Way back in 2015, the Idaho Department of Commerce partnered with the Idaho National Laboratory (INL) and the Center for Advanced Energy Studies (CAES) to officially form the Idaho Autonomous Systems Center of Excellence – a team designed to support research and development opportunities in Idaho through data-focused technologies and initiatives. By synthesizing information from massive data assets in areas ranging from agriculture to natural resource management, the Center aims to capitalize on the emerging opportunities in the autonomous systems field, which is expected to become an $89 billion industry in the next 15 years. That includes using GPS and GIS technologies to auto-steer systems in tractors for planting and harvesting – you guessed it, potatoes – with a lower human workload.
In Idaho’s private sector, Boise-based Micron Technology is continuing to push the boundaries of big data’s potential by delivering newer and better tools for data crunching and analysis. In early 2020, the company announced a partnership with automotive supplier Continental to use Micron’s deep learning accelerator to improve machine learning applications. Through a combination of innovative hardware and software packaging, Micron reduces power consumption and memory bandwidth to accelerate ML and other high-demand processing applications.
The common thread running through these government initiatives and technological advancements in the private sector are the master’s-educated data scientists working to improve our understanding of the world through data.
Preparing for a Master’s Degree in Data Science in Idaho
To prepare for a master’s degree program in data science, students are expected to hold a relevant undergraduate degree, achieve high scores on entrance exams and gain relevant experience that enables them to hit the ground running when they enter the program. As a confluence between the statistical and the programming worlds, data science programs insist that applicants have at least some relevant skills in one or the other before applying to prevent learning overload.
Undergraduate Degree and Master’s Prerequisite Courses
To be considered for admission to a master’s program in data science, applicants are typically expected to meet the following undergraduate requirements:
- Bachelor’s degree in a relevant quantitative field such as applied math, statistics, computer science, or engineering
- A course load that includes coverage of key disciplines like linear algebra, statistics, calculus I and II, programming languages, and quantitative methods
- Minimum GPA of 3.0
Preparing to Succeed on GRE/GMAT Exams
To earn top consideration for admission to master’s programs in data science, students typically have to score in the top 15% of the quantitative section of the GRE or GMAT. Many programs also consider strong scores in the Verbal and Writing sections of these exams, which can demonstrate an applicant’s communication skills; the best analysis isn’t much good if you can’t successfully share the results with decision-makers.
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 problem solving and data efficiency. To prepare for the GMAT, students may take practice exams through Veritas Prep and the Princeton Review.
Relevant Personal and Work Experience for Admissions
Applicants with strong professional backgrounds, particularly those who have demonstrated good communication skills and elite quantitative and analytical reasoning abilities, are given top consideration for admission to master’s programs in data science. Schools may consider the following when reviewing applications:
- Database administration proficiency
- Communication skills
- Total relevant work experience (five years is preferred)
- Programming proficiency in languages such as Java, C++, and Python
Many gain relevant experience while working in Idaho’s tech sector. A few examples of employers and jobs in Idaho that would help develop the proficiencies graduate schools are looking for include:
- Statistical analysis at On Semiconductor Corp
- Cyber security at Saint Alphonsus Regional Medical Center or Eastern Idaho Regional Medical Center
- Data management at Melaleuca, Inc.
Consider a Data Science Bootcamp to Prepare For Master’s Degrees or Jobs in the Industry
Not everyone can move straight out of their bachelor’s program to acceptance in a prestigious data science master’s degree. The competition is stiff and the entry requirements are high. You maximize your chances by standing out from the pack with experience or skills or both.
One great way to pick those up is by enlisting—er, enrolling—in a data science boot camp.
If you think it sounds tough, you’re right! Boot camps were designed as intensive, linear, hardcore dives into the essential elements of data science, stuffing a complete course of study into a period of weeks or months to give you all the practical skills you need to get a position in the field immediately. That means a lot of study and a lot of effort, working together with a cohort of fellow students on projects that are infused with real-world data and performed with techniques and tools that are at the cutting edge of the field today.
You can find bootcamps that are aimed at every level of data science expertise, from the entry-level to very advanced specializations that you haven’t even heard of yet. Most of them, particularly those aimed at entry-level students, also come with extensive career preparation or job placement services. That can include everything from giving you a resume checkup to extensive connections with actively hiring employers or job fairs and demo days.
You can expect to pick up skills in hot areas of data science including:
- Big Data analysis and storage, using tools such as Hadoop
- Basic programming skills in Python and R
- Specialist API and library training with Numpy and Matplotlib
- Data visualization training on tools like Tableau, D3.js and leaflet.js
- SQL and relational data stores
Bootcamps started out as in-person, nose-to-the-grindstone affairs, but you’re increasingly seeing new options that are better suited to people who are currently employed or studying in other degree programs. Those may be conducted primarily online and in a part-time, evening and weekend format.
Whether you use that as a way to prepare yourself for the demanding process of applying to a master’s program in data science, or as a springboard directly into the industry, you’ll find it to be a solid investment in your career.
Filling Skills Gaps Through MOOCs in Order to Meet Master’s Programs Admission Criteria
Professionals who are interested in pursuing a data science master’s may lack one or more of the minimum qualifications for admission into a graduate program. This is very common and programs are in place specifically designed to fill knowledge/skills gaps, whether in advanced math, statistics or programming. These professionals may be given the option to complete bridge programs or take massive open online courses (MOOCs) to fulfill their outstanding requirements.
Massive Open Online Courses (MOOCs), which consist of filmed lectures, problem sets, and student-professor communication, provide students with the opportunity to fill gaps in their education through flexible online formats. Just a few examples of relevant MOOCs for data scientists include:
- Data Science and Machine Learning Essentials
- Networks, Crowds and Markets
- Text Mining and Analytics
Data Science Bridge Courses are available as pre-master’s courses for students that have met requirements for enrollment and have been accepted by a graduate school, even though they may lack proficiency in a certain key area. Graduate schools offer these bridge courses in an effort to be more inclusive of highly qualified prospective students that may need just a course or two before being ready for graduate study.
- Fundamental Bridge Programs provide coursework in linear algebra, algorithms and analysis of algorithms, and data structures.
- Programming Bridge Programs provide coursework in the mandatory programming languages required to begin master’s-level courses.
Earning a Master’s Degree in Data Science in Idaho
By offering both curricular coursework and immersion experiences, master’s programs in data science equip students with the skills sought after by today’s top companies. With part-time and full-time options available, students can set their own pace and earn their degree in 18-30 months. Through accelerated learning formats, students may earn their degree in as little as 12 months.
Examples of master’s degrees in data science include:
- Data Mining and Applications Graduate Certificate
- Data Science Certificate
- Master of Science in Data Science (MSDS)
- Master of Science (MS) in Data Science
- Online Certificate in Data Science
- Graduate Certificate in Data Science
- Master of Information and Data Science (MIDS)
Idaho students often apply to accredited online programs to earn degrees like the Master of Science in Data Science (MSDS) or the Master of Information and Data Science (MIDS). The flexibility of these programs allow students to further their education without sacrificing their current career obligations.
Core Curriculum and Immersion
Master’s in data science programs offer diverse courses designed to equip students with relevant, in-demand tools for the professional world. Just some of the courses often found in these programs include:
- Experimental statistics
- Statistical sampling
- Visualization of data
- Ethics and law for data science
- Network and data security
- Applied regression and time series analysis
- Data mining
- Data research design and applications
- Machine learning and artificial intelligence
- File organization and database management
An important aspect of data science programs is the immersion experience, which consists of a collaborative project designed to simulate real-world applications of data science. Corporate recruiters often come to observe students during these immersion experiences, providing students with the opportunity to demonstrate their talent in a team-based setting and become professionally established before graduating.
Key Competencies and Objectives
Upon graduating from a master’s program in data science, students should be proficient in the following core competencies:
- Be able to work in teams to achieve specific goals
- Become familiar with hash algorithms, cyphers, and secure communications protocols
- Be able to conduct association mining and cluster analysis
- Be able to interpret and communicate results
- Develop innovative design and research methods
- Be able to run an analysis of survey data
- Be able to develop and conduct sophisticated data analyses
Career Opportunities in Idaho for Data Scientists with Advanced Degrees
The simultaneous growth of data science in Idaho’s public and private sectors will likely mean increased job opportunities for the state’s master’s-prepared data scientists in the coming years. According to the Idaho Department of Commerce, the solutions and strategies developed by the Idaho Autonomous Systems Center of Excellence will play a significant role in shaping the state’s economy, impacting industries and services such as:
- Disaster response
- Public safety
- Agriculture
- Environmental monitoring
The following job listings were taken from a survey of job vacancy announcements in Idaho and are shown for illustrative purposes only. They are not meant to represent job offers or provide any assurance of employment:
Data Scientist at Micron in Boise – The job would consist of driving strategy for data analysis and data warehousing systems across the company, as well as identifying and cleaning existing data sets.
Senior Manager Data Scientist, Machine Learning at Otsuka Pharmaceutical (Remote) – The professional would be expected to create innovative methodologies for data, build proof of concept systems, and establish strategic partnerships with technical leadership across functional areas.