As technology continues to become more advanced, there is an increasing need for professionals who are data-savvy. Some of the top businesses employing these types of professionals include nonprofits and public agencies. And while there is an increasing need for data-savvy professionals, employers are constantly reminded that such workers are available on an extremely limited basis; this is one of the primary reasons data science professionals earn such a lucrative salary. For now, let’s take an in-depth look at data science, including what it is, how to earn a degree in this field of study, what you can do with your degree, how much money you can expect to make, and the different titles that data scientists commonly work under.
What Is Data Science?
Data is becoming larger on a daily basis and acquiring this data is becoming increasingly cheap. Data specialists have found ways to digitize analog content that was created over the period of many centuries and are able to make meaningful use out of massive amounts of data. Combining this data with other forms of data collected via financial transactions, weblogs and an assortment of mobile devices, these specialists have also been able to optimize business functions like never before. And while much data has been pinpointed from older content, IBM reports that 90 percent of the data existing today was actually created during the past 24 months.
Professionals who specialize in data science are using today’s latest technologies to organize old and new data, which enables them to identify patterns that can be used to boost commercial and social functions. This massive amount of data that the professionals work with is commonly referred to as Big Data, and it is believed that such data will continue to greatly improve economic behavior as well as human social interactions.
Data science, essentially, is a multidisciplinary practice that focuses on the following:
- Data inference
- Algorithm development
The goal of data science is to produce solutions to analytically-complex problems. These solutions are created by collecting raw information, storing it in data warehouses and mining it for patterns and regularities. In doing so, advanced capabilities have been created, allowing data scientists to creatively and innovatively develop advanced data products.
When data is mined, it is analyzed in a way that allows data professionals to uncover findings. In some instances, these professionals may have a specific finding they are looking for. In other instances, they may have no specific finding in mind. Instead, they allow the data mining process to uncover findings they didn’t even know they were looking for, also known as hidden insights.
Data science can be used in any field and industry. Take for example the company Netflix. This company uses data science mining to reveal customer viewing patterns. In doing so, the company is better able to pinpoint what drives customer interest, which then allows it to make appropriate viewing recommendations. Proctor & Gamble is another company that takes advantage of data science. Using a variety of time series models, the company is better able to meet production level requirements by gaining a clearer understanding of future demand needs.
What Does a Data Scientist Do?
If you choose to pursue a degree in data science, you will spend your time mining out insights through data exploration. Generally, data scientists are provided a challenging question from their employer, and it then becomes their responsibility to answer this question by making meaningful use out of the data they are provided. In some cases, data scientists are not provided data and they are instructed to identify and collect the data on their own. In a sense, data scientists are detectives and they carry out investigative leads to spot data patterns and characteristics.
Common tasks carried out by data scientists include any of the following:
- Analyze data to identify trends
- Interpret data to pinpoint analytical solutions
- Determine which data sets are correct
- Develop and apply algorithms
- Mine data
- Communicate findings to appropriate entities, including stakeholders
- Use visualization to communicate findings
- Collect large sets of data
- Structure unorganized data
- Clean data
- Validate data
- Prioritize issues and problems relating to data analytics
In a book titled Doing Data Science, there is an easy-to-understand example explanation of what a data scientist does. This example states that,
Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms—some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.
What Are the Different Types of Data Science Programs?
To become a data scientist, you will need to complete a data science program. When you go to enroll in such a program, however, you will quickly notice that there are many programs to choose from. Your preferred field of study and the career you intend to pursue will determine which program is the best fit. For example, if you intend to become a data statistician, you will want to complete a data science degree program that specializes in Applied Statistics. In addition to a data statistician, nine other top career choices for those who enroll in a data science program include:
- Data engineer
- Machine learning scientist
- Actuarial scientist
- Business analytic practitioner
- Software programming analyst
- Spatial data scientist
- Digital analytic consultant
- Quality analyst
University of Wisconsin
If you are wanting to earn your master’s of science in statistics, you can do so in 12 months at the University of Wisconsin. The program offered through this school is available on a full-time basis only and all credits must be earned on campus (there is no online option available). If you are in need of a program that offers online learning, you will need to enroll in the school’s master’s of data science curriculum. After completing either program, you will have an extensive amount of knowledge relating to:
- Prescriptive analytics
- Statistical methods
- Data visualization
- Data mining
- Complex data analytics
Indiana University, Bloomington
At the Indiana University, Bloomington, you can earn your master’s in data science on either a full- or part-time basis. In fact, if you enroll in the part-time option, you are given up to five years to complete your courses. The school also offers an online learning option. Probably one of the most advantageous benefits of going to this school to earn your data science degree is that you can choose from two educational paths. If you opt for the technical path, a heavy emphasis will be placed on data mining, cloud computing, and analysis of algorithms. If you choose to follow the decision-making path while earning your degree, a heavier focus is placed on leadership, project management, and organizational change.
Southern Methodist University
When enrolling in the master of data science program at the Southern Methodist University, you can earn your degree in 18 months. Specially designed for working professionals, the program provides students with a project-based learning environment, giving them real-world knowledge about computer science, data visualization, statistics, and strategic behavior. When applying for enrollment in the program, please note that you will need to meet a variety of requirements. In some cases, though, a GRE waiver can be obtained.
Earning your master’s degree in data science analytics at Georgia Tech can be completed in as little as 12 months. If you are looking for part-time enrollment option, you won’t find it at this school. With the program being completed in such a small amount of time, you will quickly see that your courses are intense and you are expected to meet strict deadlines on all coursework. Upon enrollment, as well, you will be asked to choose between three educational paths, each of which has a different focus — analytical tools, computational data analytics, and business analytics.
North Carolina State University
Offering an extremely short curriculum, you can earn your data science degree at the North Carolina State University in 10 months. All courses, however, must be completed at the school’s campus, with no online option available. If you intend to extend your studies and earn a Ph.D. in data science, you will not want to enroll in this program as it is a STEM degree and cannot be used to earn a doctorate-level credential. You can, however, go on to earn SAS product certifications, which are essential to expanding your career opportunities as well as increasing your salary.
With a degree in data science, you are going to make a very comfortable salary. In fact, according to Indeed.com, salary levels for data scientist job openings are 113 percent greater than the average salaries for other job postings. In 2014, the average salary for a data scientist was $123,000 a year. Today (2018), it is not uncommon for data scientists to earn upward of $135,000 a year. As a greater need for data scientists continues to develop, annual salaries are expected to increase as well.
Who Will Make a Good Data Scientist?
Most of today’s most successful data scientists possess a strong background in technology and have a good sense of intuition relating to data. They understand the importance of meaningful use in data mining and have creative and analytical capabilities in being able to structure data according to the complex problems needing to be solved. More importantly, they have strong communication skills and are able to effectively transmit their findings in understandable terms to all parties affected by their findings.