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How to Become a Data Scientist

What is Data Scientist?

Ready to start your career in Data? Find out more about BrainStation's Data Science Bootcamp

What Is a Data Scientist?

A Data Scientist is a data expert with the analytical and technical skills to solve complex problems. A Data Scientist's role involves using computer science, mathematics, and statistics to find patterns in data and develop actionable strategies for organizations.

Data Scientists spend a lot of time collecting, organizing, modeling, and examining data from various angles, including some that have not been looked at before. If it sounds like data science offers no singular road map from problem to solution, that’s because it doesn’t. As Biostatistics Professor Jeff Leek explains, “The keyword in ‘data science’ isn’t ‘data’; it’s ‘science’”—which is to say, by definition, data science is an exploratory field.

Because Data Scientists tend to have one foot in each of the business and IT worlds, they’re highly sought-after and highly compensated. Most companies have recently realized the tremendous power and value of data science, and that they can no longer afford to ignore the mass of unstructured data they have at their fingertips on their users and customers. But someone needs to mine that unruly mess for gold – and that’s where a Data Scientist comes in.

Given the surging demand for data science due to the sudden prominence of big data, the vast majority of Data Scientists did not begin their careers in the field. Many start off as Data Analysts or Statisticians before transitioning over. In fact, the 2020 BrainStation Digital Skills Survey found that 76 percent of respondents did not begin their career in data, and 68 percent have been working for five years or less.

That’s because data science, in its current form, is a relatively new field.

History of Data Science

In fact, the history of modern data science and overall interest in big data really picked up in the mid-90s, when Business Week published a cover story on "database marketing," noting that companies were collecting large amounts of data about their customers and using it to predict how likely they would be to buy a product and to craft a marketing message that would make you more likely to do so.

Two years later, members of the International Federation of Classification Societies met for their biennial meeting, and for the very first time, "data science" was included in the title of the conference ("Data science, classification and related methods.") The same year, an influential paper titled "From Data Mining to Knowledge Discovery in Databases" was published, and the following year the journal Data Mining and Knowledge Discovery was launched. Also in 1997, C.F. Jeff Wu delivered an inaugural lecture for the H. C. Carver Chair in Statistics at the University of Michigan in which he called for statistics to be renamed data science and statisticians to be renamed Data Scientists.

In 2002, the Data Science Journal launched, followed by the Journal of Data Science the next year. And 2007 saw the establishment of the Research Center for Dataology and Data Science in Shanghai.

Still, those who weren’t plugged into data science trends might have been taken aback when, in 2009, Google Chief Economist Hal Varian told the McKinsey Quarterly that “the sexy job in the next 10 years will be statisticians.” Time has proven him right. You’d be hard-pressed to find a successful company that isn’t pouring money into finding creative and efficient ways to harness the power of big data, and Data Scientists are at the core of that.

How to Be a Data Scientist

The pathways to becoming a Data Scientist have changed over the years, too. Originally, the Data Scientist role had academic origins, and still many Data Scientists are highly educated. In fact, 88 percent have at least a Master’s degree, and 46 percent have a Ph.D. Often, a data science career begins with a Bachelor’s degree in computer science, mathematics, or statistics

There are also university programs that have popped up specifically relating to data science, like the Institute for Advanced Analytics at North Carolina State University. And where hopeful Data Scientists used to be largely relegated to studying computer science, there are now many universities finally offering a Data Science degree. Syracuse University, UC Berkeley, John Hopkins University, Columbia University, and the University of Michigan are among the top-notch schools that now offer a master’s degree in data science.

Still, that lofty level of education – or a data science degree – is not a hard requirement to learn data science. A polished portfolio and a resume showcasing some serious technical skills might be enough to land an entry-level job in data science.

There are also a growing number of bootcamps and certificate courses teaching data science and analytics skills. For career changers who want to be immersed in a promising new field in a hurry – or for those who simply don’t have the time or money to pursue years of university – this can be an appealing option. These focused and immersive programs promise to give graduates all the technical skills they need plus an array of career services to ensure their alumni comprise the next generation of data science stars. This is also an effective method of ensuring your portfolio shines since you’ll have industry veterans watching your back.

Characteristics of a Successful Data Scientist

Regardless of their educational background, good Data Scientists share a number of characteristics.

First of all, there’s no getting around the fact that there are certain skills that you need to acquire to become a Data Scientist. Every Data Scientist should know his or her way around Python, R, SQL, Hadoop, and Spark. All Data Scientists should also be well-versed in the following: data analytics, data collecting, data analysis, statistical analysis, data visualizations and reporting technologies, databases such as Postgres and MySQL, predictive analytics, machine learning, artificial intelligence, and programming.

And yes, the Data Scientist role certainly involves numbers. They do work with large amounts of data and data sets. Data Scientists decide which data is needed, clean the data, build models of what the data can show, and organize it to reveal latent insights. All of this is done to serve a larger business goal.

Although Data Scientists do work with numbers, it’s important to note that data sets could also be composed of text, structured data, images, video, audio, and graphics. The type of data a Data Scientist uses in his or her day-to-day work life will largely depend on the industry. But from a high level, a Data Scientist’s job is to take that data – in whatever form it may come – and ultimately leverage it to help leaders make smarter decisions. That could take the form of making improvements to a product or service, finding more efficient workflows, unearthing new market insights, or improving the experience of their customers.

Data Science Soft Skills

A good Data Scientist will also be adept at visualizing and presenting data – an area that combines technical skills and soft skills. Converting data from tables into charts, graphs and dashboards can be accomplished with a number of tools including Tableau, Plotly, Bokeh, and Matplotlib, and becoming proficient with these tools meets the technical side of data visualization.

The soft skills involved with data science come in when you try to determine what type of visualization will most effectively communicate your findings, as well as having the esthetic taste to know how to present your findings in a way that will make the biggest and most persuasive impact on the decision-makers who are being presented with the information.

In addition to that, great Data Scientists need a wide spectrum of soft skills to excel in their roles. For one thing, it’s important to understand business. You could have all the technical skills in the world but if you don’t understand business principles or your company’s goals, those skills won’t be utilized in a productive, efficient way.

Communication skills are also crucial as you present your findings, present your case for the implementation of changes related to those findings, and try to have a voice within your company’s overall organizational strategy.

According to BrainStatoin's survey, 83 percent of data professionals ranked the overall data literacy as intermediate or low and 89 percent said that this shortcoming was impacting the success of their organization's projects. Further, 59 percent said their company would be more successful if their employees had data skills.

This illustrates two things: data professionals are in high demand and in this field, you will be working with people who likely won’t understand much about data, and finding ways to deliver your message in a compelling, accessible way will be crucial to your success.

A Data Scientist also needs to be a good team player. You’ll be dealing with large, multi-disciplinary teams and an effective Data Scientist can’t simply work in isolation on projects of their choice. You’ll have to coordinate and collaborate with an array of people in other technical and non-technical roles.

Programming Languages For Data Science

Python, R, SQL, and Java are some of the most popular programming languages Data Scientists use.

Python

Accessible, easy-to-use, and versatile, Python is the top programming language for many Data Scientists.

R

R offers a range of domain-specific packages to meet the statistical and data visualization application Data Scientists might need.

SQL

SQL, or “Structured Query Language,” is a domain-specific language used for managing data in relational databases.

Java

Part of Java's usefulness is in its popularity: many companies used Java to create backend systems and applications for desktop, mobile, and web. Knowing how to use Java allows you to weave data science production code directly into an existing database.

Scala

User-friendly and flexible, Scala is the ideal programming language for dealing with great volumes of data.

Julia

Julia is designed for numerical analysis and computational science, and useful for solving complex mathematical operations.

MATLAB

MATLAB is used in industry and academia thanks to its intensive mathematical functionality.

Click here to find out more about what programming languages Data Scientists use.

Jobs in Data Science

Understanding the distinction between the various data and data-adjacent roles that you might find on a typical data team can be difficult. Here are a few job titles in data science and how they differ:

  • Data Scientist. Unlike Data Analysts, Data Scientists must understand the challenges facing a business and offer the best solutions using data analysis and data processing. Data Scientists are expected to perform predictive analyses and sift through unstructured data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies make better decisions.

  • Data Analyst. A Data Analyst is responsible for visualization, munging, and processing massive amounts of data. They occasionally also have to perform queries on the databases. One of the most important tools in a Data Analyst’s toolkit is optimization, where they create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

  • Data Engineers. Data Engineers build and test scalable big data ecosystems so that Data Scientists have stable and optimized data systems on which to run their algorithms. Data Engineers are also tasked with updating the existing systems with upgraded versions of the current technologies to improve database efficiency.

  • Database Administrator. Database Administrators are responsible for the proper functioning of a company’s databases. Further, they can grant or revoke its services to the employees of the company depending on their requirements. They also tend to database backups and recoveries.

  • Machine Learning Engineer. An in-demand role, Machine Learning Engineers perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification and clustering. They must have in-depth knowledge of powerful technologies including SQL and REST APIs.

  • Data Architect. To ensure databases are secure and centralized, Data Architect creates the data architecture blueprints for information management. They also ensure that the Data Engineers have the best tools and systems to work with.

  • Artificial Intelligence Engineer. Artificial Intelligence Engineers work with traditional machine learning techniques like natural language processing and neural networks to build models that power AI-based applications.

  • Computer Scientist. Computer science is the study of how computers can be used to solve a wide range of problems. To put it very simply, computer science integrates topics including math, physics, engineering, and design to look at how to use computers to transmit and transform information. Computer Scientists use technology to solve problems and prepare for the future, they write and program software to create applications, and they validate and develop models for interaction between people and various devices.

In addition to the people performing all the roles above, Data Scientists usually collaborate with a wide variety of stakeholders throughout an organization, including everyone from marketing to sales to IT to senior management, especially at the level of a Senior Data Scientist. Typically, however, the immediate data team is a smaller group; BrainStation’s survey found that a majority of data professionals are on small teams, with 37 percent on teams of less than five, and 26 percent on teams of five to 10 people.

Data Science Demand

Getting to collaborate with a number of different people from different backgrounds – rather than being stuck in a tech silo – is one of many reasons so many people are looking into becoming a Data Scientist.

Another is job security. Since 2012, Data Scientist roles have increased by 650 percent, and the U.S. Bureau of Labor Statistics predicts that the demand for data science skills will increase by another 28 percent by 2026. A report from McKinsey predicts a shortage of between 140,000 and 190,000 people with analytical skills in the U.S., with another 1.5 million managers and analysts who will need to upskill to better understand how to make data-driven decisions.

There are other reasons big data professionals are so content and so optimistic about their future career prospects. With an average base salary of $120,000-plus (according to Indeed) plus the usual buffet of appealing perks including commuter assistance, stock options, and gym memberships, Data Scientists are well-compensated and treated well by employers who know they have plenty of options if they want to look on the job market.

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