Data science is one of the fastest-growing fields in tech, and with good reason: organizations everywhere need talented data scientists who can analyze enormous datasets for crucial insights.
But what’s the best way for someone to break into a data science career? How high can data scientists climb within an organization? And what sort of skills and knowledge do you need to achieve your dreams?
From finance to healthcare, there’s a pressing need for data scientists, so whatever your interests, chances are good you can find an appropriate role. Whatever industry you choose, you’ll need to learn its specific needs, challenges, and vernacular. And remember: while the pressures on data scientists to deliver a correct analysis are intense, the compensation is often extremely generous.
Data Scientist Education
If you’re already working in another tech role—as a software developer, for instance—and want to make the jump to data science, many of your skills (such as Python programming) are very transferrable. For those who like to learn as much as possible about a potential career path before jumping in, or who prefer self-teaching to traditional education, consider the following online data scientist training resources:
- Google—Machine Learning Crash Course
- CalTech: Learning from Data
- Codementor Data Science Tutorials and Insights
- KDNuggets Tutorials
- R-bloggers Tutorial: Data Science with SQL Server R Services
- Open Source Data Science Masters
- Simply Statistics
Here are options that cost money (but you earn a certificate at the end):
- Harvard Data Science Graduate Certificate
- SimpliLearn Certificate Program in Data Science
- Berkeley Online Master’s in Data Science
While you technically don’t need a degree to land a data scientist role, many organizations like to see a degree on your resume. A degree program at a college or university will teach you basic and advanced data science concepts, including data mining, statistical/data analysis, and much more.
Data Scientist Job Interview
During the job interview for any data scientist role, you’ll be asked lots of questions about your data science skills, as well as your mastery of key programming languages such as Python and R. In addition, be prepared for technical queries like this:
- How you can build a predictive model in the absence of labeled data (using unsupervised ML techniques, or keyword-based approaches to generate labels)?
- Explain the concepts of supervised and unsupervised learning, including the types of problems solved by each, as well as the “right” algorithms to use.
Chances are good you’ll also be asked questions to delve into your analytical skills. The trick here is to show the interviewer that you have a holistic grasp of the data and can strategize accordingly. For example:
- What are some of the data sources you would use to solve a long-term business problem?
- How will you clean a dataset before analysis?
- Show us how you think about a problem holistically.
In addition, interviewers will ask questions designed to gauge your “soft skills” such as empathy and communication. Data scientists are often required to work in teams and communicate their findings in an easy-to-understand way to multiple stakeholders.
Career Path: From Data Analyst or Engineer to Data Scientist
Data analysts also analyze data for insight, but often work on a much smaller scale than data scientists; data engineers are tasked with constructing, maintaining, and iterating an organization’s data repositories. If you’re a tech professional in either of these jobs, and you’re interested in transitioning to a career as a data scientist, consider asking your organization if they’ll pay for training and education.
Given the incredible hunger at the moment for data scientists, chances are good your organization will play ball with you on the training front. After all, upskilling an existing worker is cheaper and less time-intensive than hiring someone from the outside. For data analysts, becoming a data scientist involves taking many of the same skills and using them in a somewhat different context; you may transition to a new role faster than you thought.
Career Path: From Beginner to Master
When you’re just breaking into data science, it’s important to make as many connections as possible. Go to meet-ups; join online forums; and compete in data-centric contests. That will give you a good idea of the data science landscape and alert you to new job opportunities.
Even as you learn the fundamentals of data science, keep an eye on cutting-edge technology such as machine learning and artificial intelligence (A.I.), which will deeply impact the industry in coming years. Data scientists who learn how to train ML models will differentiate themselves in a crowded marketplace.
Once you’ve built up experience, it’s time to target mid-level positions. Keep in mind that businesses hiring a data scientist with a few years of experience are very focused on your soft skills, so make sure to come to any job interview prepared with stories of how you used your empathy and speaking ability to navigate past challenges.
For data scientists with experience, companies trust increasingly in your intuition. Data is often messy; building up an instinct for seeing through all that unruly data to a clear conclusion is a prized skill, but also one that takes time to develop.
Data scientists in advanced roles are expected to act as a major voice in strategic decisions. They’re also responsible for fostering the company’s data-science culture, which can mean interviewing and onboarding more junior data scientists—a tough and critical task, one that demands a whole new level of empathy and communication. It’s a tough job, but for those who reach this level, the sheer amount of strategic power is often a thrill.