With every major company now deeply dependent on consumer and operational data to remain competitive in the global marketplace, every aspect of business relies in some way or another on skilled professionals capable of deriving meaningful insights from unstructured data so it can be used to improve efficiency and inform strategic decision-making.
- SMU - Master of Science in Data Science - Bachelor's Degree 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
But those professionals are getting harder and harder to find; a 2018 LinkedIn Workforce Report found that there were more than 151,000 unfilled data scientist positions across the United States… At the same time, KPMG’s CIO Survey reported that big data talent remains the number one business need according to the chief information officers surveyed.
Universities are rushing to fill that gap in Ohio by ramping up their data science degree offerings in terms of both student capacity and the sophistication of the curriculum. There’s no better example of this than the Translational Data Analytics Institute (TDAI) at the Ohio State University in Columbus.
With a highly-respected master’s program on offer and specializations in interests ranging from biomedical informatics to data visualization to social and decision science analytics, it’s become a hub for Midwestern data science research and education. In 2020, TDAI hosted a workshop for the Midwest Big Data Innovation Hub, one of four such hubs funded by the National Science Foundation.
This is the right time to get the advanced training businesses in Ohio are looking for, and to get into the candidate pool while companies are competing for talent by making some serious starting salary offers.
Preparing for a Data Science Master’s Degree in Ohio
Planning ahead is important when it comes to getting into a data science grad program. Still limited in number, and swamped with applications driven by the interest in the lucrative and interesting jobs they lead to, master’s programs are picky about who they let in.
You need to start charting your course even before you complete your bachelor’s degree to make sure you build up the skills and knowledge they’ll be looking for. Taking appropriate courses and obtaining relevant work experience will greatly improve your chances of getting to the top of the admissions list.
Undergraduate Degree and Master’s Prerequisite Courses
Math is hard, but it’s absolutely vital to truly learning to become a data scientist. By the time you get to the master’s level, you’ll need to have an appropriate background in the quantitative skills needed for this field. Preparing for a data science master’s degree entails:
- A minimum undergrad GPA of 3.0
- A degree in a quantitative field like computer science, applied math, statistics, or engineering
- Coursework in such key disciplines as calculus I and II, linear algebra, statistics, quantitative methods, and programming languages
Relevant Personal and Work Experience for Admissions
It’s not just academics that are considered, either. Real-world experience of some sort is a big boost to your admissions packet, and you should plan to accumulate some before you apply. Typically, graduate schools look for applicants with highly relevant professional experience including:
- At least five years of technical work experience ideally obtained through employment that demonstrates quantitative skills
- Strong communication skills
- Personal experience related to coding, data mining, database administration, mathematics, or statistics
Performing high level work with an employer is essential to obtain the strong letters of recommendation required to be admitted into a data science master’s program.
Scoring in the 85th Percentile on the GRE/GMAT Exams
Obtaining a score in the top 15th percent of the GRE and/or GMAT is an excellent way to demonstrate core competency in key data science skills. Serious preparation for these exams is essential; showing up the day of with a hangover and no cramming is a ticket to the bottom of the stack of applicants. Both the testing companies themselves and your unfortunate predecessors who have taken these exams strongly recommend taking practice tests on sample quantitative problems until the candidate feels proficient with them.
The GRE’s quantitative section is particularly important and evaluates the candidate’s skills in algebra, geometry, data analysis, and arithmetic. You should pay particular note to statistics including probabilities and standard deviations. The official GRE website offers free practice exams and sample questions.
The General Management Admissions Test (GMAT) evaluates a candidate’s quantitative, writing, and verbal abilities. Graduate school admissions departments expect high scores in all of these areas. However, the 37 questions that assess data efficiency and problem solving are particularly important. Candidates can take GMAT practice exams through Veritas Prep and The Princeton Review.
Exploring Data Science Boot Camps in Cleveland or Online as a Path to Graduate Studies or Data Science Careers
There’s a new option for data science education that might just be the ticket to getting you into a graduate program or directly into the industry itself.
That option is a data science boot camp. Combat boots aren’t required, but discipline and intensity definitely are in these 1-9 month-long crash courses in data science. Intensely practical in nature, boot camps steer away from theoretical and academic investigation in favor of hands-on, practical data analysis skills, using real-world datasets and the same cutting-edge tools that are common in the industry.
Boot camps have a spectrum of qualifications they look at in candidates, depending on the skill level they teach to – everything from just a GED and a pulse for entry-level programs, to advanced programs that require applicants to have a master’s and several years of experience in the field.
At the entry level, you’ve got the Case Western Reserve University Data Analytics Boot Camp. Taught both in a virtual class format and on-site in Cleveland, this is a six-month, part-time program with few entry requirements but a lot of knowledge to pass along from experienced instructors.
The program offers a fast-paced course of training in subjects like:
- Python programming
- SQL language skills in MySQL and MongoDB
- NoSQL database work
- Web visualization in HTML/CSS
- Hadoop and machine learning
By focusing on the local demands in the Cleveland area and using live data with relevance to regional employers, there’s no better setup for a direct-entry position. Career services bundled with the program make it easy to land interviews, and you might just decide to take a detour into a professional position instead of going directly to a data science master’s program after your training.
Bridge Programs and MOOCs (Massive Open Online Courses) to Fill Gaps in Functional Knowledge
A boot camp is only one option for boosting your baseline skills for a master’s program, however. Many graduate programs recognize that students are coming in without the right mix of skills to maximize their learning potential. These schools commonly offer bridge programs for candidates who may lack specific skills essential for data science work. Two types of bridge programs are available, reflecting the deficiencies candidates commonly have:
- Programming bridge programs – training in such essential programming languages as JAVA, C++, and Python
- Fundamental bridge programs – courses in algorithms and their analysis, data structures, and linear algebra
These are almost always simply the same undergraduate courses that students take as part of quantitative degree programs at those schools, so you benefit from some continuity in the curriculum.
Another, more self-directed, way to acquire key skills is by arranging to take part in Massive Open Online Course (MOOC). These courses are exactly what they are described as, and usually also offered by or in conjunction with major colleges. The disadvantage is that the Massive nature means you have less opportunity for one-to-one instruction; the advantages are that they are usually inexpensive or even free, and that, unlike a bridge program, you can pick and choose exactly what kind of classes you feel will best meet your needs.
Earning a Master’s Degree in Data Science in Ohio
Prospective graduate students in data science have a large number of options to choose from in Ohio. The state’s rich academic diversity provides a number of local master’s programs, while residents of Ohio can also avail themselves of the high-quality education available online.
Data Science Programs Available From Ohio Schools
You can find master’s-level programs offered in traditional formats all across the state, from Athens to Bowling Green to Kent, in a variety of specialized areas:
- MBA in Business Analytics
- MS in Analytics
- MS in Applied Statistics (specialization in Business Analytics)
- MS in Management – Business Analytics
- MS in Health Informatics
- MS in Digital Science – concentration in Data Science
Online Data Science Programs For Ohio Residents
In addition to the data programs offered by state and private schools in Ohio, students have a number of options to obtain a master’s degree in data science from highly respected online programs.
Such programs offer greater flexibility to working professionals and are offered in a variety of options ranging from accelerated to part-time programs. These programs range from as little as 12-months for accelerated programs to 32-months for part-time study. A graduate student studying full-time can expect to graduate in 18-months.
In addition to the initial online coursework, most graduate programs require that their data science students spend their final semester on campus. Such immersion programs involve intensive classes on campus and the opportunity to network with professors and peers.
Degree programs available include:
- Master of Science in Data Science (MSDS)
- Master of Information and Data Science (MIDS)
- Online Certificate in Data Science
- Data Mining and Application Graduate Certificate
Core Curriculum Content
While the specific courses required will vary in different master’s programs, the core courses will cover the essential skills required for data science positions. All programs will include topics like:
- Network and data security
- File organization and database management
- Information visualization
- Applied regression and time series analysis
- Machine learning and artificial intelligence
- Data research design and applications
Most online data science programs provide the opportunity for students to apply their academic training to real-world problems during their immersion experience. Students get the chance to work in small teams and spearhead a data science project. Often, these projects involve data science solutions for local companies.
Key Competencies and Objectives
Master’s programs in data science equip their graduates with a breadth of proficiencies in a number of core areas:
- Programming languages such as Python and C++
- Ethics, privacy, and relevant law
- Data mining and machine learning
- Data cleansing
- Statistical sampling
- Research design
- Communication and visualization
Career Opportunities for Ohio Data Scientists with Advanced Degrees
Graduates of data science master’s degree programs in Ohio have a wide breadth of opportunities for employment. Large companies almost universally employ data scientists, and generate a lot of data for them to work with. As of 2020, Columbus alone was home to at least 20 Fortune 1000 companies, and 29 Fortune 500 companies call Ohio home.
In addition, you’ll find career opportunities with highly specialized data science companies that need scientists with advanced expertise. For example Soothsayer Analytics in Dublin only employs data scientists with master’s or doctoral degrees.
This company synthesizes design & data science using predictive AI to identify opportunities for businesses to optimize their operations, and thier outcomes. Its data scientists are well versed in innovations such as natural language processing, meta-model based simulations, and deep learning.
Another high powered Ohio data science company is Cleveland’s Advance Ohio. Advance is the firm behind cleveland.com, the number one source of news and information in the state, and Sun News, one of the largest paid weekly publications in the U.S. Data analysts working there specialize in creating data systems to help the company understand and predict consumer behavior patterns using statistical modeling, data monetization, and consumer data analytics.
Advance Ohio utilizes demographic data on 4.7 million households and more than 140,000 businesses in the state.
To illustrate the breadth of data science job options and qualifications in Ohio, you only need to look as far as job listings from local employers across the state. These listings are informational only and should not be interpreted to be current job offers or an assurance of employment.
Watson Health – Data Scientist, Analytics with IBM in Cleveland – This position involves creating new data-driven techniques and requires basic knowledge in Python, Java, and SQL along with machine learning, data manipulation, and statistical packages in R and/or Python.
One year of experience in each of these areas is required. Applicants with a Master’s degree are preferred.
Senior Data Scientist with ADP in Findlay – Applicants who can work with large amounts of real data with R, SQL, or other statistical packages are preferred. A strong knowledge of SQL query is considered a plus. Another preferred qualification is strong experience with applied statistics and defining key business metrics.
A Master’s degree in information science, business analysis, engineering, business administration, or a related field is preferred.
Marketing Data Scientist Manager with JP Morgan in Columbus – This position combines problem solving and client interaction with cross-LOB analytical insights and reporting solutions. JP Morgan desires candidates with 5+ years of analytical and reporting experience and requires proficiency in standard data mining and analytical methods with SAS experience preferred. In addition, candidates should know how to perform time series analysis and have demonstrable experience in applying comprehensive factor analysis. Applicants should also have a technical understanding of data extraction, transformation, and load processing.
A Master’s degree in statistics, math, engineering, or the sciences is preferred.
Data Scientist with Parker Hannifin Corporation in Cleveland – The individual will use data mining and modeling techniques to address tactical and strategic business programs that help drive growth. Qualifications include experience in feature engineering, data mining, machine learning, and regression analysis in a business setting. The applicant must be proficient in Java, SQL, statistical software (Python, R, and Stat) and be comfortable working with large data sets of varying quality to solve business needs.
A Master’s degree in data science, statistics, BI, econometrics, physics, or mathematics is preferred.