Cloud Computing
2 Credits
Cloud computing is a transformative force in the development of technology solutions that meet business analytics needs. Firms no longer need to make significant capital investments in large-scale data centers that sit idle for extended periods of time. The cloud model offers flexible, scalable, and cost-efficient access to computing resources on a just-in-time basis. In this course, we will explore the applications of cloud computing to common business analytics problems. We will explore full-stack cloud solutions, including the use of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) technologies.
Conveying Visual Data Insights
2 Credits
A principal challenge for anyone working with ubiquitous data is communicating results of an analysis to stakeholders. This course teaches students the art of clear, effective, and engaging data presentation with attention to the business necessity of translating complex technical subjects into actionable insights for a lay audience. Students will harness the power of storytelling for the strategic benefit of an organization by turning a raw set of data into a compelling message that resonates with an intended audience.
Data Management
2 Credits
In this course, students will learn the fundamental skills of data management as the field relates to business analytics. As most business data resides within relational databases, we will spend a large portion of our time ensuring that students have the skills needed to formulate questions about data in the form of SQL queries that you may then execute against a relational database. In many cases, the data required to draw business insights resides not in a relational database but rather in text files, spreadsheets, web applications or other locations. Students will learn a process known as “data wrangling” that will help you use extract, transform and load (ETL) processes to obtain data from diverse sources, clean it and store it in a relational database using the R language. Students will also learn about the core components of corporate data governance programs, including the development of consistent data definitions, data quality standards and master data management (MDM). Finally, we will discuss the critical privacy issues that exist, even within the context of anonymized information, and the security issues associated with protecting the confidentiality, integrity and availability of enterprise data.
Data Wrangling
2 Credits
This course is about building core skills for visual analysis of complex data, and for developing visualizations that improve business decision makers’ capabilities to explore, understand, and act on information. The course introduces a conceptual framework for effective and insightful data exploration, and provides a hands-on introduction to use of visualization software.
Ethics of Data Analytics
1.5 Credits
As companies and government entities accumulate vast quantities of data regarding details aspects of economic transactions, social interactions, and other potentially sensitive information, we are likely to see more and more impact on our lives and on society in general. This course provides a survey of ethical issues that arise in the context of “big data” analytics in both the private and the public sectors, including issues related to identity, privacy, ownership of information, and both personal and corporate reputation. Students will get an overview of current practices, issues, and concerns.
Intro to Statistical Analysis
2 Credits
In this course students will learn the fundamental building blocks of statistical analysis, while continuing to develop programming skills. Topics such as probability distributions, estimation, hypothesis testing, and regression analysis will be covered. Throughout the course, students will be able to apply these concepts to real-world problems.
Machine Learning
2 Credits
This course will introduce the fundamental concepts of machine learning, with a focus on the most common techniques and applications used by practitioners for both supervised and unsupervised learning using R. Besides being introduced to techniques for data acquisition, preparation and exploration, students will learn how to choose an appropriate machine learning model, train the model, evaluate the model and draw meaningful insight from the model. The machine learning algorithms and methods covered in the course include clustering, association rules, logistic regression, probabilistic (bayesian) classification, black box methods such as artificial neural networks and support vector machines, decision trees and several ensemble methods.
Python for Data Analysis
2 Credits
This course introduces students to Python, a widely-used programming language among data professionals, with the goal of cleaning, modeling, transforming, and analyzing data. Students will learn the fundamentals of programming, use Python packages for acquiring data from various sources, and learn skills to slice and dice the data and produce data visualizations. They will gain experience in Python and apply these skills in generating reproducible reports in business contexts. This course also prepares them for more advanced data science and machine learning methods.
Time-Series Analysis
2 Credits
Introduction to the use of statistical analysis of time series data, building on previous courses in statistics techniques to build and deploy forecasting models. Emphasis will be placed on analysis of economic data relevant to a range of problems in business and finance.
Unstructured Data Analytics
2 Credits
This course will introduce the fundamental concepts of machine learning, with a focus on the most common techniques and applications used by practitioners for both supervised and unsupervised learning using R. Besides being introduced to techniques for data acquisition, preparation and exploration, students will learn how to choose an appropriate machine learning model, train the model, evaluate the model and draw meaningful insight from the results. The machine learning algorithms and methods covered in the course include logistic regression methods, bayesian classification, neural networks, support vector machines, random forest classification, clustering, association rules and ensemble techniques. The course will also briefly explore text mining for sentiment analysis as well as the emerging area of deep learning and how it is driving advances in artificial intelligence that are helping change the world.