Data scientists possess the technical skills to solve problems and the curiosity to be the first to discover the problems that need to be solved. Straddling both the IT and business worlds, data scientists work to find patterns in large amounts of data (called big data) and connect them to real-world applications.
From Pittsburgh to Harrisburg, Pennsylvania companies and government agencies flooded with data and working to make sense of it turn to data scientists, making these technology data experts a hot commodity throughout the Keystone State. Data scientists use data science to boost revenue, cut costs, streamline operations, protect public health, and even save lives.
For example, Penn Medicine is just one of a growing number of Pennsylvania healthcare systems using big data to save lives. Their big data project, Penn Signals, has developed predictive analytics to predict deadly diseases. Using a homegrown data warehouse that holds records on more than three million patients of the last 10 years, data scientists build predictive models based on historical data – and it’s beginning to yield results.
For example, the results of Penn Medicine’s data science efforts have resulted in the development of a sepsis early warning system, which has led to a 4 percent reduction in mortality rates, and an algorithm for detecting patients at risk for cardiac failure, which has helped detect 20 percent more patients trending toward heart failure and has identified patients who are five times more likely to be readmitted after heart failure.
Preparing for a Master’s Degree in Data Science in Pennsylvania
The best preparation for pursuing or advancing a career in data science is arguably the master’s degree in data science. However, these popular degree programs have strict admission requirements in place to ensure that students possess the technical skills required to handle the curriculum demands.
Admission requirements for master’s degree programs in data science often include:
- Undergraduate degree in a related discipline (or the completion of specific undergraduate prerequisites)
- Minimum undergraduate GPA
- Minimum GRE/GMAT scores in the quantitative reasoning section
- Previous experience in a quantitative discipline
- Admissions essay/interview
- Professional letters of recommendation
Undergraduate Degree and Master’s Prerequisite Courses
Undergraduate degrees are an important component when determining a candidate’s qualifications for entering a master’s degree in data science. Some of the desirable undergraduate degrees commonly held by candidates interested in pursuing a data science master’s degree include:
- Applied mathematics
- Operations research
- Computer science
While some institutions require candidates to possess a bachelor’s degree in a quantitative discipline, others do not; instead requiring bachelor’s prepared candidates to have completed specific undergraduate courses, such as:
- Programming languages (C++, Python, JAVA, R)
- Linear algebra
- Data structures
- Algorithms and algorithm analysis
Bridge Programs and Massive Open Online Course (MOOC) Options
Some colleges and universities accept students into a master’s degree in data science that may not have satisfied all of the program’s required undergraduate courses, instead allowing them to satisfy the prerequisites through the completion of bridge courses. Bridge courses allow students to satisfy the educational gaps of their undergraduate work upon admission into the program.
Another option available to students with undergraduate deficiencies is massive open online courses (MOOCs). Students with an interest in pursuing graduate work in data science may use MOOCs to complete any prerequisite courses they did not complete during their undergraduate course of study. MOOCs offer flexible scheduling options from a variety of providers. Students complete them according to their schedule and through a completely online format.
Relevant Personal and Work Experience
In addition to possessing specific undergraduate coursework, candidates for master’s degrees in data science must also often possess relevant work experience to qualify for admission into the program. Many colleges and universities look for experience in areas like:
- Data analytics
- Data mining
- Text analytics
- Machine learning
- Pattern recognition
A number of Pennsylvania firms employ entry-level data scientists, thus allowing those with undergraduate degrees in a quantitative major to begin building their resume.
For example, GlaxoSmithKline in Upper Providence employs bachelor’s level clinical data scientists to provide input to protocol and study data quality plan, input data capture tools for clinical studies, and develop and manage the execution of the validation and integration plans. Likewise, JUNO Search Partners in Bristol employs entry-level data scientists that possess bachelor’s degrees in engineering, math, or a related degree for their Strategic Operations team.
Preparing for Success on the GRE/GMAT
Many colleges and universities requires candidates to submit GRE or GMAT exam scores when applying to a master’s degree in data science. More specifically, they consider the quantitative sections of these exams, often looking for candidates who scored in the 85th percentile in the quantitative section. Preparation for these exams allows students to improve their chances of success:
The Quantitative Reasoning Section of the GRE exam assesses a candidate’s skills, concepts, and abilities in four areas:
- Data analysis
Individuals may review the Math Review document, which provides a detailed overview of the exam’s quantitative section.
The quantitative reasoning section of the GMAT measures a candidate’s ability to analyze data and draw conclusions using reasoning skills.
Study tools, sample questions, and videos help test takers prepare for the exam.
Earning a Master’s Degree in Data Science in Pennsylvania
Pennsylvania colleges and universities offer master’s degrees in data science in a variety of formats:
- Online: A number of institutions offer data science master degree programs in an online format. Students of these programs complete all required curriculum through online study, thus providing them with an unparalleled level of flexibility and convenience. Most programs require students to visit the campus on just one or two occasions to complete an immersion experience, which allow students to meet their peers, their professors, and to engage in networking activities. Online programs are designed to rival their campus-based counterparts with features such as videos, interactive case studies, and self-paced lectures.
- Part-time/accelerated: Although many students choose to complete data science master degree programs in a full-time format, other students may find that completing the program in a part-time or accelerated format better fits their personal or professional goals. Full-time programs in data science take between 18 and 24 months to complete, whereas accelerated programs take about 12 months and part-time programs take about 32 months.
Depending on the institution, master degree programs in data science may go by a number of titles:
- Master of Computational Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Statistics: Data Science
- Master of Science in Data Science
Curriculum and Core Coursework
A master’s degree in data science consists of about 30 credits of coursework designed to prepare tomorrow’s data science leaders in area such as:
- Applied machine learning
- Data mining
- Experimental statistics
- File organization and data management
- Statistical sampling
Program Competencies and Objectives
Data science master degree programs prepare students to utilize the latest tools and analytical methods and to interpret and communicate their findings. A multidisciplinary curriculum draws on the social sciences, computer science, statistics, management, and the law.
Graduates of these programs are able to identify patterns in—and extract insights from—complex datasets. Their expertise lies in their ability to utilize the latest statistical and computational methods to make predictions, communicate their findings, and appreciate the ethical and legal considerations associated with working with real-world data.
Career Opportunities for Data Scientists in Pennsylvania with Advanced Degrees
The value of data scientists in Pennsylvania is reflected in the many job postings for these technically savvy professionals. Although the following job descriptions (sourced in March 2016) do not provide a guarantee or assurance of employment, they do highlight the abundance of job opportunities for today’s master’s prepared data scientist:
Data Scientist, Siemens: Mountain View, PA
- Collaborate with data engineers to ensure availability of data
- Define and drive requirements for adapters to enterprise data source
- Create and implement algorithms to process healthcare data for descriptive and predictive analytics
- Translate proof-of-concept analyses into scalable pipelines
- Skills in modern digital product design and delivery
- Design thinking and customer experience mapping
- Master’s degree in a relevant field, clinical experience a plus
- Strong working knowledge of medical imaging SW engineering
- Expert knowledge of data mining algorithms including decision trees, probability networks, associate rules, clustering, and neural networks
Data Scientist, Hershey: Hershey, PA
- Root cause analyses
- Growth opportunity identification via analytical modeling
- Master’s degree with a STEM focus strong preferred (majors could include data science, statistics, analytics, or a closely related field)
- At least eight years of experience in statistics, machine learning, information retrieval, or graph analyses
- At least four years of experience with small and large scale data mining technologies
- At least four years of experience in predictive analytics or statistical modeling, Bayesian modeling, Time Series modeling, Panel modeling, Marketing Mix modeling
- At least four years of experience with large data sets and big data/distributed computed platforms
Data Scientist, RJ Metrics: Philadelphia, PA
- Develop data models and analysis methods
- Using statistical techniques to design scalable solutions to business problems
- Analyze and extract insights from large, unstructured datasets
- Author whitepapers and case studies
- At least two years of professional experience, specifically in data modeling, statistical interference, and analysis
- Track record of learning new skills and putting them to use immediately
- Ability to communicate complex quantitative analysis and analytic approaches in a clear, precise, and actionable manner