Interested in becoming a data scientist? Learn the foundational steps you need to follow to become a data scientist in this comprehensive guide.
Here’s what we’ll cover:
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.
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.
To get started with data science, it’s important to have an understanding of the delineation between data scientists and data analysts.
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.
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:
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.
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:
It’s not always static, but a data scientist’s professional responsibilities usually will include the following:
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.
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.
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.
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.
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.
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.
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.
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