Masters in Business Analytics Courses


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You’ll study core courses through fall and winter on your home campus before elevating your learning with electives at any or all of Hult’s global campuses. Our Masters in Business Analytics is also available as a part-time program.

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Immersion


Your program begins with a total immersion in Hult’s uniquely practical approach to education. This is when you’ll be introduced to the Hult DNA and the mindsets that will help every student succeed. You’ll start your first core course and learn more about your program journey, yourself, and your classmates through intensive workshops, lectures, and challenges. You’ll also be introduced to your campus support teams, professors, career coaches, and mentors who will guide you through your journey at Hult.

Key topics

  • Growth Mindset & Hult DNA
  • Academic Toolkit
  • Setting Goals & Personal Development Plan

Introduction to Data Analytics


Understand how the utilization of traditional statistics and tools can lead to better decisions by managers and entrepreneurs and develop foundational key skills for many of the courses offered in the program.

Key topics

  • Spreadsheet Modeling & Data
  • Defining & Testing Hypotheses
  • Predicting Outcomes Using Regression
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Collaboration & Teamwork


Your ability to work with others is what will define your career success—perhaps more than any other skill. In this course, you’ll learn about the difference between groups and teams and explore the features of high-performing teams. Ultimately, you’ll leave this course armed with response strategies that drive and sustain productive team communication and lead to better outcomes.

Key topics

  • Conflict Resolution
  • Driving Productivity & Performance
  • Cross-Cultural Communication

Introduction to Python


Learn the principles of computer programming using Python, a widely used general-purpose language, plus an introduction to various numerical, computational, and computer science problems. Emphasis will be on practical implementation, not computational aesthetics.

Key topics

  • Importing, Cleaning & Manipulating Data
  • Decision Structures & Boolean Logic
  • Generating Graphical Output
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Advanced Data Analytics


Develop an applicable understanding of probability, inference, comparison, and regression techniques. Gain a strong understanding of the reasons behind running an analysis and learn to translate the results into actionable recommendations.

Key topics

  • Random Variables & Probability Models
  • Data for Comparisons
  • Multiple Regression Models

Text Analytics


Deep-dive into the principles and techniques of text analytics. Learn to analyze and construct text files, read and write in R and/or Python, and get to grips with Google APIs through working examples and exercises.

Key topics

  • Text Frequency Analysis & Plotting
  • Visualizing Text Data
  • Google API & Text APIs
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Data Visualization


Data visualization techniques enable us to summarize and maximize the value of data. Cover the concepts involved in visualizing data for decision makers, including data structure, color theory, and dashboarding tools, alongside audience roles and learning styles.

Key topics

  • Data Visualization Design
  • Choosing Charts
  • Analysis Using KPIs

Introduction to R


Study the principles and techniques of computer programming using R. Learn both how to program in R and its use for effective data analysis. Topics in statistical data analysis will provide working examples.

Key topics

  • Basic Data Mining in R
  • Frequency Histograms, Distributions & Visualization
  • Interactive Dashboard
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Data Management & SQL


Develop a basic applied proficiency in using Structured Query Language, or SQL—a standard database management language widely used for manipulation of data in relational databases and retrieving data from their native environments.

Key topics

  • Database Management Systems
  • Making Your Data Useful
  • Structured Query Language

Data Optimization


Learn about the theory, algorithms, and applications of optimization. You will explore modeling methodology, tools, software, and applications in finance, marketing, and operations management, as well as the mathematics underlying the optimization methods.

Key topics

  • Introduction to Optimization Models
  • Linear Programming Models
  • Decision Making in Uncertainty
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Machine Learning


Explore the core theory and application of classification and clustering techniques, feature selection, and performance evaluation. You’ll be introduced to machine learning, data mining, and statistical pattern recognition, using case studies to apply theory to key business intelligence problems.

Key topics

  • Supervised & Unsupervised Learning
  • Design & Analysis of Experiments
  • Business Applications
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Grading & Assessment

Hult’s grading system is intended to encourage and acknowledge academic achievement while promoting a growth mindset. The system is based on principles of fairness and transparency, and faculty award grades based upon established criteria that you’ll find in your course syllabus. Course grades at Hult usually involve a combination of assessments such as case analysis, examinations, presentations, and other types of team and individual assessments.

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