How to Become a Data Scientist
Data science is arguably the hottest career of the 21st century. In today’s high-tech world, everyone has pressing questions that must be answered by "big data". From businesses to non-profit organizations to government institutions, there is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes.
Finding the right answers, however, can be a serious challenge.
How can a business sort through purchasing data to create a marketing plan? How can government departments use patterns of behavior to create engaging community activities? How can a non-profit best use their available marketing budget to further enhance their potential operations?
It all comes down to data scientists.
Because there is simply too much information for the average person to process and use, data scientists are trained to gather, organize, and analyze data, helping people from every corner of industry and every segment of the population.
Data scientists come from a wide range of educational backgrounds, but the majority of them will have technical schooling of some kind. Data science degrees include a wide range of computer-related majors, but it could also include areas of math and statistics. Training in business or human behavior is also common, which bolsters more accurate conclusions in their work.
There is a nearly infinite amount of information, and there is a nearly infinite amount of uses for data scientists. If you are intrigued by this captivating work, then let’s take a closer look at the career as a whole. Explore what they do, who they serve, and what skills they need to get the job done.
What is a Data Scientist?
Data science is a complex and often confusing field, and it involves dozens of different skills that make defining the profession a constant struggle.
Essentially, a data scientist is someone who gathers and analyzes with the goal of reaching a conclusion. They do this through many different techniques. They may present the data in a visual context, which is often called “visualizing the data,” allowing a user to look for clear patterns that wouldn’t be noticeable if the information was presented in hard numbers on a spreadsheet. They often create highly advanced algorithms that are used to determine patterns and take the data from a jumble of numbers and stats to something that can be useful for a business or organization. At its core, data science is the practice of looking for meaning in mass amounts of data.
Let’s look at a fairly typical example of a data scientist in action. Perhaps a major business, say a cell phone company, wants to know what current customers are more likely to switch services to their competitor. They may hire a data analyst who can look at millions of different data points (or more specifically, create an algorithm to look at millions of data points) related to former customers. They may discover that customers who use a certain amount of bandwidth are more likely to leave, or that customers who are married and between the ages of 35 and 45 are the most likely to switch carriers. The cell phone company can then change their business plan or marketing efforts to engage and retain these customers.
Netflix users see a real-world example of data management in action every time they access their account. The video streaming service has a program designed to give you suggestions that will best fit your preferences. Using information from your past viewing history, an algorithm gives you recommendations for shows you may enjoy. This is also seen in services like Pandora with their thumbs-up and thumbs-down buttons, and from Amazon, with their shopping recommendations.
Data Science vs Statistics
Data science should not be mistaken for statistics. Although these two areas combine similar skills and share common goals (like using a large amount of data to reach conclusions), they are unique in one clear aspect. Data science, which is a newer field, is heavily based on the use of computers and technology. It accesses information from large databases, uses code to manipulate data, and visualizes numbers in a digital format.
Statistics, on the other hand, generally uses established theories and focuses more on hypothesis testing. It is a more traditional discipline that has, from a broad perspective, changed little over the last 100 years or more, while data science has essentially evolved with the rising use of computers.
Who is a Good Candidate?
So what are the top traits of a data scientist? How can you determine if you have the raw material needed for a long career in the field of data science? There are many unique characteristics that apply to data science, so it’s likely you have one or more of these skills.
First of all, you must have a curious nature that pushes a constant pursuit of learning. There are so many areas and so many data points to analyze, that a data scientist must have an inherent curiosity that drives their need to find answers.
You’ll also need a strong ability for organization. As we said earlier, there are millions of potential data points, so keeping each one conveniently tucked away in its own little corner, and making sure information is organized in a useful way is essential. Good organization will help you reach the right conclusions at the end of your work.
You’ll likely find that this career path can sometimes be full of frustration, so a hearty dose of stubbornness is a good thing. When things get tough and it seems like there couldn’t possibly be an answer to the problem, a good data scientist will keep reorganizing, reanalyzing, and working the data in the hopes that a new perspective will lead to a “Eureka!” moment.
Other traits, like creativity, the strong ability to stay focused, and an acute attention to detail, will all help you in becoming a data scientist.
Steps to Become a Data Scientist
There are three general steps to becoming a data scientist:
- Earn a bachelor's degree in IT, computer science, math, physics, or another related field;
- Earn a master's degree in data or related field;
- Gain experience in the field you intend to work in (ex: healthcare, physics, business).
Data Scientist Education Requirements
There are many paths to landing a career in data science, but for all intents and purposes, it is completely impossible to launch a career in the field without a college education. You will, at the very least, need a four-year bachelor’s degree. Keep in mind, however, that 73% of the professionals working in the industry have a graduate degree and 38% have a PhD. If your goal is an advanced leadership position, you will have to earn either a master’s degree or doctorate.
Some schools offer data science degrees, which is an obvious choice. This degree will give you the necessary skills to process and analyze a complex set of data, and will involve lots of technical information related to statistics, computers, analysis techniques, and more. Most data science programs will also have a creative and analytical element, allowing you to make judgment decisions based on your findings.
- Capella University MS in Data Analytics
- Colorado Technical University Bachelor of Science in Information Technology / Data Management
While a data science degree is the most obvious career path, there are also technical and computer-based degrees that will help launch your data science career. Common degrees that help you learn data science include:
At the end of one or more of these degrees, you’ll likely have a wide range of skills that apply to data science. These skills include experimentation, coding, quantitative problem solving, handling large sets of data, and more.
The ability to understand people, businesses, and marketing is also a powerful tool in a data science career. The skills are often highlighted in business, psychology, political science, and various liberal arts degrees. These are often a great minor, complementing a data science degree or a technical degree.
Data science is needed by nearly every business, organization, and agency in the country and across the globe, so there is certainly the chance for specialization. Many data scientists will be heavily specialized in business, often specific segments of the economy (such as automotive or insurance) or business-related fields like marketing or pricing. For example, a data scientist may specialize in helping car dealerships analyze their customer information and create effective marketing campaigns. Another data scientist may help large retail chains determine the perfect price range for their products.
Some data scientist work for the Defense Department, specializing in the analysis of threat levels, while other specialize in helping small startup businesses find and retain customers.
Data Scientist Career Path
While you may have the skills needed to become a data scientist straight out of college, it’s not uncommon for people to need some on the job training before they are off and running in their careers. This training is often centered around the company’s specific programs and internal system, but it may include advanced analytics techniques that are not taught in college.
The world of data science is an always-changing area, so people working in this field need to constantly update their skills. They are continually training to stay at the leading edge of information and technology.
Data Scientist Jobs
Data scientists work in many different settings, but the majority of them will work in office-like settings that allow people to work together in teams, collaborate on projects, and communicate effectively. Much of the work may include uploading numbers and data into the system or writing code for a program that will analyze the information.
The pace, atmosphere, and all-around tempo of the work environment will largely depend on the company and the industry you work in. You could work in a fast-paced work environment that emphasizes quick results, or you could work for an organization that values slow, methodical, detailed progress.
You may find a work environment designed to encourage creative thinking, or you could work in an office that is designed for efficiency and effectiveness; it really depends on the type of data science you are doing and the nature of the business you work for.
Pros & Cons
There are many benefits to becoming a data scientist, and it doesn’t all center around pay. The job is a unique yet challenging career that offers a wide variety of daily tasks, and this variety is often cited as one of the main benefits. As a data scientist, you may work for a wide variety of companies, coming up with solutions and information related to customer retainment, marketing, new products, or general business solutions. This means you get to engage in unique and interesting topics and subjects that give you a wide perspective on the economy and world at large.
Just like any career, there are some clear drawbacks. While the extreme variety of subjects gives you new challenges, it can also mean that you never get to fully dive into a specific topic. The technologies that you use will be constantly evolving, so you may find that the systems and software that you just mastered are suddenly obsolete. Before you know it, you need to learn a whole new system. This can also lead to lots of confusion, as determining which systems are the best for specific jobs is very tough.
Data Scientist Salary
No matter what source you look at, one thing is for sure: these professionals stand to earn a substantial income. The best source for career salaries is the Bureau of Labor Statistics, but unfortunately they do not compile information for data scientists specifically. They do, however, have information on “Computer and Information Research Scientists,” which includes what they call “data mining,” a skill that mirrors data science in many ways. According to the BLS, people working as computer and information research scientists earn an average income of $108,360 per year, and all computer-related occupations pull an average of $79,390. Check out the BLS report on big data here.
These numbers seem to correlate with wage numbers from other sources as well. Glassdoor reports a salary average of $113,436 while PayScale has their earnings at $93,146. A data scientist with 9 or more years of experience can expect a salary around $150,000 and those managing teams of ten or more can expect to earn close to $232,000.
Any source you look at, you can see these advanced skills are in high demand. If you have the skills, training, and know-how that it takes to become a data scientist, you will likely earn a substantial income for the length of your career. There is more good news as well, as these professionals will be in high demand for the foreseeable future.
Anyone working in the field of data science can expect the one-two punch of job security. Not only will they earn an income well above the national average, they can also expect their field to continue to grow over the coming decade. The demand for data scientists is well above national average and 50% higher than that of software engineers (17%) and data analysts (21%). The number of data scientists doubled over the last four years and some even quote the growth at 300%.
As more and more businesses rely on hard information for their decisions, the need for people who can not only compile the information, but can organize it, store it, interpret it, and discover trends, will be all the more important. Data collection by businesses will continue to grow, and data analysts should expect to be in high demand for years to come.
There are many careers that are either branches of data science or extensions of the career. One of the jobs you may move into during your career is a senior data scientist. These professionals will use their training and advanced experience to create new and innovative approaches, lead data science teams, and build new prototypes and algorithms for analyzing information and reaching conclusions.
Similar careers also include software development, computer network architects, database administrators, data analysts, and information security analysts. Any career that uses computer technology, information analysis, or forecasting could be a potential home for anyone trained or experienced in big data.
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