The Best Courses for a Future in Data Analytics

Data analytics is the science of investigating and parsing through information, drawing actionable conclusions and using them to sway business decisions. Data analysts often use technical skills in computer science, statistics and mathematical modeling, as well as soft skills such as business strategy, ethics and communication. Data scientists are more than just data gurus—they are also strategists and storytellers. Careers in data analytics require particular combinations of specialized skills and, as a result, can be professionally and personally rewarding.

Professionals with the right education and expertise currently earn salaries aligned with these national averages:

• Data Scientist: $119,413²
• Machine Learning Scientist: $134,000³
• Data Warehouse Manager: $157,500⁴
• IT Data Architect: $159,443⁵

To become fluent in data analytics, students need to understand the language of business and data science. Rigorous course content will build upon students’ existing knowledge of statistics, algebra, Excel and computer programming so they may begin to discern patterns, communicate actionable insights, and sway business decisions in more strategic and profitable directions.

High-quality data analytics courses will teach students to:

• Query relational databases using SQL
• Write computer programs in Python and R
• Analyze data using statistical software
• Create data visualizations using Tableau
• Execute prescriptive optimization models
• Design and program heuristic algorithms in Python
• Extract meaning from big data sets and use it to persuade business leaders

Top data analytics courses are taught by highly qualified instructors who hold terminal degrees. They have expertise in solving real-world problems in the cybersecurity, IT, healthcare, marketing, manufacturing or telecommunication industries. The universities offering these courses are accredited by the Association to Advance Collegiate Schools of Business (AACSB).

The Online Master’s in Business Analytics (MSBA) program at William & Mary includes these courses, among others:

Competing Through Business Analytics
This course includes a survey of the state-of-the-art in business analytics, examining companies that have used business analytics for competitive advantage and how they have done so. It teaches business acumen and how the field of analytics fits within the context of business. Topics include business metrics as used for performance measurement and incentives, communicating with impact, visualization, and the functions of a company—how they interact, what data they have, and their development and deployment of algorithms. The course includes a survey of opportunities for problem solving using business analytics in operations, supply chain, human resources, finance and marketing, as well as an introduction to the tools that are covered in the remainder of this program.

Database Management and Visualization
This advanced course presents students with the fundamentals of database management systems, the principles and methodologies of database design and techniques for database application development. It is essential in business analytics to be skilled in two arenas: the first is understanding the structure, uses and means of access to data so data scientists or analysts can administer and maintain databases; the second is being able to interpret and visualize data for the purposes of decision-making in business. This course explores both arenas of business analytics, so students are prepared to bridge the gap between technical maintenance of data and the data’s actual value and message.

Topics include the fundamentals of database architecture, database management systems and data warehouse systems. Students apply the principles and methodologies of database design, use structured query language (SQL) to create databases, tables and indexes, and practice techniques for database application development in the first half of the course. The second half of the course focuses on data visualization, where students prepare charts, tables, graphics and dashboards. Software critical to a student’s success in the course includes ERDPlus, MySQL, Alteryx and Tableau. By the end of the course, students should understand the ETL process and feel prepared to begin the practice of business analytics.

Key skills taught: database management, data visualization, database architecture, data warehouse systems, structured query language, ETL process, database design, database application development, database administration, ERDPlus, MySQL, Alteryx, Tableau

Machine Learning
This largely hands-on course teaches predictive analytics techniques through working with data sets. Working predominantly in R, topics include preparing data, building models and analyzing the results. Many concepts and skills can also be applied to working in Python as well. This course prepares the future data scientist’s toolkit with a deep understanding of the theory and practice of regression and classification.

The focus is on data preparation in the initial stages of the course before progressing to building models and performing analysis from prepared data. Students learn how to determine the appropriate generalized linear model or models for a given data set, how to detect and handle outliers and missing values in prepared data and how to score data, which involves applying algorithmic models, built from one data set, to another data set in order to solve a business problem.

Key skills taught: machine learning, R programming, generalized linear models, problem-solving for business, detecting outliers and missing values, scoring data, predictive analytics techniques, preparing data sets, regression, classification

Optimization and Heuristics
Students are introduced to analytics methodologies involved in problem-solving for a given industry. This course in prescriptive analytics—using a data model to inform a recommended course of action—is central to the understanding of the analytics modeling process. Where optimization is about uncovering the best solution to a problem, heuristic solutions approximate the optimal solution. In other words, constructing heuristics can be more efficient and give an organization a competitive edge, but they also present difficulties of accurately representing real-world processes with a mathematical model.

Throughout the course, students encounter the theory and applications of optimization, including linear programming, non-linear programming, discrete optimization, and specialized networks. Students create optimization models using Excel with Solver in addition to R and Python with Gurobi. Emphasis in the course is placed on developing more advanced skills in programming and modeling for use with larger and larger data sets. Students gain the ability to integrate these programming packages with the MySQL, a database management system that aids the manipulation of larger data sets.

Key skills taught: Excel, Solver, R programming, Python, Gurobi, MySQL, prescriptive analytics, optimization models, linear and non-linear programming, discrete algorithms, heuristic solutions, interpreting models for business applications, problem-solving for optimization

The 100% Online MS in Business Analytics from “Public Ivy” William and Mary can enhance your earning potential and place you at the top of your field.

Find out how our Online MSBA curriculum can help you build the data science skills to analyze and brilliantly communicate valuable consumer insights that will enrich your career and boost your company's bottom line. For more information about our program and the application process, reach out to an Admissions Advisor today.