Online Master's in Data Science for Jobs in Arizona

According to Forbes, by 2015 there was a need for 4.4 million data analysts worldwide. Robert Mitelstaedt, dean of the W. P. Carey School of Business in Arizona, said that companies have a high demand for data scientists “with a good mix of industrial engineering knowledge and command of supply chains for efficiency, cost savings and risk reduction” (Forbes, 2013). 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.

In 2011, the McKinsey Global Institute reported that big data had “swept into every industry and business function and are now an important factor of production, alongside labor and capital.” According to the report, the computer and electronic products and information sectors, finance and insurance, and government are the sectors benefitting most from the use of data science.

Data scientists in Arizona have the opportunity to work for technology staffing firms like Robert Half Technology in Peoria, which contracts data scientists to work with IT and other technology clients to design experiments, test hypotheses, and build models. Arizona’s data scientists are also found working with companies like Carvana, an online car market 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.

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 a variety of qualifications in related to past education, related employment experience and key proficiencies. Master’s programs have highly selective standards and require candidates to possess a diverse skill set, prerequisite courses, high scores on graduate entrance exams, and employment experience.

Undergraduate Degree and Master’s Prerequisite Courses

Applicants to data science master’s programs 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

Applicants are also expected to complete prerequisite courses before enrolling in the program. Required prerequisites usually include:

  • Statistics
  • Calculus I & II
  • Linear algebra
  • Programming

Master’s program applicants are generally expected 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.

Data science courses build off of certain fundamental concepts that enrolling students are expected to be familiar with, 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, master’s program applicants are typically expected to score in the top 15 percent on the quantitative section of the GRE or GMAT. In addition, admission departments place a particular emphasis on excellent verbal and writing scores, as data science professionals are expected to possess strong communication skills.

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 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

Admissions departments for data science master’s programs seek bachelor’s-prepared 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 the trends in transaction history, and write up narratives for company executives to forward to cooperating banking institutions.

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 Temple – 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.

Bridge Programs and Massive Open Online Course (MOOC) Options for Master’s Programs Applicants Who Do Not Meet Admission Criteria

Applicants to data science master’s programs are expected to possess a diverse array of qualifications. Master’s courses build on an established knowledge of mathematics and programming languages. Some applicants may not meet each requirement and choose to enroll in bridge programs or massive online open courses (MOOCs) to fill the gaps in their education or experience.

Bridge programs are offered through schools that house data science programs as a way for students that 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 designed to fill knowledge gaps before enrolling in a master’s program. They include problem modules, filmed lectures, and the opportunity to interact with professors and teaching assistants. There are many different online hosts for prospective data science graduate students that wish to fill gaps in their knowledge prior to enrolling in a master’s program.

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 require the student to visit campus for intensive classes that give them 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 an immersion 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

In Arizona, data scientists are found working in industries that range from finance and banking, to healthcare and biotechnology, to manufacturing and logistics, and beyond.

Fortune ranked Gilbert, Arizona as one of the top cities for job seekers in the US, as the city has an unemployment rate under 4%. Meanwhile, Business Insider ranked Chandler, Arizona as one of the 20 best cities for finding a job in 2016 due to high annual income paired with housing affordability.

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 in February 2016.

Data Scientist at Honeywell in PhoenixData 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 PhoenixSolutions 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.

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