How to Become a Data Scientist in 2019
and Beyond (Roadmap)

A data scientist uses skills in math, statistics, and computer programming to gather and analyze data and gain insights from it. If you’re wondering how to become a data scientist, you’re in the right place.

Data scientists are some of the most sought-after job candidates in today’s tech world. This is because every business is relying on data to help them find and understand the best ways to improve their organization, retain their customers, and generate more revenue. 

They want to see how they can enhance their operations and fine-tune their strategies, content, products, and services. They want to learn more about the behavior of their customers so that they can figure out ways to attract and retain them.

The key to all this lies in big data. It lies in gaining insight from all the gathered data, but there’s often too much information for one person to process and analyze. This is precisely why businesses in all industries are increasingly relying on data scientists.

Take a look at the essential steps to becoming one, along with the skills and qualifications you must have.

General Steps to Becoming a Data Scientist

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Enroll in a Data Science Course

Before enrolling in a college program, you should explore a data science course to determine whether or not this career choice is the right fit for you. There are plenty of free and affordable online courses you can take to ensure you make a smart decision later.

Moreover, this path will help you get a great head start if you do decide to earn a data science degree. It will help you learn all the basics so that you can have the right skillset once you set foot on your chosen university campus. It will help you prepare for the field and improve your learning rate and knowledge retention in college.

Obtain an Undergraduate Degree

This is the first step that most people take to become data scientist professionals. While academic data scientist education is not absolutely necessary for the job, it definitely does help.

You can choose to learn various programming languages on your own or seek help from an abundance of online courses. However, an undergraduate degree in computer science, IT, statistics, math, data science, or a related field will perfectly prepare you for various entry-level data science jobs.

Network and Seek Internship Opportunities

Even before you get a degree, you should start networking. Search for various connections in the data science world, so that you can dip your toes in it and gain a better perspective of your future career. Be sure to ask your college professors too for valuable advice for your future job, and look for internships where you can start building your career.

Gain Work Experience

An undergraduate degree will open a lot of doors for landing a job in the field. Even if you decide to get a high-level degree afterward, you can still find an entry-level job and start gaining real-world experience.

What Is Data Science?

Data science is a field that uses computer and math algorithms, scientific methods, and tech systems to gather, process, and analyze structured and unstructured data for the purpose of gaining knowledge and insights from it.

In a nutshell, it’s the study of data. It involves extracting valuable information from large sets of data in order to find patterns and trends that will help better understand that data.

For instance, if a business wants to retain more customers, it may want to look into the reasons why some customers are switching to their competition. A data scientist can create an algorithm to explore a number of data points and conduct various data analyses to see the patterns in consumer behavior.

They can visualize the data and help make data-driven decisions for improving various operations and retaining customers. They can even use predictive analytics to anticipate future events or demands and help devise cost-effective plans and strategies.

Amazon is one great example of data science in action. Thanks to various algorithms that look at consumers’ purchasing history, the platform offers relevant shopping recommendations.

Netflix is another good example, as its data algorithm helps users get content suggestions based on their viewing history.

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5 Must-Have Skills to Become a Data Scientist

A big data scientist must have all the following technical and non-technical skills to be truly successful at their job. They are all crucial data science requirements for a successful career.

Programming Skills

One of the most important data scientist requirements has to do with programming languages. You must be highly proficient in various programming languages, such as R, Python, SQL, MATLAB, SAS, Java, Julia, Scala, and C++. You should also have excellent knowledge of Apache Hadoop, Apache Spark, and NoSQL.

Machine Learning

Becoming proficient in machine learning is also becoming a must-have for data scientists these days. Machine learning techniques will help you with various predictive analyses.

You’ll be able to develop data-driven models for efficiently processing data in real-time, and getting accurate results. You’ll be able to help businesses solve different challenges that can improve their organizational outcomes, all thanks to predictive analytics based on past knowledge you extract from data.

Machine-Learning

Data Visualization and Reporting

There’s a huge amount of data in today’s high-tech world. The ability to visualize that data is crucial for making sound business decisions.

If you look at raw data, you can’t really see the meaning in it. But if you can visualize it, you can turn the numbers into something meaningful that has real value.

This is why data visualization and reporting skills are also some of the most important data science prerequisites.

No matter how complex particular data sets are, you need to be able to convert them into a format that’s easy to comprehend. Only then will you be able to help a business understand what to do to seize business opportunities.

Data-Visualization-and-Reporting

Communication Skills

Communication skills may not carry the same amount of weight as the aforementioned technical skills, but they are also incredibly essential.

You need to be able to clearly and thoroughly explain all your analytical findings to people who may not be very tech-savvy, if at all. You must be able to translate your data analyses into a language that your co-workers will understand.

They will rely on you to make data-driven decisions, so you need to be able to help them do just that. For instance, when you present a data table or chart, you need to know how to communicate the insights from those formats clearly. Don’t tell your co-workers what and how you analyzed, but rather how your results will help them improve business outcomes. That’s how you deliver real value.

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Statistics and Math

Advanced knowledge of statistics and math, especially calculus, linear algebra, and probability, are also extremely important for data science. They’re both vital for creating and understanding all machine learning algorithms.

Without these disciplines, you can’t analyze data properly and accurately, nor identify and predict trends from it. And without statistics, you simply can’t find any insight when looking at data.

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Educational Qualifications for a Data Scientist

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Data scientist education has two academic paths that you can take. Both will provide you with the necessary data scientist qualification for landing a profitable career in this in-demand field.

Earn a Bachelor’s Degree in Data Science or Related Field

As already discussed, you can learn data science through various courses and textbooks, but earning a degree will help you launch a lucrative career in the field.

First of all, you’ll get all the necessary skills for analyzing complex data. You’ll learn more than you ever could on your own. Secondly, you’ll get many more business opportunities, because you’ll stand out from people with no data science degree.

To get a proper qualification for a data scientist career, you can study computer science, statistics, math, applied math, social science, economics, and physics. Obviously, you can also study data science, if available.

Earn a Master’s Degree or Ph.D. in Data Science or Related Field

A lot of data scientists get highly-paid jobs with a Ph.D. degree, but almost all of them hold a master’s degree in data science or related field. Given the fact that data scientists are in exceptionally high demand, you should seriously consider obtaining a high-level degree.

If you do, you’ll have much greater career opportunities, not to mention that you’ll acquire more advanced skills that will make you stand out from other professionals with lower-level degrees.

Popular Specializations in Data Science

In data science, there’s no shortage of opportunities when it comes to the areas you can specialize in. The most popular areas of expertise in the field include:

Data mining
Data engineering
Database management and architecture
Machine learning
Cloud and system architecture
Data visualization
Business intelligence
Market-related data analytics (marketing, sales, pricing, web, etc.)
Sector-specific data analytics (automotive, insurance, healthcare, finance, DoD, etc.).

Data Science: Online vs. Offline

The differences between learning data science online and learning it offline are pretty straightforward. Read on to explore them and find out the pros and cons of each.

Learning Data Science Online

The popular ways of learning data science online include engaging in online courses (edX, Udemy, Coursera, Metis, Harvard, Codeacademy), participating in boot camps, joining LinkedIn groups, and finding other relevant resources to further your learning or study the field on your own.

CONS

  • Lack of hands-on instruction
  • Inability to gain complex skills that college programs provide

PROS

  • Learning at your own pace
  • Engaging in interactive learning
  • Saving money
  • Networking

Learning Data Science Offline

Learning data science offline can mean trying to master all the basics, and later more complex stuff, completely on your own. Naturally, it can also mean enrolling in a college and gaining all the necessary skills from experts.

PROS

  • Structured educational program
  • Gaining a mixture of valuable skills
  • Learning from experts in the field
  • Hands-on instruction
  • Valuable guidance
  • Collaborative environment
  • More business opportunities
  • Career growth
  • Working with the latest data science tools

CONS

  • Less cost-effective than online learning

Average Salary of a Data Scientist

According to Glassdoor, the average salary of a data scientist in the US is $120931. This is without cash compensations that usually hit the mark of $11772.

These are the top countries in the US with the highest average salary of a data scientist.

 

CITY SALARY
California $128530
Illinois $121310
Colorado $116400
Arizona $109530
Florida $105560

Here are the top five countries in the world with the highest average salary of a data scientist.

 

COUNTRY SALARY
USA $120000
Australia $110719
Israel $87500
Canada $81339
Germany $80120

Why Should You Become a Data Scientist?

Becoming a data scientist is an excellent career choice because data science is in high demand. The data science job outlook looks very bright, as there are huge job opportunities, and there isn’t even a large competition to worry about.

Moreover, salaries are exceptional. Jobs in data science are some of the highest-paid jobs in the world.

Furthermore, the field is ever-evolving, so there’s always room for learning something new and adding more value to a business. It’s a very interesting job role that even enables you to specialize in many areas of expertise and dip your toes in a wide range of different domains.

Conclusion

Hopefully, this guide has shed some light on how to become a data scientist and enter the exciting world of data science.

Being a data scientist can be incredibly interesting and lucrative, but it does require a lot of time to master the skills. Therefore, follow all these tips on how to be a data scientist, and you’ll certainly land an excellent job and build a successful career.

Can I become a data scientist without a degree?

Yes, you can, but you may not acquire as advanced skills as you would while earning a degree. Also, companies are usually looking for job candidates that hold a degree.

How much money can you make as a data scientist?

If you get a job in the US, you can make an average of about $120000. Other countries in the world also offer high salaries, but they’re somewhat lower than in the US.

How long does it take to become data scientists?

It takes about 4-5 years to become a good data scientist, but you may decide to further your education and truly master your skills.

What is the difference between a data analyst and a data scientist?

A data analyst extracts meaning and value from data, while a data scientist involves predictive analytics as well, among many other things.

What is the simplest way to become a data scientist?

The simplest way is to engage in an online course and learn at your own pace, but that may not be as beneficial as earning a college degree.