CareerExplorer’s step-by-step guide on how to become a data scientist.
Is becoming a data scientist right for me?
The first step to choosing a career is to make sure you are actually willing to commit to pursuing the career. You don’t want to waste your time doing something you don’t want to do. If you’re new here, you should read about:
Still unsure if becoming a data scientist is the right career path? Take the free CareerExplorer career test to find out if this career is right for you. Perhaps you are well-suited to become a data scientist or another similar career!
Described by our users as being “shockingly accurate”, you might discover careers you haven’t thought of before.
Find out if it’s really for you
The field of data science is a rigorous one, both intellectually and in terms of the education needed to thrive in it. A great to way to find out if the field is right for you is to take a free data science course through an online learning portal like EdX.
Another way to learn what big data is all about is to follow some insiders:
10 Big Data Insiders to Follow on Twitter
Choose an initial academic path
The basic foundation for a long career in data science is a Bachelor’s Degree. While a data science degree is the obvious career path, not all universities offer a designated undergraduate program in the discipline. Therefore, aspiring data scientists often earn a degree in a related field, such as computer science, statistics, physics, applied mathematics, management information systems (MIS), computer engineering, or informatics. Graduates from these programs typically have a wide range of skills that apply to data science, including experimentation, coding, quantitative problem solving, data management, and more.
A Bachelor’s is the recommended course of initial study for prospective data scientists for two reasons:
• It provides a solid foundation for earning a Master’s Degree, which is very common in the field of data science (see Step 3, below)
• At many universities, a Bachelor’s Degree is a prerequisite for admission to a Master’s program.
However, there are two alternatives to formal undergraduate programs:
Massive Open Online Courses (MOOCs)
A MOOC is an online self-guided course of study made available, often without charge, to anyone with access to the internet. This often fragmented and scrappy educational option requires students to structure their own academic path and allows them to complete projects at their own pace. MOOCs do not provide any job search resources or assistance.
Bootcamps
Bootcamps are an accelerated and intense approach to data science education. They focus on experiential learning, with projects built in to the program. Their instructors and staffing managers – typically practising data scientists – commonly have established relationships with potential employers.
Whereas students who complete a Bachelor’s program come out with a degree and those who opt for a MOOC earn a certificate, bootcamp graduates leave camp with a portfolio of projects.
Graduate Degree(s)
Nine out of ten data scientists hold a graduate-level degree. Most opt for a Master’s, but almost half of those practicing in the field hold a Ph.D.
Data science programs target subject areas such as:
• Mathematics – linear algebra, calculus, and probability
• Statistics – hypothesis testing and summary statistics
• Machine learning tools and techniques – k-nearest neighbors, random forests, ensemble methods, etc.
• Software engineering skills – distributed computing, algorithms, and data structures)
• Data mining
• Data cleansing and data munging
• Data visualization – ggplot and d3.js; reporting techniques
• Unstructured data techniques
• R and/or SAS languages
• SQL databases and database querying languages
• Python (most common), C/C++ Java, Perl
• Big data platforms like Hadoop, Hive & Pig
• Cloud tools like Amazon S3
Employment / Specialization
Some entry level jobs – such as data visualization specialist, management analyst, and market research analyst – may require only undergraduate education.
Positions like machine learning algorithm developer, statistician, or data engineer will most certainly demand a Master’s Degree.
A Ph.D. will often be required to compete for roles such as business solutions scientist, data scientist, and enterprise science analytics manager.
Because data science is needed by nearly every business, organization, and agency across the globe, specialization is an option. Some data scientists choose to work solely in a specific business sector, such as automotive or insurance. Some prefer to focus on a particular aspect of business, like marketing or pricing. And others specialize in helping start-up businesses find and retain customers.
Certifications
Pursuing professional credentials in data science, while voluntary, is quite common.
*Datasciencegraduateprograms.com has compiled a current (year 2018) and comprehensive list of data science certifications*
Continuing Education / Resources
Data science is an ever-evolving because it uses ever-evolving technologies. Continuing education in the field is therefore of paramount importance. The profession offers an array of learning and networking resources:
• Data Science Association
• Institute for Operations Research and the Management Sciences (INFORMS)
• International Institute for Analytics (IIA)
• International Machine Learning Society (IMLS)
• Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)
How to become a Data Scientist
According to Burtch Works, a leading data science and analytics recruitment agency, ‘It’s incredibly rare for someone without an advanced quantitative degree to have the technical skills to be a data scientist.’ In its May 2017 Education Study, Burtch Works reveals that 90% of interviewed data scientists reported having an advanced degree: 49% hold a Master’s and 46% hold a Ph.D. The majority of these degrees are in rigorous quantitative, technical, or scientific disciplines, including mathematics and statistics(32%); computer science(19%); and engineering. Other majors popular in the field are economics and operations research.
While graduate degrees at the Master’s and Doctorate levels are common among data scientists, there are two other possible pathways to entering the profession:
Massive Open Online Courses (MOOCs) A MOOC is an online self-guided course of study made available, often without charge, to anyone with access to the internet.
Bootcamps Bootcamps are an accelerated and intense approach to data science education. They focus on experiential learning, with projects built in to the program. They are typically taught by practising data scientists.
One of the important objectives of any educational track in the field is to teach the most common programming languages used in Big Data – extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Job and recruitment site Glassdoor reports that nine out of every ten data science job postings in their sample required at least Python, R, and/or SQL skills. According to Glassdoor economic research fellow Pablo Ruiz Junco, ‘With these skills, you’ll be eligible to apply to over 70 percent of all online job postings for data scientist roles. Plus, expanding your skills beyond these foundational languages can lead to a higher salary and allow you to cast a wider net when applying.’
Regardless of the route taken to learn the profession, once employed, data scientists commonly undergo some on-the-job training. This training is often centered around the company’s specific programs and internal systems and may also include advanced analytics techniques not taught in college, in a MOOC, or at a bootcamp.