Core Masters in Business Analytics Courses

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.



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.

Sample 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.

Sample topics:

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

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.

Sample 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.

Sample topics:

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

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.

Sample 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.

Sample topics:

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

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.

Sample 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.

Sample topics:

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

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.

Sample 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.

Sample topics:

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

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.

Sample topics:

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



Download a brochure for an in-depth look at Hult's programs.