Masters in Business Analytics course descriptions

Core courses form the backbone of your Masters in Business Analytics curriculum. You will study them throughout Fall/Winter. The classes have been carefully designed and sequenced to build broad exposure to key aspects of business analytics.

Quantitative Methods

You will mainly focus on solving business problems with the use of data and
will gain an understanding of how the utilization of modern computational techniques can lead to better decisions by managers and entrepreneurs. Learn about the various statistical methods and analysis techniques with a lecture and case-based instruction. You’ll gain skills for quantitative reasoning, charting categorical data, depicting numerical data, plotting relationships, and finding association, correlation, and causation among variables. You’ll also explore the use of statistical and quantitative methods to better balance business risks, forecast scenarios, and develop business recommendations.

Data Management & SQL

Develop a basic applied proficiency in using Structured Query Language, or SQL, which is a standard database management language widely used for manipulation
of data in relational databases and particularly helpful in retrieving data from their native environments. You will receive an overview of relational databases, following which you will be introduced to ways to select and summarize columns from database tables and to use basic comparison operators to combine multiple criteria. Additionally, the use of aggregate functions and sorting, grouping, and joins will also be introduced.

Introduction to Data Science: R

Learn about the principles and techniques of computer programming using R, and an introduction to a variety of numerical and computational problems. Topics include functions, recursion, loops, list comprehensions, and reading and writing files. You will also learn how to program in R as well as how to use R for effective data analysis. Explore the practical issues in statistical computing, including programming in R, reading data into R, and accessing R packages. Topics in statistical data analysis will provide working examples.

Big Data Analytics

Learn about the modern organizational information landscape built around big data and big data analytics. Rooted in the notion of evidence-based management, you will enhance your understanding of how successful data-driven businesses leverage the available data to enhance their competitive advantage by translating large volumes of multisource data
into decision-guiding insights. You will receive an overview of key data type and source differences including structured vs. unstructured, numeric vs. text, transactional vs. descriptive, and graph vs. symbolic. You will also explore the key data analytical approaches and methodologies, including hypothesis testing, descriptive, prescriptive, and predictive analytics, supervised and unsupervised machine learning, text mining, and deep learning.

Data Visualization & Storytelling 

The ultimate value of data is to help leaders make better decisions, which
calls for being able to quickly and unambiguously extract the key insights hidden in data. Data visualization techniques and tools enable us to summarize data details and patterns into graphically expressed representations, which lend themselves to actionable conclusions. The way we present data to stakeholders may be the difference between a well-informed or very misguided decision. You will cover the concepts involved in visualizing data for decision-makers, including visualization techniques, data structure, color theory, dashboarding tools, and presentation structure, as well as audience roles and learning styles. You’ll also learn to produce data-driven decision support using visual representation methods and techniques.

Introduction to Data Science: Python

Learn about the principles and techniques of computer programming using Python, a widely used general purpose language that is an ideal combination of power
and simplicity, and an introduction to a variety of numerical, computational, and computer science problems. Topics include importing websites, generating random numbers, the method of inverse transformations, and acceptance/rejection sampling. You will learn to use Python in a hands-on way through tutorials and weekly homework that challenges you to break down problems into manageable units. The emphasis is on practical implementation, not on computational aesthetics.

Data Modeling & Optimization

Learn about the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, advanced network optimization, integer programming, and simulation. You will explore modeling methodology (linear, network, integer, and heuristics), modeling tools (sensitivity analysis), software, and applications
in finance, marketing, and operations management. You will study the mathematics underlying the optimization methods, and at the same time, considerable attention will also be paid to the business and applicability aspect of the subject.

Machine Learning

Explore the core theory and application of classification and clustering techniques, feature selection, and performance evaluation. You will be introduced to machine learning, data mining, and statistical pattern recognition. Topics include supervised and unsupervised learning as well as best practices in machine learning. You will also draw from numerous case studies and applications
to gain experience with application of the theory to key predictive and descriptive analytics problems in business intelligence.

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