Online Masters in Data Analytics | WGU (2024)

Analytics is the creative use of data and statistical modeling to tell a compelling story that not only drives strategic action but also results in business value. The Data Analytics Journey uses the analytics life cycle to conceptualize the processes, tools, and techniques for implementing data analysis, data engineering, and analytics product management. Learners gain fluency in gathering requirements, asking business questions, establishing evaluation metrics, identifying communication models, and aligning the analytics project outcomes to business goals. It presents an overview of the various tracks offered in the program and the career options in these specializations.

Data Management builds proficiency in using both relational and non-relational databases. Topics include selection of a data storage architecture, data types, data structures, normalization and denormalization, and querying databases. Structured Query Language (SQL) topics including Data Definition Language (DDL) and Data Manipulation Language (DML) are covered, including joins, aggregations, and transactions. Non-relational approaches to organizing and querying data are contrasted with relational approaches to build competency in adapting data storage architectures to business needs.

Analytics Programming builds algorithmic thinking using both the Python and R programming languages. This course builds from the foundations of programming. Learners use libraries and packages to perform common analytics tasks, including acquiring, organizing, and manipulating datasets. The course also presents methods for applying statistical functions and graphical user interfaces to perform basic analysis and to present findings.

Data Preparation and Exploration applies analytical programming skills to the early steps of the data analytics life cycle. This course covers cleaning data to ensure the structure, accuracy, and quality of the data; interpretation of descriptive and inferential statistics as well as visualizations of data; and wrangling data to prepare it for further analysis. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. The following courses are prerequisites: The Data Analytics Journey, Data Management, and Analytics Programming.

Statistical Data Mining focuses on concepts in data preparation and supervised and unsupervised machine learning techniques. The course helps students gain basic knowledge in statistics, data preparation, regression, and dimensional reduction. Learners implement supervised models—specifically classification and prediction data mining models—to unearth relationships among variables that are not apparent with more surface-level techniques. The course also explains when, how, and why to use unsupervised models to best meet organizational needs. The following course is prerequisite: Data Preparation and Exploration.

Data Storytelling for Diverse Audiences focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps learners gain communication and storytelling skills in order to motivate change and answer business problems. It also covers data visualizations, audio representations, interactive dashboards, interpersonal communication, and presentation skills.

Deployment is the practice of operationalizing data analysis within a business environment. Given an analysis, learners determine the business functional and non-functional requirements for wider use and implement pipelines and functions to deploy analyses at scale. Topics including security, scalability, usability, and availability are discussed. Prerequisites for this course are Analytical Programming, Data Management, Data Preparation, and Statistical Data Mining.

Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course covers approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis.

Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite. 

Machine Learning is the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and that iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods covered include k-nearest neighbors, logistic regression, decision trees, and support vector machines. Unsupervised models covered include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. Prerequisites are Analytics Programming and Statistical Data Mining.

The Data Science Capstone integrates the learning in the MSDA core and the three courses within the specialization. The student evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs. Projects within this specialization include the design and construction of machine learning approaches, optimization, and/or advanced analytics techniques as the project requires.

Cloud Databases covers the application of cloud architectures to large-scale data systems. The differences between cloud-native approaches to data architectures and smaller scale systems are discussed and learners apply cloud computing concepts to address specific business scenarios.

The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.

Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.

Data Processing is the practice of automating data flow into and out of components of an analytics system and comprises a major part of the analytics life cycle in modern organizations. Data Processing covers concepts in Extract, Transform, and Load (ETL) pipeline operations on data at scale and variations of ETL (Extract, Transform, and Load) as a function of data repositories including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and learners implement pipeline solutions in cloud-native environments.

Processes form the core of any organization and involve both manual and automated steps. Business Process Engineering introduces how to identify processes, visualize them, and how to design and implement operational methods that promote organizations’ overall efficiency. The course covers common process engineering frameworks, the stages of process engineering present in common frameworks, and introduces tools used to conduct business process reengineering.

Decision Intelligence is a domain that optimizes decision-making by balancing technology, processes, and people. In this course students learn the core principles of Decision Intelligence, exploring the augmentation of decision processes with machine learning, comprehensive decision modeling, and the pivotal role of a “human-in-the-loop” design.  Students will navigate decision theories and multi-criteria decision analysis, gaining insight into how biases and heuristics influence decision outcomes. The course emphasizes framing decisions using causal decision diagrams (CDD), implementing decision intelligence, evaluating the outcome using key performance indicators and determining the return on investment of the change, and using change management techniques to help the organization adapt to new decision making strategies.

The Decision Process Engineering capstone integrates the learning in the MSDA core and the three courses within the specialization. The learner evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs.  Projects within this specialization include a project management plan, decision intelligence plan, or process engineering plan to deliver on the business need or opportunity.

Project Management is a thorough exploration of the inputs, tools, techniques, and outputs across the five process groups and 10 knowledge areas identified in the Project Management Body of Knowledge (PMBOK) Guide. The essential concepts and practical scenarios included enable students to build the competencies required to successfully complete the CAPM certification exam. There is no prerequisite for this course.