It’s the sexiest job of the 21st century, according to Harvard Business Review. Data scientists have the training and curiosity to make discoveries in the world of big data, which is getting bigger by the minute. Across the globe, an estimated 79 zettabytes (or 79 trillion gigabytes) of data were generated in 2021, an amount that’s expected to double in 2025.
Wondering how to get into data science and enter the lucrative big data analytics market, expected to reach more than $655 billion annually around the globe over the next six years? Here’s your guide on becoming a high-value professional who specializes in analyzing and interpreting data to help organizations make better decisions and ultimately improve their bottom line.
The Growth of Data Science
The term “data science” was coined in the early 1960s to describe a profession that would support the understanding and interpretation of the large amount of data being amassed. Of course, there was no way of predicting the truly massive amounts of data that would be generated over the next 60 years. Today, data science continues to evolve as a discipline that taps into the power of computer science and statistical methodology to make useful predictions and gain insights in fields ranging from medicine and engineering to social sciences and business.
In the future, Forbes predicts data science will be transformed by artificial intelligence and other forms of automation, cybersecurity tools and techniques to help companies protect their data, and quantum computing. To prepare for the future, data scientists will need to understand how quantum computers work, how to program them, and how to use their algorithms to solve particular problems.
Role of a Data Scientist
The role of a data scientist combines elements of several traditional and technical jobs, including mathematician, scientist, statistician, and computer programmer. They collect, analyze, and interpret data to help drive an organization’s decision-making, tapping into a toolbox of scientific principles combined with advanced analytics techniques, such as machine learning and predictive modeling. Data scientists wrangle vast amounts of data to develop and test hypotheses, make inferences, and analyze things such as customer and market trends, cybersecurity threats, financial risks, stock trades, medical conditions, and equipment maintenance needs. Their role varies by industry.
How to Start a Career in Data Science
There are many ways to launch a lucrative career as a data scientist, who have traditionally been well educated, with degrees in mathematics, statistics, and computer science, among others. But things have started to change. Here are six steps to get into the data science field — with or without a bachelor’s degree.
Getting into Data Science Without a Degree
According to Indeed.com, you can land a data science job without a degree from a college or university by acquiring the knowledge and skills you need through educational alternatives such as data science bootcamps. These short-term immersive training programs typically last between three and six months and teach you practical skills that prepare you for entry-level positions. Other alternatives include gaining on-the-job training by accepting an entry-level job as a junior data scientist or pursuing internships or volunteer positions to help you expand your skills and experience.
Getting into Data Science With a Degree
CareerOneStop reports that 37% of data scientists have earned a bachelor’s degree, usually in fields such as computer science, statistics, information technologies, mathematics, or data science. In addition, 35% of data scientists hold a master’s degree, and 14% have earned a doctoral degree.
Mastering Tools and Programming Languages
Data scientists rely on a number of specialized tools and programs developed for their tricks of the trade, such as data cleaning, analysis, and modeling. In addition to the general purpose application Excel, these professionals need to be skilled in key data science programming languages and tools such as:
- Structured Query Language (SQL)
- RStudio Server
- Jupyter Notebook
- Apache Mahout
Data science increasingly involves machine learning, tools that tap into the power of artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed.
Once you’ve learned the basics of programming languages and digital tools that data scientists use, it’s time to gain work experience. While most organizations prefer to hire experienced data scientists, Indeed.com reveals six ways a fresh graduate can gain work experience, whether it’s paid, unpaid, onsite, or remote:
- Contribute to open-source projects. By collaborating with other data scientists on open-source projects, you’ll both develop skills and build professional contacts.
- Go into data science freelancing. By tapping into the gig economy, you can hone your data science skills until you find a suitable full-time employer. Be sure to ask your clients for testimonials to help convince a future employer you’re the right person for the job.
- Volunteer with nonprofit organizations. When you help fill an organization’s data science needs, you gain work experience, the opportunity to build a network and collect references, and exposure to topics of discussion that may arise during a job interview. Better yet, voluntary positions can turn into paid jobs.
- Get involved in hackathons and data-related competitions. Popular data science hackathons around the world include Alcrowd, Datahack, and HackerEarth. These competitions involve building practical data applications for various industries (a great bullet point for your resume) and opportunities to network with other data scientists and tech enthusiasts.
- Serve as an intern. Internships are a tried-and-true way for recent grads to gain hands-on, real-world work experience and may lead to a full-time job.
- Teach data science to novices. Start a blog, vlog, or post regularly about it on your social media pages. Explain projects you’ve completed and the processes you followed. When applying for jobs, mention you have a site where you teach data science, which demonstrates your passion for the field.
Build your Portfolio
Now that you’ve gained work experience, it’s time to show it off in a compelling portfolio that grabs a hiring manager’s attention at first glance and sets you apart from the competition. This all-important document shows clients and hiring managers what skills you can offer them and why you’re the right person for the job.
Your portfolio may showcase simple projects — from data cleaning, analysis, interpretation, and prediction — to more complex ones, such as how you used machine learning to solve a particular problem. Each project featured in your portfolio should include the code you used and easy-to-understand visualizations for the interpreted data.
You’re now ready to kick your career into high gear by building a strong network of professionals in the field and investing in professional development.
Networking and Professional Development
Networking in data science accelerates your career growth by keeping you informed of job opportunities and the latest industry trends and best practices.
By attending industry events such as conferences, meetups, and workshops, you’ll have an opportunity to meet professionals from various backgrounds, exchange ideas, and learn about the latest developments in data science. Speaking at these events or participating in panel discussions can help you build your personal brand and establish yourself as an expert in the field.
Joining one of the numerous online communities dedicated to data science — from forums and discussion boards to social media groups — is another great way to build your professional network and stay up to date on the latest industry news and trends. Likewise, networking through social media platforms such as LinkedIn and Twitter and utilizing mentorship programs provide opportunities to learn from experienced professionals, gain insights into the industry, and receive guidance and support in your career.
Data Science Career Paths
As you’ll discover, data science is a broad field that allows you to embark on a variety of different paths and explore different career options. You may choose to become a data science professional and pursue jobs such as data analyst, database developer, or data scientist. Or you may transition into an analytics-enabled role, such as a functional business analyst or data-driven manager.
Top Roles for Data Science Graduates
As you get ready to launch your new career, here are five entry-level data science jobs to help you get your foot in the door.
- Junior data scientist. You’ll handle the same tasks as a senior data scientist — analyzing, reporting, and communicating the results of analyses — but you’ll be less likely to steer projects or work with large datasets or complex machine-learning models. Glassdoor reports a junior data scientist earns an average of $100,265 annually.
- Junior data engineer. As an expert in the technical workings of data — how it’s collected, stored, and analyzed — you’ll typically work on smaller open-source projects and have less responsibility than senior engineers. According to Glassdoor, a junior data engineer can expect to earn an average $101,638 annual salary.
- Junior data analyst. In this role, you’ll manage and analyze big data, working with experienced analysts and analytics managers to learn how to interpret this data using a variety of tools. You can expect to earn an average annual salary of $75,658, according to Glassdoor.
- Junior data modeler. Responsibilities include creating and maintaining the database structure of a company along with other tasks, such as designing indexes and triggers. Depending on experience, data modelers earn an average annual income of $106,875, according to Glassdoor.
- Junior database administrator. You’ll help manage database-driven websites or other applications, including creating new databases and tables, monitoring performance, and troubleshooting issues. Glassdoor reports a junior database administrator can earn an annual average salary of $101,028.
Is Data Science Right for Me?
Are you wondering whether you’re a good fit for the field? Here are five signs that data science is right for you:
- You enjoy working with numbers and statistics.
- Naturally curious, you enjoy solving unstructured problems.
- Strong programming skills is your forte.
- Using data to drive decision-making is a passion.
- You’re familiar with machine learning, a must-have skill.
If you’ve discovered you’re a great fit for this rapidly growing field, earning a Bachelor of Science in Data Science (BSDS) at National University can give you the knowledge and skills you need to succeed. National University, a Veteran-founded nonprofit, has catered to the needs of hard-working adults by providing accessible, affordable, and achievable higher education opportunities since 1971. NU offers more than 190 programs and 4- and 8-week courses designed to help students reach their goals while balancing busy lives.