It has proven to be very difficult to define data science. In fact, even the experts can’t seem to come to an agreement! Numerous questions are being asked about the scope of this field of science, relating to Big Data and what that is, whether it is new, and whether it is different from analytics and statistics. The consensus now is that data science is interdisciplinary, and that it is an evolution of the various other elements of business analysis that it touches. Furthermore, it incorporates mathematics, analytics, statistics, modeling, and computer science.
What is Data Science?
The core of data science is that huge amounts of data are being analyzed through automated processes, and that this analysis enables experts to gain knowledge. Automated processes, meanwhile, are everywhere, from high energy physics to genomics. As such, data science has been able to assist in the creation of new scientific branches, while at the same time being of tremendous influence to the humanities and other social sciences. It is expected that this trend will continue to accelerate, as the amount of Big Data, from the web, sophisticated instruments, and mobile sensors, continues to grow as well. It is also expected to take up a lot of academic research, in which traditional disciplines have started to give rise to sub-disciplines to which the terms “quantitative” or “computational” are added. Furthermore, data science has a huge influence on every industry, from media to healthcare, and it is transforming the ways those industries operate.
Interestingly, data science also allows for brand new discoveries, as it is a form of intellectual inquiry. It combines visualization, applied mathematics, computer science, and statistics. And, in so doing, all of the data that is generated all around us each and every day develops into new knowledge and new insights. Very simply put: data science provides insight, and insight is required for growth.
As a fast growing field, it is apparent that the opportunity for data scientists is massive. For instance:
- According to Dell EMC, the growth of big data is tremendous. In fact, the data generated in 2020 will exceed that generated in 2011 by 50 times over. Demand for degree candidates with big data training will be paramount to feed the demand.
- The U.S. Bureau of Labor Statistics (BLS), which considers data scientists as computer and information research scientists, has reported that, in May 2015, these specialists earned an average annual salary of $111,840.
- The BLS has also predicted that from 2014 to 2024, there will be an 11% increase in demand for these specialists, which is faster than the national average. This will translate into 2,700 new jobs.
- The 2011 McKinsey Global Institute Study on Big Data said that in this country alone, there is currently a shortage of 140,000 to 190,000 specialists in the field of analytics. Additionally, some 1.5 million extra managers are required to use the findings of Big Data analysis to make critical decisions. The report also stated that this shortage will only continue to grow.
According to the BLS, the average annual salary for computer and information research scientists in May 2016 was $111,840. The bottom 10% earned $64,950 per year, and the top 10% earned $169,680 per year, a significant disparity.
The BLS has also reported on common career paths, or industries, and their earnings:
- Information science, where average annual earnings stand at $123,800. 14% of data scientists work here.
- Research and development in the physical, engineering, and life sciences, where average annual earnings stand at $123,180. 12% of data scientists work here.
- Computer systems design and related services, where average annual earnings stand at $115,890. 18% of data scientists work here.
- Federal government, excluding postal service, where average annual earnings stand at $105,970. 28% of data scientists work here.
- Colleges, universities, and professional schools; state, local, and private, where average annual earnings stand at $72,030. 11% of data scientists work here.
According to Payscale.com, the average annual salary for a data scientist currently stands at $91,000 per year. They also reported that experience only has a moderate effect on how much someone can earn.
Geographical location is a key factor of importance in terms of how much someone can earn. According to the BLS, the top four states to work in as a computer and information research scientist are:
- New York, with average annual earnings of $146,150
- Washington, with average annual earnings of $133,950
- New Hampshire, with average annual earnings of $128,280
- Virginia, with average annual earnings of $126,800
It should be noted, however, that in areas that have relatively much higher earnings, the cost of living is usually also higher, which means it evens out to a certain degree.
What plays a much more significant role in terms of earnings is the company that someone works for. Indeed.com has reported on the most popular companies for data scientists and their average earnings. This showed that:
- Cisco pays an average of $205,810 per year.
- Selby Jennings pays an average of $157,939 per year.
- Harnham pays an average of $151,999 per year.
- Huxley Associates pays an average of $150,979 per year.
- Sears pays an average of $158,363 per year.
- Averity pays an average of $144,184 per year.
- WSI Nationwide, Inc. pays an average of $146,509 per year.
- Glocomms pays an average of $147,345 per year.
- Staples pays an average of $141,752 per year.
- CyberCoders pays an average of $135,926 per year.
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Online Master’s Degree
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Online Bachelor’s Degree
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Obviously, to have a lucrative career in the field of data science, you will need a good education. This starts with a bachelor’s degree. For the convenience of students, and to remain relevant with the developing digital world, many schools now offer their degree programs online, although on campus studies continue to be popular. One example of a bachelor’s degree in data science is the one offered by UC Irvine’s Department of Statistics, Donald Bren School of Information & Computer Science. Just some of the things you will learn as part of their B.S. in Data Science are:
- Web Search – How do search engines like Google or Bing rank search results?
- Shopping – How does Amazon forecast how many items it needs to store in its warehouses?
- Astronomy – How can we process terabytes/day of telescope data?
- Physics – How do you write software to search for new physics particles?
- Medicine – How can researchers use genomics to make personalized medical recommendations?
- Sports – How can we visualize and understand massive amounts of game sensor data?
- Social media – How does Facebook recognize people in images?
All of these applications use data science. These applications are built on combinations of ideas from database systems, algorithms, machine learning, probabilistic models, statistical forecasting, data visualization, and more.
Naturally, completing a MBA or master’s degree will advance your career much further. This will give you the opportunity to gain advanced skills and knowledge in your field, and set yourself apart from the rest of the crowd. As with bachelor’s degrees, many master’s programs are now also offered online, which makes it easier than ever to complete a degree without having to completely stop working. Johns Hopkins University, Whiting School of Engineering, offers the Master of Science in Data Science. This degree is offered both on campus and online.
Each school is entitled to set its own admission requirements. However, they are usually quite similar across the board due to the fact that these are used to find out whether or not the applicant has the academic capacity to complete a program at the graduate degree level. For instance, the admissions requirements for Johns Hopkins University are:
- Having completed or being in the last semester of a bachelor’s degree from a regionally accredited university; or holding a technical graduate degree.
- Having a minimum GPA of 3.0.
- Sending all official transcripts.
- Completing undergraduate prerequisite courses.
- Sending a resume.
Each college or university is allowed to develop its own curriculum for each particular degree program. This is why it is advisable to study with an accredited university as this ensures prospective employers that your degree included courses that meet the minimum requirements for the profession. For example, the M.S. in Data Science offered by Johns Hopkins University offers the following courses in its curriculum:
- Foundations of Algorithms
- Statistical Methods and Data Analysis
- Principles of Database Systems
- Data Science
- Data Visualization
- Introduction to Optimization
- Statistical Models and Regression
- Computational Statistics
- Optimization in Finance may be substituted
- Large-Scale Database Systems
- Machine Learning
- Semantic Natural Language Processing
- Big Data Processing Using Hadoop
- Probability and Stochastic Processes
- Theory of Statistics
- Queuing Theory with Applications to Computer Science
- Data Mining
- Game Theory
- Stochastic Optimization and Control
- Modeling, Simulation, and Monte Carlo
It is common knowledge that completing an education, particularly a master’s degree, is expensive. First of all, you have to invest a substantial amount of time. Second, a substantial amount of money would be required. Fortunately, the university can signpost you to financial aid, and they may also have scholarships and grants available. At the same time, there are a number of external scholarships that you may want to consider. These include the:
- AFB Paul and Ellen Ruckes Scholarships, for engineering and computer science.
- AFCEA STEM Majors Scholarships, which are for US students majoring in any STEM subject at undergraduate or graduate level.
- Banatao Family Filipino American Education Fund, which offers STEM and computer science scholarships awarded to Northern Californian students of Filipino heritage.
- Betty Stevens Frecknall Scholarship, which offers computer science scholarships for AITP members enrolled on full-time undergraduate STEM programs in the US.
- Citigroup Fellows Program, which offers various FAME (finance, accounting, management and economics) and computer science scholarships and mentorships funded by Citigroup Foundation.
- CyberCorps: Scholarship for Service, which offers scholarships for computer science and technology students, in return for service within the federal government (e.g. information security roles) upon graduation.
- Dr. Robert W. Sims Memorial Scholarship, which offers US scholarships for computer science and STEM students studying in Florida at an accredited university.
- ESA Foundation Scholarship Program, which offers undergraduate scholarships for minority students and women to pursue degrees in computer and video game arts.
- ExCEL Computing Scholarships, which offers US scholarships for computer science students enrolled at an accredited university in the US.
- HENAAC Scholars Program, which offers engineering and computer science scholarships for Hispanic students enrolled on an undergraduate technical degree.
- IEEE Presidents’ Scholarship, which offers US scholarships for the winners of the Intel ISEF competition.
- The NSA Stokes Educational Scholarship Program, which offers US scholarships for computer science and electrical engineering students, in return for one year of service at the NSA (National Security Agency) upon graduation.
Generally speaking, applying for scholarships means that you have to meet certain requirements: studying at a certain school, taking on a certain concentration, working in a certain field, proving financial need, having a minimum GPA, belonging to a certain minority group, being a member of a professional organization, or being of a certain gender.
It is not really required to become certified in the field of data science, but it is recommended. A certification shows that you are committed to your professional development and to the advancement of the field. Naturally, completing certification does require a further investment of time and money, and you often have to maintain certification through continuous education credits. Nevertheless, it is believed to a worthwhile investment because it allows possible advancements in your career. Some certifications that you may want to consider in the field of data science are:
- Certified Analytics Professional
- Certification of Professional Achievement in Data Sciences
- EMC Data Scientist Associate (EMCDSA)
- Cloudera Certified Professional: Data Engineer
- Cloudera Certified Associate Spark and Hadoop Developer
Becoming a member of the relevant professional organizations as soon as you decide to study towards a data science degree, even at bachelor’s level, is a good move. This allows you to access certain scholarships and grants. Furthermore, you are always informed of new developments in your field, which is crucial in the fast developing field of data science. In addition, getting a certification and obtaining continuous education credits would be easier. And finally, you would be able to build a professional network that would be of vital importance later in your career. Some of the associations to consider are the:
- Data Science Association
- Advanced Analytics Institute (AAI)
- Institute for Operations Research and the Management Sciences (INFORMS)
- International Institute for Business Analysis
- Association for Computing Machinery’s Special Interest Group on Management of Data (SIGMOD)
Learn More on BusinessStudent.com
- Occupational Outlook Handbook – Computer and Information Research Scientists. (2016, May). Retrieved from https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
- Data Scientist, IT Salary. (2017, Mar. 25). Retrieved from http://www.payscale.com/research/US/Job=Data_Scientist%2c_IT/Salary
- Data Scientist Salaries in the United States. (2017, Apr. 24). Retrieved from https://www.indeed.com/salaries/Data-Scientist-Salaries
- Bachelor of Science Degree in Data Science. (n.d.). Retrieved from http://datascience.uci.edu/data-science-degree/
- Manyika, J., Chui, M., Brown, B., Bughin, J. Dobbs, R., Roxburgh, C. Byers, A.H. Big data: The next frontier for innovation, competition, and productivity. (2011, May). Retrieved from http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
- Data Science. (n.d.). Retrieved from https://ep.jhu.edu/programs-and-courses/programs/data-science