Qualified data scientists provide companies with innovative, cost-saving solutions and strategies, making them among today’s most sought after professionals in a variety of industries.
In Indiana, several prominent sectors look to benefit significantly from the use of data science in the coming years, including:
Healthcare – According to the McKinsey Global Institute, big-data strategies that are used to inform decision making could generate up to $100 billion in value annually across the US health care system. In the fall of 2013, Indianapolis-based pharmaceutical company Eli Lilly & Co. partnered with Pfizer Inc. and Novartis AG to improve the Clinicaltrials.gov site. The collaboration received financial backing from the White House’s $200 million Big Data Research and Development Initiative, which was launched in 2012 to improve the U.S.’s ability to extract knowledge and insights from large and complex collections of digital data. Indiana is also home to health insurance juggernaut Anthem, a company that employs 51,500 people.
Automotive – The future of car part manufacturing will undoubtedly be influenced by the use of big data, particularly real-time data streaming. According to the IBM Big Data and Business Hub, “the growing use of telematics and other sensor technologies is changing the way automotive and manufacturing companies approach their customers and their business strategies.” Indiana is home to automotive manufacturing giants Thor Industries (9,400 employees) and Allison Transmission Holdings (2,700 employees), each of which are Fortune 1000 companies.
Preparing for a Master’s Degree in Data Science in Indiana
Master’s degree programs in data science can be highly competitive, with schools giving weight to applicants’ past education, professional experience, their functional proficiency in programming and applied math, and entrance exam scores.
Undergraduate Degree Requirements and Prerequisite Courses
To be considered for admission to a master’s program in data science, applicants would typically be expected to meet the following undergraduate requirements:
- Bachelor’s degree in a relevant quantitative field such as applied math, statistics, computer science, or engineering
- Prerequisites in programming languages, linear algebra, statistics, calculus I and II, and quantitative methods
- Minimum GPA of 3.0
How to Prepare for the Quantitative Reasoning Section of the GRE/GMAT Exams
Applicants’ scores on the quantitative section of the GRE or GMAT are strongly considered by master’s programs admissions offices. Students who score in the top 15% on this section are typically given top consideration for admission. Programs may also give weight to an applicant’s scores on the verbal and writing sections of these exams since strong communication skills are such a vital part of presenting findings.
GRE – The Graduate Record Exam (GRE) revised general test quantitative reasoning section evaluates the following:
- Algebraic topics, including linear equations, functions, graphing, algebraic expressions, and quadratic equations
- Arithmetic topics, including roots, exponents, integers, and factorization
- Geometry topics such as the Pythagorean theorem and the properties of triangles, circles, quadrilaterals, and polygons
- Data analysis, covering topics including interquartile range, standard deviation, statistics, graphs, Venn diagrams, probability tables, and permutations
To prepare for the GRE, students may take two sample tests by downloading a free program through Educational Testing Service (ETS). Additionally, students may sign up with the Princeton Review to take a practice exam.
GMAT – Consisting of 37 questions, the quantitative section of the Graduate Management Admissions Test (GMAT) is designed to evaluate students’ data analytics skills. To prepare for the GMAT, students may take practice exams through Veritas Prep and the Princeton Review.
Relevant Personal and Work Experience
By demonstrating strong communication skills and exceptional quantitative and analytical reasoning abilities at the professional level, applicants may be given top consideration for admission to master’s programs in data science. Schools typically consider the following when reviewing applications:
- Coding skills
- Hacking skills
- Total relevant work experience (five years is preferred)
- Data mining ability
- Database administration proficiency
- Communication skills
- Programming proficiency in languages such as JAVA, C++, and Python
Just some of the jobs through which graduate students develop these basic competencies in Indiana include:
- Cyber security at Anthem, which fell victim to a massive cyber attack in 2015
- Working with data management at Berry Plastics Group
- Statistical analysis at Kar Auction Services
Bridge Courses and Massive Open Online Courses (MOOCs) for Applicants Who Need to Bridge Gaps in Knowledge
Even data experts with impressive professional resumes may lack one or more of the qualifications required for admission to master’s programs in data science. To meet these outstanding requirements, professionals may choose to enroll in massive open online courses (MOOCs). Bridge courses are also available for students that have already been accepted into a graduate program, but who need to become proficient in an area of applied practice prior to beginning graduate courses.
MOOCs – Massive Open Online Courses – MOOCs provide aspiring data scientists with the chance to proactively fill gaps in their knowledge – whether in fundamental mathematics or programming – through a blend of filmed lectures, online modules, and student-professor communication. Prospective students would complete any MOOCs they might be interested in before applying to a master’s program in data
Fundamental and Programming Bridge Courses – Bridge courses are often available to students that have been accepted into a master’s program in data science, but that need to fill gaps in functional knowledge before transitioning to master’s-level courses. These courses are offered directly through the graduate program and typically take about 15 weeks to complete.
Programming bridge courses are for those that need to become proficient in the mandatory programming languages required to begin the master’s degree program.
Fundamental bridge courses would develop proficiency in the following areas:
- Linear algebra
- Algorithms and analysis of algorithms
- Data structures
Earning a Master’s Degree in Data Science in Indiana
As of March of 2016, there is only one master’s program in data science in Indiana, located in Bloomington. However, a growing number of professionals are pursuing their degree through accredited online programs, allowing them to complete their coursework in a more flexible format.
Through these programs, which consist of self-paced coursework, live classes, and an immersion experience, students can earn one of the following degrees:
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
Through part-time and full-time learning, students typically earn a master’s degree in data science in 18-30 months. Through accelerated learning formats, students may earn their degree in as little as 12 months.
Core Curriculum and Immersion
The courses offered in master’s programs in data science are targeted specifically to meet the demands of Indian’s top employers. Examples of these courses include:
- Advanced managerial economics
- Visualization of data
- Quantifying materials
- Applied regression and time series analysis
- Machine learning and artificial intelligence
- Experiments and causal inference
- Data research design and applications
- File organization and database management
- Network and data security
- Experimental statistics
- Scaling data – macro and micro
- Ethics and law for data science
- Statistical sampling
- Data storage and retrieval
- Information visualization
- Data mining
Beyond traditional coursework, master’s programs in data science require students to complete an immersion experience, which consists of collaborative work on a project designed to simulate real-world data science applications. Through these experiences, students can demonstrate their talent and communication skills before entering the professional workforce.
Key Competencies and Objectives
Master’s programs in data science allow students to develop expertise in a number of in-demand areas including, but not limited to:
- Innovative design and research methods
- Interpretation and communication of results
- Sophisticated data analyses
- Database queries
- Data survey analysis
- Association mining and cluster analysis
- Proficiency in programming languages such as GitHub, SAS, Python, and Shiny by Rstudio
- Hash algorithms, cyphers, and secure communications protocols
- Work within a team setting
Career Opportunities in Indiana for Data Scientists with Advanced Degrees
With major companies across a variety of industries in Indiana actively competing for the talents of qualified data scientists, the state is among the most promising in the Midwest for those pursuing the profession. Beyond the healthcare and automotive industries, Indiana plays hosts to a number of successful companies in the transportation industry and the machinery & equipment manufacturing sector. Both fields are expected to rely significantly on the innovative use of big data in the coming years.
The following job listings were taken from a survey of job vacancy announcements for data scientists in Indiana completed in February 2016 (examples are shown for illustrative purposes only and are not meant to represent job offers or provide any assurance of employment):
Senior Data Scientist at Adesa Inc. in Carmel – The data scientist’s role would consist of responsibilities including, but not limited to: analyzing data sets to provide actionable insights that optimize the company’s business operations; using SQL/Tableau to create reports and data extracts against large data sets; and partnering with various teams within the company to develop innovative solutions.
Data Scientist at KSM Consulting in Indianapolis – The role would consist of developing algorithms and methodologies on structured and unstructured data for on-demand and software-imbedded use, including the application and development of machine learning.
Data Scientist (R Programming) at Net2Source, Inc. in Indianapolis – Within a team setting, the data scientist would be responsible for evaluating and documenting the design and development of various system enhancements as the company prepares to implement the next release of the Modeling and Simulation Explorer (MuSE) system.