Online Master's in Data Science for Jobs in Wisconsin

The wave of demand for data scientists is washing through every part of the country, even up here in the north woods. According to tech industry recruiting firm, DICE, two out of the top three fastest growing job categories in technology between 2019 and 2020 were both in the field of data science. One of those, data engineer, expanded by a mind-blowing 50% year-over-year. Data scientists in Wisconsin will find no shortage of opportunities in industries as diverse as finance and insurance, healthcare, retail, human resources, marketing, logistics, manufacturing and a whole lot more.

Companies like Schneider, a logistics and transportation outfit out of Green Bay and Rocket Industrial out of Wausau rely on data-driven insights to streamline operations, building statistical models from enormous troves of data to forecast demand and make strategic decisions about everything from staffing to seasonal fuel purchases to fleet maintenance and upgrades.

But this only scratches the surface of how Wisconsin-based companies are marshalling the power of big data to cut costs and boost profits.

As companies compete for talent, the demand for master’s-prepared data scientists is increasing dramatically. According to the 2020 Technology Salary Guide put out by executive recruiting firm, Robert Half, starting salary offers for data scientists in Milwaukee fall within the range of $108,000 and $184,000. Sky high starting offers are only fitting for professionals that are helping companies solve complex problems and increase revenue to the tune of billions. You can bet the analysts getting those top offers are the ones that come prepared with a master’s degree.

Preparing for a Master’s Degree in Data Science

With so many high tech professionals eager to enter the field of data science, new graduate programs are being created to keep up with the growing demand. But entry remains very competitive. Students can prepare by making sure they meet the standards that are commonly upheld by graduate program admissions departments in terms of undergraduate education, work experience, functional proficiencies and high scores on entrance exams.

Undergraduate Degree and Masters Prerequisite Courses

Due to the rigorous nature of these programs and the competition for entry, graduate schools maintain rigorous requirements for admission. In most cases, applicants must meet the following undergraduate requirements in order to gain entry into a data science graduate program:

  • Completion of a bachelor’s degree in a quantitative field such as statistics, engineering, computer science, or mathematics
  • An undergraduate GPA of at least 3.0.
  • Undergraduate courses in related topics like statistics, calculus, and computer programming
  • Three letters of recommendation from professional references or undergraduate faculty

Relevant Personal and Work Experience

Graduate admissions departments typically seek applicants who have significant professional experience in a field related to data science. Most programs require the following for entry into the program:

  • Several years of technical work experience
  • Significant experience demonstrating proficient quantitative abilities such as data analysis, data management, computer programming, and other related skills
  • Basic understanding of programming languages such as R, Java, C++, or Python

Graduate program applicants generally come from a professional background in database administration, math/statistics, network security, programming or data analytics.

How to Prepare for Success on the GMAT/GRE Exams

Applicants who may not have the kind of work experience to get a waiver on entrance exam requirements would be expected to achieve high scores (at least in the 85th percentile) on the quantitative reasoning section of the GRE or GMAT exams prior to admission into a data science graduate program. Admissions departments also consider strong scores in the verbal and writing sections, since data scientists are expected to be able to effectively show and share their results in plain language that anybody can understand.

You don’t usually just show up the morning of the test and expect to get scores that will see you into a data science master’s program. Prior students have indicated that a significant amount of studying preparation is required in order to earn a competitive score on the exams. Students may take a free practice test online to prepare for the GRE or GMAT.

Online Data Science Bootcamps to Prepare for a Masters Program or Direct Entry into the Industry

Just boning up for an entry exam won’t do you much good if you didn’t get the undergraduate prep or the professional experience needed to be competitive in the graduate program admissions process. While you can’t go back and magically earn a different bachelor’s degree or re-do your career path for the past years, what you can do is enter a data science bootcamp as a way to build your knowledge and practical expertise quickly and at a relatively low cost.

Bootcamps offer an intensive, practical course of instruction in the latest tools and techniques in the field. Usually designed around a cohort of students at similar levels of expertise (some are entry-level, while others are more advanced), bootcamps put you through a series of team-based projects that will have you devising real-world strategies and solutions based on genuine data sets from government, healthcare, and private industry.

Your instructors will have recently come from those fields themselves, equipped with the latest innovations and techniques to teach you. Those tools are evolving constantly, but today usually incorporate:

  • Java, R, and Python programming
  • Statistical libraries like Numpy and pandas
  • Data visualization tools like Tableau
  • SQL and SQL data stores
  • Hadoop and Spark Big Data tools
  • Artificial intelligence and machine learning approaches

Many programs are now being offered online, such as these four from big name grad schools that are open to Wisconsin residents:

Many are privately run by bootcamp providers, but taking one offered by a university gives you a deeper bench in terms of academic expertise and a lot of resources on tap for support and training. That includes experienced career services teams to help you build up your resume and interview skills to increase your odds of admission to a data science master’s program.

Those particular programs are also offered on a part-time basis, which is convenient if you are already holding down another job and don’t have time to take leave for a full-time bootcamp.

Either way, it’s a rapid approach to getting very practical knowledge in the field sure to impress any admissions committee.

Bridging Gaps in Functional Knowledge Through Bridge Courses and MOOCs

If you don’t need the full course of instruction in data science, but just have a few minor gaps in your background to fill, you have a couple of other options for building your fundamental skills that aren’t quite as intensive as a bootcamp.

Bridge Courses  Students that have been accepted into a graduate program in data science based on having met all enrollment criteria, but who lack extensive professional experience or education in either fundamental math and statistics or programming, are often given the opportunity to take prerequisites before transitioning to master’s-level coursework. These are just basic undergraduate level courses in either fundamental sciences or coding that you may have missed on your first pass through college. These 15-week pre-master’s programs are offered directly through the graduate school, frequently in summer term immediately before the main program begins.

MOOCs (Massive Open Online Courses) – MOOCs provide aspiring data scientists with the chance to fill gaps in their knowledge through a blend of video lectures, online coursework, and student-professor communication. MOOCs are for prospective graduate students who wish to gain the necessary proficiencies before applying to a master’s program in data science and are available completely independent of graduate school. You can pick and choose exactly the kinds of courses you want, and enjoy the flexibility of online learning to fit them into your own schedule.

Earning a Master’s Degree in Data Science in Wisconsin

Wisconsin is blessed with a number of well-respected and cutting-edge data science master’s degree programs at excellent public and private schools across the state. Those traditional study options aren’t the only ones available in today’s increasingly virtual world, however. With most students seeking a more flexible program format, online options are even more widely used.

Regardless of the format that you select, a master’s program in data science consists of 30-32 credits. Common program titles that offer essentially the same curriculum include:

  • Master of Science in Data Science (MSDS)
  • Master of Information and Data Science (MIDS)

You can expect to take 18 to 32 months in most programs, depending on whether you choose a full or part-time option. On the other hand, for the truly ambitious, it’s also possible to enter accelerated programs that can have you out on the street and earning the big bucks in as little as a year of hard study.

Curriculum and Core Coursework

Graduate students in data science programs will take courses covering the following topics:

  • Analyzing data
  • Applied machine learning
  • Data visualization and communication
  • Research design
  • Application for data and analysis
  • Retrieving and storing data
  • Data mining
  • Experimental statistics
  • Database management
  • Quantifying data

Students may also be required to complete an internship, which involves being placed with a business or government agency that actively uses advanced data science, where you’ll get your hands dirty with real-world projects. Additionally, most programs require a group immersion experience, which typically serves as the capstone project. This sees you working with your fellow students, working on a project that is designed to bring together all your learned skills and expertise in a practical data science effort. This would usually take place on-site, even in a program that is mostly online.

Key Competencies and Objectives

Graduate programs in data science provide students with a knowledge base they will use to succeed in their future careers. Coursework and immersion experiences are designed to prepare students to become proficient in the following areas:

  • Technical skills such as data mining, machine learning, computer programming, management of data, and network security
  • Translating data into visual formats for lay audiences
  • The ability to communicate findings both verbally and in writing
  • Interpreting large batches of complex data
  • Proficiency in programming languages such as Python or R
  • Understanding of cryptology, hash algorithms, and network security protocols
  • Statistical surveys and analysis

Career Opportunities for Data Scientists in Wisconsin with Advanced Degrees

After earning an advanced degree in data science, the full spectrum of jobs in the industry open up for you. And in Wisconsin, that’s a very wide spectrum indeed. According to CompTIA’s Cyberstates 2020 report, net tech employment in the state increased by almost 5,000 workers over the prior year. That means high tech professionals now represent around 7% of the state’s total workforce, helping to fuel an industry worth more than $21 billion annually.

Data scientists are a significant part of every slice of that industry, working in both the public and private sector and pulling down big salaries to solve problems that are as challenging as they are interesting.

The following job listings are a few examples of those positions, and have been provided as illustrative examples only, and are not meant to represent job offers or an assurance of employment.

Data Science Intern with Lands End at Dodgeville  This internship allows recent graduates to ease into the data science field. An ideal candidate is required to have a graduate degree in a related field and must be familiar with SQL or another querying languages. The chosen intern will work with developing statistical models, investigating company problems using computer science, and interpreting incoming data.

Data Scientist with Smith & Hanley Associates, LLC at Madison – This position requires the candidate to hold a master’s or Ph.D. in computer science, engineering, math, statistics, or economics. An ideal candidate must have at least five years of fulltime business-related experience in data mining algorithms, neural networks, text, and NLP. Responsibilities include using data to identify patterns and developing solutions to complex company problems.

Data Scientist with Foot Locker at Wausau – Those seeking a data science job in the retail industry can apply for jobs in retail store headquarters like Foot Locker. A data scientist in this role would ideally be proficient in green field product development, API based integration, SAS, Python, SQL, MapReduce, Hive, Pig, and Spark. Candidates must have a master’s degree or Ph.D. in a science or math related field as well as at least 3 years of relevant experience or training.

Enterprise Technology Innovation Data Scientist with WPS Health Solutions at Madison – The data scientist in this position is responsible for presenting new analytics opportunities to enhance the efficiency and value of the company. Ideal candidates have a strong understanding of statistical analysis, predictive modeling, and data visualization and are comfortable managing a high volume of complex data. A bachelor’s degree is required, but candidates with a master’s or higher degree are given preference.

Lead Data Scientist with United Health Group at Wauwatosa – This position requires a graduate degree or Ph.D. in statistics, applied statistics, applied mathematics, or similar quantitative fields. Candidates for this position would ideally already have at least five years of experience working with statistics and data related to the health services industry. Primary responsibilities include providing leadership to other data science department employees, implementing predictive models, and using analytic methods to detect healthcare fraud, waste, abuse, or errors.

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