Qualified data scientists in the Midwest are so hard to find that Seth Dobrin, chief data analyst at agricultural giant Monsanto, likened them to “unicorns” in a 2015 LinkedIn article. Even with this scarcity, however, companies in Iowa across several major industries are looking to big data more than ever to increase efficiency and maximize profits. As a result, data scientists are now, and will continue to be, among the most sought after professionals in the state.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SMU - Master of Science in Data Science - No GRE Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
Agriculture is just one industry in Iowa that will be significantly impacted by the application of data science in the coming years. Since 2017, the Iowa AgriTech Accelerator has been brining four to six new start-ups to Des Moines each year, with the batch for 2020 including companies developing sensor technology that can report soil conditions in real-time and others offering cloud-based agronomic analytic solutions that can be tuned right down to the level of individual farmers.
Beyond agriculture, data scientists in Iowa will continue to bolster the state’s strong manufacturing sector through innovative strategies and solutions. According to the Iowa Area Development Group, manufacturing accounts for 88 percent of Iowa’s total exports, with billions of dollars of manufactured goods being produced every year. Master’s-educated data scientists play an integral role in manufacturing companies, developing cost-saving initiatives from the machine level to organization-wide practices.
As those practices bear fruit (or corn, as the case may be), you’ll find even more opportunities emerging in Iowa and surrounding states for fully qualified data scientists.
Preparing for a Master’s Degree Program in Data Science in Iowa
A master’s degree in data science gives professionals a competitive edge over their peers when applying to the highest paying jobs, creating a tremendous demand for graduate programs in the field. As a result, admission to these programs is very selective, with schools considering performance on entrance exams, past education, work history, and proficiency in a variety of areas related to the field.
Undergraduate Degree Requirements and Prerequisite Courses
To be considered for admission to master’s programs in data science, applicants are typically expected to meet the following undergraduate requirements:
- Applicants must earn a minimum of a 3.0 GPA during undergraduate studies
- Applicants must possess a bachelor’s degree in a field such as computer science, applied math, statistics, or engineering
- Applicants must complete prerequisite courses, which typically include the following:
- Calculus I & II
- Linear algebra
Applicants must be able to demonstrate working knowledge of fundamental concepts in the following areas:
- Data structures
- Algorithms and analysis of algorithms
- Linear algebra
Preparing for Success on the GRE/GMAT Exams
Master’s in data science programs typically seek applicants who have scored in the top 15% of the quantitative reasoning section of the GRE or GMAT. Admissions offices may also consider an applicant’s scores in the verbal and writing sections given the importance of communication skills in the field of data science. Either way, you’re going to need to hit it out of the park on one of these two exams to be considered for admission.
GRE – The Graduate Record Exam (GRE) revised general test quantitative reasoning section evaluates the following:
- Algebraic topics such as functions, linear equations, algebraic expressions, quadratic equations, and graphing
- Arithmetic topics such as integers, roots, exponents, and factorization
- Geometry topics including the properties of triangles, circles, quadrilaterals, polygons, and the Pythagorean theorem
- Data analysis, including topics such as statistics, graphs, Venn diagrams, interquartile range, standard deviation, probabilities, permutations, and tables.
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 – The quantitative section of the Graduate Management Admissions Test (GMAT) consists of 37 questions that evaluate students’ data analytics skills, particularly in the areas of problem solving and data efficiency. To prepare for the GMAT, students may take practice exams through Veritas Prep and the Princeton Review.
Prior Relevant Work Experience
Admissions department will usually give top consideration to applicants who have already demonstrated exceptional quantitative and analytical reasoning abilities and strong communication skills through their professional work. Among the professional skill sets considered are:
- Communication skills
- Programming proficiency in languages such as Java, C++, and Python
- Database administration proficiency
Five to seven years of experience are preferable, but you’ll be evaluated on the merits of the position itself. Just a few examples of positions and companies in Iowa that would satisfy work experience include:
- Data analysis at A.Y McDonald Manufacturing Company
- Data management at one of the Mercy Medical Center locations
- Cyber security at Dubuque Bank and Trust
Picking an Online Data Science Bootcamp to Prepare For Master’s Program Applications or Jobs in Data Science
As big a boost as a master’s degree in data science can be to your career, it’s not the only route to a job in data science. And it’s not always an easy path to follow—competition is stiff, and the standards are high.
One other path to those jobs is one that can also boost your prospects of acceptance into a data science master’s program down the road… a two-for-one deal that you can achieve at a fraction of the price and in only weeks or months.
That path is going through a data science bootcamp. Although it’s easier to get into these kinds of programs and faster to get through them than a full-on master’s degree, you’re not going to breeze through one, either. For starters, bootcamps exist all along the spectrum of skills and specializations in data science, so they range from beginner’s camps that have few if any entry restrictions to those that only admit current master’s holders or even PhDs. Because of the limited timeline, they pack a lot of learning into a rigorous, rapid curriculum that emphasizes hands-on skills and practical results. You’ll have to scramble to keep up.
Fortunately, they are generally conducted for a cohort of students who are expected to work together on a series of projects that build knowledge and skills along the way. You’ll have both your fellow students and experienced instructors to fall back on when the going gets tough.
And it will get tough. You will start off in the deep end, learning new and hot technologies in the industry like:
- Hadoop and Spark for Big Data storage and analysis
- Advanced statistical algorithms
- SQL and SQL-based data stores like MySQL and SQL Server
- Python and R programming
- Sophisticated analytics libraries like Matplotlib and Numpy
- Data visualization tools such as Tableau and HTML/CSS
You might not immediately think of Iowa as a hotbed of data science training, but the wonders of the internet can and do bring in some of the most talented instructors to deliver comprehensive curricula online. These four examples are all available to Iowa students, for instance:
- Northwestern Data Science and Visualization Boot Camp
- Rice University Data Analytics Boot Camp
- The Data Analysis and Visualization Boot Camp at Texas McCombs
- University of Minnesota Data Visualization and Analytics Boot Camp
All operate as part-time programs and are run on a part-time basis to accommodate anyone who has other daytime commitments. And with the resources of major colleges with strong data science programs, you’ll be able to draw on professional instructors and advanced career services teams to help you get your launch into the industry right.
Whether you build a career out of what you have learned in a boot camp or use it to help get into a master’s program, it’s a solid investment.
Bridge Courses and Massive Online Open Courses (MOOCs) for Applicants Who Need to Fill Gaps in Knowledge
Students who lack one or more of the qualifications necessary for admission to a master’s program in data science might choose to independently pursue Massive Open Online Courses (MOOCs) to build those skills. Another option for those who are on the bubble and have been accepted to the program but need to further develop proficiencies before transitioning to master’s-level courses can take advantage of bridge programs available through schools that offer master’s programs in data science.
MOOCs – Massive Open Online Courses – MOOCs allow students to fill gaps in their knowledge through a blend of online problem sets, interactive user forums, video lectures, and other online learning techniques. Students can pursue these courses independently to complete their outstanding requirements before applying to a master’s in data science program, taking advantage of the flexibility of the format and the ability to pick and choose among many different options.
Bridge Courses – Certain master’s programs in data science offer bridge courses for students who meet all admission criteria other than several basic proficiencies. These are essentially lower-level college courses that you may have missed out on as an undergrad but that fill in the blanks on some hard skills that you’ll need to be able to keep up in a master’s program. Serving as a precursor to master’s studies, bridge courses typically last 15 weeks and are offered in one of two focus areas:
- Fundamental bridge programsoffer courses in linear algebra, data structures, algorithms and analysis of algorithms, allowing students to earn their outstanding qualifications.
- Programming bridge programsoffer courses in programming languages such as Java, C++, and Python, allowing students to become proficient in these languages before beginning graduate coursework.
Earning a Master’s Degree in Data Science in Iowa
By offering both curricular coursework and immersion experiences, master’s programs in data science equip students with the skill sets sought after by today’s top companies. Through part-time and full-time learning, students typically earn their degree in 18-30 months. Through accelerated learning formats, students may earn their degree in as little as 12 months.
Titles for relevant master’s degrees might include:
- Master of Science in Data Science (MSDS)
- Master of Information and Data Science (MIDS)
Online programs have become a common path for graduate studies in data science due to their flexible scheduling options, which better allow students to maintain a career while pursuing their education, and the wide array of program availability in states where there are relatively few graduate programs offered on campus, such as in Iowa.
Core Curriculum and Immersion
Master’s in data science programs offer a diverse blend of courses that prepare students for the professional opportunities in the field. Just some of the topics these courses cover include:
- Data storage and retrieval
- Information visualization
- Machine learning and artificial intelligence
- Data research design and applications
- Network and data security
- Ethics and law for data science
- Statistical sampling
- Data mining
- File organization and database management
- Applied regression and time series analysis
- Experiments and causal inference
Master’s programs in data science also require students to complete an immersion experience – a team-based project that simulates real-world data application. Through these projects, students apply their knowledge and talents in collaboration with their classmates and professors.
Key Competencies and Objectives
Upon graduation from master’s programs in data science, students can expect to be proficient in skill sets including:
- Interpreting and communicating results
- Developing innovative design and research methods
- Familiarity with hash algorithms, cyphers, and secure communications protocols
- Working within a team setting
- Conducting association mining and cluster analysis
Career Opportunities in Iowa for Data Scientists with Advanced Degrees
The shortage of data scientists in Iowa and the increased use of big data by the state’s farming and manufacturing industries has led companies to actively compete for qualified talent.
Just one Iowa company using data science to create cost-effective strategies and solutions is Johnston-based DuPont Pioneer. The agricultural powerhouse employs data scientists to create data-driven farming technologies that allow farmers to use real-time data in areas ranging from tillage to water use. With access to these technologies, farmers can work more efficiently, leading to massive reductions in costs for the company.
In addition to the opportunities for data science in the commercial sector, several Iowa universities are employing data scientists to further school-wide use of big data and to conduct research on university projects that receive outside funding. The state is also seeing the introduction of big tech startup accelerators like Techstars, which the Des Moines Register reported would set up shop in the state in 2020.
The following job listings for data scientists in Iowa are not meant to represent job offers or provide any assurance of employment.
Principal Data Scientist at Rockwell Collins in Cedar Rapids – This role revolves around working within a business analytics team to establish strategies and solutions through the use of massive data volume. Rockwell Collins is a $4.6 billion company with 20,000 employees.
Data Scientist/Computational Scientist at University of Iowa’s Information Technology Services in Iowa City – This technical role consists of working with University of Iowa researchers on projects and grant opportunities related to the following areas:
- Big Data