How Do You Become a Data Scientist?

Data science no longer has the reputation it once had. Once named the "sexiest job of the 21st century" by Harvard Business Review, data science, and data science fields, now represent one of the fastest-growing and most profitable career paths. When thinking about what it takes to become a data scientist, it can be tough to unpack the types of complex analytical problems that data scientists solve every day. By trade, a data scientist cleans and interprets massive amounts of big data with the goal of discovering opportunities or solving problems.

Companies employ data scientists for a myriad of crucial reasons, some of which include developing a greater understanding of customer pain points, discovering product or user experience gaps, or analyzing potential growth opportunities. Data scientists utilize data visualization tools to help draw, formulate, and present conclusions or trends they identify in their day-to-day work.

There are many paths to this career, so for those thinking about what to study to become a data scientist, there are a few different options. Data scientists have traditionally come from backgrounds with technical skills in programming and statistics. And while yes, mathematics is required to become a data scientist, the good news is that most data science roles require statistics above all else.

What Is a Data Scientist?

A data scientist is someone who extracts and interprets data to strengthen or align with a business’s overall goals. Data scientists are constantly “wrangling” (or “munging”) data from its raw state into a cleaner, more interpretable presentation.

Data scientists work in big data, machine learning, or AI companies. However, experience in these types of organizations is not required as far as what you need to be a data scientist. Many data scientists come from adjacent backgrounds and fields.

Data scientists usually don’t work alone. Data analysts, data engineers, business intelligence specialists, and architects are the various occupations that a data scientist will work alongside to meet their organization’s goals.

What Is the Difference Between a Data Scientist and a Data Analyst?

To get started with data science, it’s important to have an understanding of the delineation between data scientists and data analysts.

• Data analysts are filtering through data in hopes of identifying trends. They create visual representation with data visualization tools and look to advise internal stakeholders on business progress or consumer trends.
• Data scientists are professional interpreters of data that also have expertise in programmatic skills as well as mathematical modeling. It’s fairly common for data scientists to have previously been data analysts; many professional data scientists will also echo that being a data analyst can be one of the first steps to become a data scientist. Data scientists can do the work of a data analyst but tend to be much more hands-on with their development teams.

Data scientists are more in demand with companies and technologies in machine learning, big data, and AI. Data analysts, on the other hand, can work with products or organizations that don’t have such technical focus.

How Do You Become a Data Scientist?

There are five general steps to becoming a data scientist: reinforce your mathematical and programmatic foundations; learn SQL; study machine learning; get some work experience as a data analyst; and finally, sharpen your skills and knowledge with an online course or bootcamp.

While most data scientists align their education tracts across mathematics, statistics, and computer science, it’s still very much possible to be a data scientist without the necessary degrees.

Here are five easy steps to becoming a data scientist:

1. Reinforce your mathematical and programmatic foundations. It’s important to be abundantly aware of just how math-heavy the data scientist career path is. Data science requires an advanced understanding of mathematics and a general understanding of popular programming languages. Case study: see how a former physicist turned data scientist applied deep learning models to study metamaterials.
2. Learn (and become proficient) in SQL. SQL is the domain-specific language used to extract data from databases. SQL is not as complex as most programmatic languages but is a must-learn for anybody working strictly with large datasets and analysis.
3. Study machine learning. Data science is the underpinning of machine learning and being so, data scientists need to demonstrate and reinforce their knowledge of machine learning algorithms, AI architectures, as well as statistics.
4. Get some experience as a data analyst.Learning the basics of identifying trends within data is crucial to becoming a successful data scientist. Remember, plenty of data scientists begin their careers as data analysts and continue their education in programming as they go.
5. Complete an online course or online bootcamp. While many data scientists feel confident in their abilities, some require guidance and training with up-to-date algorithms and tools. Taking online data science bootcamps is a common practice among today’s data scientists looking to reinforce their mathematical and programmatic foundations.

Can You Become a Data Scientist Without a Degree?

You can learn to become a data scientist if you don't have a degree in a related field. Many data scientists don't possess either a bachelor's degree or a master's degree. In 2018, Indeed studied thousands of data scientist resumes and noticed that there was a stronger variance in educational backgrounds than almost any other career in tech. This is because the demand for data scientists is high, and the supply is low. A large quantity of data scientists transition into data science from other industries like machine learning, data analysis, or software engineering.

Online bootcamps such as Springboard’s Data Science Career Track will prepare students for careers in data science by teaching the necessary technical skills, as well as offering career support using one-on-one mentorship and job application guidance.

Data Scientist Qualifications

When hiring data scientists, recruiters look for a number of different things on a candidate's resume, from data science skills to experience with deep learning, data mining, unstructured data, statistical analysis, and data management. As with any job, there is a range of key skills, education, and career accomplishments that will help data scientist candidates stand out.

To become a data scientist, you should possess the following required skills:

• Programming skills. Data science candidates usually need to showcase a combination of python and R programming, as well as an understanding of Hadoop, SQL, and machine learning/AI algorithms. Similar to software engineers, it’s not uncommon for data scientists to have a portfolio ready that showcases skills in these areas.
• Interpersonal skills. Communication, presentation prowess, and multi-functional collaboration are all soft skills that recruiters look for in data scientist candidates. Data scientists are the medium between business goals and product strategy; recruiters are always looking for data science candidates who can translate big data into a consumable story for the rest of the organization.
• Data visualization. While not programmatic, having an in-depth understanding of data visualization tools like Tableau and Chartist is crucial to the success of a data scientist and a must-have on the resume.
• Business strategy. Data scientists give meaning to data. Demonstrating a deeper understanding of a company’s business goals—and how that can be delineated via big data—is a plus for recruiters looking to hire data scientists.

It’s not always static, but a data scientist’s professional responsibilities usually will include the following:

• Data acquisition. A data scientist will always be looking to discover and integrate new applicable datasets into their organization’s knowledgebase.
• Data wrangling. After the acquisition, a data scientist will wrangle (clean) the data.
• Data integration and storage. Data scientists interact frequently with databases and work to keep them organized.
• Data analysis. Data scientists investigate and interpret data accordingly to draw predictive conclusions.
• Algorithm modeling. Data scientists will build algorithms that can manipulate data for products. Machine learning, statistical modeling, and AI all require algorithms driven by data scientists.
• Presentation to stakeholders. Data scientists will often be required to present their findings to internal stakeholders.

Learn more about typical data scientist positions, including average salary and job descriptions, right here.

How Can Springboard Help You Become a Data Scientist?

Curious about pursuing a career in data science? Springboard’s data science online bootcamps are comprehensive, accessible, and come with a six-month job guarantee.

The Data Science Career Track is designed for those with prior experience in statistics and programming, such as software developers, analysts, and finance professionals.

The online, six-month, self-paced curriculum will help you master statistics skills, Python, data wrangling with Pandas and APIs through completing real-world projects and developing your own data science portfolio.

You’ll learn:

• How to use Python and its standard libraries to write clear, elegant, readable code using the PEP8 standard.
• Pandas, APIs, SQL, and how to work with different file formats such as CSV and JSON.
• Top ML techniques: linear and logistic regression, naive Bayes classifiers, support vector machines, decision trees, and clustering.

If you’re just starting out your software engineering journey, Springboard also offers a Data Science Prep Course, which gives students a crash course for foundational skills in Python programming and statistics—all via a curriculum specifically designed to help you pass the admissions technical skills survey necessary for the Data Science Career Track.

Data Science Career FAQs

Want to know more about how to get into data science or a related field? Read on to find the answers to some frequently asked questions about this high demand career path.

Is data science a good career?

Data science is one of the most in-demand career paths in the tech industry. In fact, IBM projects that by 2020, the number of positions for data science and data analytics talent in the U.S. will increase by 364,000 openings, to 2,720,000. Data science is an ever-growing and important field.

What skills do you need to become a data scientist?

Data scientists need both technical as well as interpersonal skills to be successful in their roles. Data science candidates should have knowledge of Python and R programming, as well as an understanding of Hadoop, SQL, and machine learning/AI algorithms.

How many years does it take to become a data scientist?

Data science is an ever-evolving field, so experts can devote their entire careers to studying data science! That being said, there are multiple paths to getting your first job in data science that do not require spending four years at a university. Online bootcamps typically structure their curriculums to be completed within a year.

Does data science require coding?

Most data science job descriptions will require that candidates have programming skills. The technical capabilities required of a data scientist will usually involve data cleaning and analysis, as well as implementing machine learning algorithms using a programming language like Python or R.

Is data science the right career for you?

Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.

Check out Springboard’s Data Science Career Track to see if you qualify.

Not quite ready to dive into a data science bootcamp?

Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.