes elaborating on a question or tailoring the answer to the specific course audience possible.

Add to that the factor that you can collaborate with fellow students and even use it as an opportunity to network.

These courses also tend to be more adaptive.

At Crunch Analytics, we offer in-person Data Science bootcamps aimed at different profiles. Have a look at whether they fit your profile.

5. Can anyone learn Data Science?

It’s probably a bit presumptuous to say that anyone can learn Data Science. Keep in mind the most important skills that we mentioned earlier.

If you’re new to Data Science, the skill that probably frightens you most is programming. It shouldn’t scare you, but maybe it’s just because it’s unknown territory. There’s no need for it to be intimidating. It’s true that it takes time to learn, but if you’re willing to invest time into it, and if you stick to it, it’s possible.

Acquiring the right statistical and mathematical knowledge is another important aspect of learning Data Science. Just as with programming and learning the likes of Machine Learning algorithms, this sounds somewhat more geeky. But that's a good thing!

Think of it as there being two roads towards Data Science.

1. One implies that you have a good understanding of statistics and business intelligence and you just need to learn how to program, so you can ask your computer how to do the hard work for you.

2. Or you already know how to program, but need to upgrade your statistics and business understanding skills.

Learning Data Science might not be for all. But if you have a bit of aptitude for Science and Mathematics and you truly commit to it, a lot is possible.

6. How to land your first Data Science job

1. Start your own small Data Science project to build up experience

When you’ve acquired the necessary Data Science skills, it’s always a good idea to keep practising. You can start your own small Data Science projects on sites like Kaggle. Aside from providing you with the ability to keep exercising, they’re definitely an asset to your resume.

Another action you can take to expand your experience is freelancing through sites like UpWork or answering questions on Stack Overflow.

2. Apply for a Data Science internship

While these are great ways to get some experience, the best way to practice and improve your skills is by taking up an internship. During an internship, you get to experience working in a real-world setting without people expecting you to know everything there is to know. It’s an environment which is great for learning.

Data Science Weekly wrote a guide that might help you in your search for a Data Science internship that fits you.

3. Participate in appropriate hackathons or business games

4. Apply for companies with a mentor-first mentality

Once you’ve gained some experience, you can start looking for a job. Just like with all jobs, you have to think about the type of company you want to work for. It’s always a good idea to look for an environment where you can keep learning and where there is someone who can mentor you.

Furthermore, ask yourself if you want to be a trusted advisor that feels comfortable sharing his or her expertise in a variety of settings. If you also wish to flex your communication muscles, then the environment of a consultancy firm that helps other companies with the analysis of their data, might be exactly right for you.

Or you maybe want to work for a large & established company and help them with their data.

Both types of companies are in need of skilled Data Scientists and can be attractive places to work. But, it’s up to you to decide which option seems most interesting to you.

5. Be genuinely interested in the companies data

Think good about the sector you want to work in. As we mentioned before, being interested in, being able to communicate about and understanding the data your work with is a vital part of the job.

Don’t apply for a job in an industry that does not fascinate you. For you to be able to work with medical data, you don’t have to become a doctor yourself. General enthusiasm for the sector is all you need.

And if at first, you don't succeed?

Well, get yourself up and try again!

Given the massive amounts of data produced on a daily basis, no matter where you end up as a Data Scientist, it’s a career with a bright outlook on the future.

In short, these are the 5 steps you need to take to become a Data Scientist:

1. Brush up on your statistics.

Without statistics, you can’t analyze patterns in data. Here is a link to an article with resources that you can use to brush up on your Statistics.

2. Learn to code.

Coding is used in Data Science to extract, clean, analyze and visualize data. Python is the most commonly used programming language for Data Science these days and it is one of the easiest to learn for non-programmers. Hackernoon made a top 10 of free online Python courses for beginners. But before starting a course, reading Learn to Code With Me’s beginner’s guide to Python for Data Science might be helpful.

3. Follow a Machine Learning or Data Science course.

There are a lot of Machine Learning and Data Science bootcamps both online and offline. They combine your statistics of point 1 with your coding skill of point 2.

4. Join the Data Science community.

Medium is packed with articles on Data Science. Other sites that are worth following are KDnuggets and Analytics Vidhya. Reddit, Quora and Stack Overflow are great if you have questions or want to learn from other people’s questions.

Also, don’t forget that the Data Science community extends beyond the online world. All around the world, people who are active in the industry come together. So, look for 'meetups' in your region.

5. Get some experience or practice.

Start your own small projects on sites like Kaggle, or maybe start freelancing through sites like UpWork.

While those are great, the most valuable way to get experience is doing an internship. During an internship, you get to experience working in a real world setting without people expecting you to know everything there is to know. It’s an environment which is great for learning.