According to Dice’s 2020 tech job report, data engineer and data scientist were two out of the three fastest growing tech occupations in the US, with demand increasing up to 50% year-over-year for several years running. Glassdoor found more than 6,500 unfilled data scientist job openings that same year and rated it the third most-desired job in the United States. Additionally, Arizona State University reported that 85% of Fortune 500 companies with operations in the state have implemented big data initiatives, which has expanded the job market for data scientists.
Data scientists in Arizona have the opportunity to work for technology staffing firms like Robert Half Technology in Peoria and Phoenix, where contract analysts work with area technology companies to design experiments, test hypotheses, and build models for streamlining everything from product development and testing to the use of human capital within their organizations.
Arizona’s data scientists are also found working with companies like Carvana, a virtual used car dealer headquartered in Phoenix that hires data scientists to develop insights and isolate new marketing strategies based on datasets gained from used vehicle inspection and certification processes.
At the onset of the big data revolution, McKinsey Global Institute famously reported that data analytics had swept into every industry and business function to become as relevant to technology development, finance and insurance, and even government as labor and capital itself. Revisiting that analysis in 2016, the company found the value was still on the table, but still not fully leveraged because not enough data scientists were available to use it. Now’s your chance to ride the wave of demand and find your niche.
Preparing for a Master’s Degree in Data Science in Arizona
Bachelor’s-prepared professionals seeking a master’s degree in data science are expected to meet some pretty stiff requirements just to get into a graduate program. Data science master’s programs can be highly selective and will require you to have a diverse skill set, along with prerequisite courses, and a strong showing on graduate entrance exams.
Undergraduate Degree and Master’s Prerequisite Courses
Just earning a bachelor’s degree isn’t going to be enough to get you into most data science master’s programs. It’s a complex field with a huge demand, and programs put together entrance criteria accordingly. You must meet several minimum qualifications in order to be considered. These often include:
- Bachelor’s degree in a related field such as statistics, computer science, engineering, or applied math
- Minimum of 3.0 GPA during undergraduate studies
You are also expected to complete prerequisite courses before enrolling in the program. Required prerequisites usually include:
- Calculus I & II
- Linear algebra
Master’s program applicants need to demonstrate a knowledge of fundamental quantitative concepts, score in the 85th percentile on either the GRE or GMAT exam, and have at least 5-7 years of related work experience.
All of these qualifications are aimed at ensuring your familiarity with certain fundamental concepts that data science programs are rooted in, including:
- Data structures
- Algorithms and analysis of algorithms
- Linear algebra
Preparing for Success on the GRE/GMAT Exams
In order to be considered for admission, you’ll typically be expected to score in the top 15 percent on the quantitative section of the GRE or GMAT. Admission departments place a particular emphasis on excellent verbal and writing scores, as data science professionals are expected to be strong communicators.
The GRE exam’s quantitative section evaluates the candidate’s abilities in data analysis, arithmetic, algebra, and geometry. Topics especially relevant to data scientists include statistics, standard deviation, tables, graphs, and probabilities. The official GRE website offers sample questions and free practice exams.
The General Management Admissions Test (GMAT) also evaluates student’s quantitative skills and familiarity with data analysis. With 37 questions on the test involving problem solving and data efficiency, admissions departments expect an excellent quantitative score as well as high verbal and writing scores. GMAT practice exams are available through The Princeton Review and Veritas Prep.
Prior Work Experience
Academic preparation isn’t always enough, however. Many graduate programs want the kind of maturity and real-world familiarity with data analysis that you typically find in professionals who have 5-7 years of experience in data science or a related field. The following skills are generally expected of master’s program applicants:
- Strong communication skills
- Programming proficiency in languages such as JAVA, C++, and Python
- Coding skills
- Hacking skills
- Data mining ability
- Database administration proficiency
A few examples of the kinds of employers and entry-level positions that would satisfy work experience requirements for a master’s program in data science might include:
Entry Level Data Analyst at ConsultNet in Phoenix – Data Analyst works with a team to review suspicious banking transactions, analyze historical trends in transactions, and write up narratives for company executives to forward to banking institution clients.
Data Engineer at American Express in Phoenix – Data engineer will create data structures and databases, build and enhance the database structure, work with database analysts, administrators, and architects, and facilitate improvements across all company levels by presenting pertinent information to management.
Clinical Data Analyst at Statistics & Data Corporation in Tempe – Clinical data analyst will perform data entry, query management, and review of datasets, as well as assisting in database setup and maintaining quality control of the data as part of a larger data science team.
Prepare for a Master’s Program or Skip it Completely by Attending a Programming Bootcamp in Phoenix or Online
One relatively new option for building both your experiential and quantitative credentials is a data science bootcamp. These fast-paced, practical indoctrinations into the elements of statistics, analysis, databases, and data visualization tools can satisfy the most demanding master’s programs entry requirements. Alternatively, they can jump-start your career and accelerate you directly into the booming job market, all without ever enrolling in an advanced degree program.
A dedicated career team can help you land a job in that market if you decide not to continue to a master’s degree, with portfolio and resume-building support along the way.
That puts the UA program toward the entry-level tier as far as bootcamps go, but you can find them offering more focused or more advanced skills as well. Many have a variety of industry partnerships as well as career counseling to put in the direct-to-work pipelines in hot industries.
You won’t get college credit for the programs, and they do not teach general theory, only practical applications. With the kind of demand the field is experiencing today, that’s no drawback, however.
Bridge Programs and Massive Open Online Course (MOOC) Options for Master’s Program Applicants Who Do Not Meet Admission Criteria
If you are primarily interested in that strong theoretical base, however, and didn’t get it through your bachelor program or work experience, you have another path available. You can also enroll in bridge programs or massive online open courses (MOOCs) to fill the gaps.
Bridge programs are offered through schools that house data science programs as a way for students to meet all other enrollment criteria to fill knowledge gaps before beginning graduate level coursework. Bridge programs are generally offered in one of two areas:
- Fundamental bridge programs, including courses in linear algebra, algorithms and analysis of algorithms, and data structures
- Programming bridge programs, including essential programming languages such as Python, JAVA, and C++
MOOCs are online-hosted educational programs offered completely independent of graduate programs. MOOCs are usually more general courses, the same kind of collegiate curriculum you would find in statistics or math bachelor’s programs. They include problem modules, filmed lectures, and the opportunity to interact with professors and teaching assistants. There are many different online hosts for MOOCs, and many prestigious universities that offer them for free or at a greatly reduced cost.
Earning a Master’s Degree in Data Science in Arizona
Master’s programs in data science are offered in traditional on-campus programs through a handful of schools in Arizona. However, many working professionals prefer the flexibility of an online program. Online programs are widely respected by employers, and are offered in full-time, part-time, and accelerated options.
Most programs will require an immersion experience in the last semester that will see you visiting campus for intensive classes that give you the opportunity to network with professors and peers. Full-time options can be completed in 18 months, part-time programs can be completed in 32 months, and accelerated options can be completed in as little as 12 months.
Students may choose from several degree programs:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Graduate Certificate in Data Science
- Data Mining and Applications Graduate Certificate
- Online Certificate in Data Science
- Data Science Certificate
Curriculum Content and Core Coursework
Coursework within master’s programs will vary, but core courses will focus on essential skills required for data science positions. All programs will consist of a combination of the following topics:
- File organization and database management
- Applied regression and time series analysis
- Advanced managerial economics
- Quantifying materials
- Ethics and law for data science
- Network and data security
- Visualization of data
- Data storage and retrieval
- Experimental statistics
- Statistical sampling
- Machine learning and artificial intelligence
- Experiments and causal inference
- Data research design and applications
- Scaling data – macro and micro
- Data mining
- Information visualization
Data science programs often include a final capstone experience, which is a project spearheaded by a group of students, designed to give students hands-on experience with data science applications in a team environment.
Key Competencies and Objectives
Data science master’s programs will prepare students for the challenges of working in the field. After graduating, students will be proficient in the following core areas:
- Statistical sampling
- Data collection and analysis
- Research design
- Data mining and machine learning
- Communication and visualization
- Ethics, privacy, and relevant law
- Database management and file organization
- Data and network security
- Data cleansing
- Programming languages such as Python, GitHub, and SAS
- Database queries
Career Opportunities for Data Scientists in Arizona with Advanced Degrees
The IT sector in Arizona is expanding rapidly, and that means a lot of big data is being churned out. Data scientists are found working in industries that range from finance and banking, to healthcare and biotechnology, to manufacturing and logistics, and beyond.
AZCentral reported that Scottsdale, in the Phoenix metro area, was the number one destination for job seekers in the U.S. for 2019. Business Insider found the same thing, and also ranked Chandler, Arizona at number seven due to high median annual household income and the lowest number of residents living under the poverty line.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment. These examples were taken from a survey of job vacancy announcements for data scientists in Arizona.
Data Scientist at Honeywell in Phoenix – Data scientist will work to identify opportunities for new growth and expansion within the company through data-driven initiatives. This position would involve working closely with application architects to integrate results into supporting platforms and deliver contemporary analytics solutions for all Honeywell business groups.
Solutions Architect at Impetus Technologies in Phoenix – Solutions architect will develop proposals for implementation of big data architecture, as well as propose solutions within workshops for customers and clients. This position would involve designing projects dealing with company data and providing solutions to customers’ needs by designing applications for existing databases.