What you'll learn

  • The rigorous curriculum consists of entirely new courses, designed by Harvard faculty, that will help you build your capabilities in technical, analytical, and operational areas that can be used to advance your firm’s position in the global market.
  • Sharpen your core data analysis and management skills.
  • Explore emerging technologies and practices in next-generation analytics, such as blockchain, digital strategy, and AI/ML.
  • Learn how to interpret your findings and use them to uncover valuable business insights.
  • Our faculty will guide you in applying these concepts to your organization, with a mind toward maximizing efficiencies and outcomes.
  • Learn alongside data-driven professionals: 50% of students are director level or above and 65% Hold an advanced degree (master’s or above).

Course description

Unlike many other online offerings, the Harvard Business Analytics Program features a blended format with live online and in-person components.

The Harvard Business Analytics Program consists of six core courses, two seminars, and two in-person immersions. The program can be completed in as little as nine months.

Course outline

  • Digital Strategy and Innovation

    Through global case studies on market leaders and innovative startups in diverse industries, Professors Iansiti and Lakhani will examine the strategies and operational changes needed to make data analytics integral to your future success.

  • Programming and Data Science Systems

    Through a mix of technical instruction, discussion of case studies, and weekly programming projects, this course empowers participants to make technological decisions even if not technologists themselves. Topics include cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies.

  • Leadership, Innovation, and Change

    This course will focus on the leader’s role in both executing their current strategy better than their competitors as well as their role in shaping strategic innovation. We employ the congruence model that links strategy to execution through alignment of culture, people, tasks, structure, and executive leadership. Because ambidexterity requires leaders that can deal with punctuated change and paradoxical strategies, our course concludes with what we know about ambidextrous leadership and leading large system change.

  • Operations and Supply Chain Management

    This course emphasizes managing product availability, especially in a context of rapid product proliferation, short product life cycles, and global networks of suppliers and customers. Topics examined include inventory management, distribution economics, demand forecasting, and supplier management.

  • Foundations of Quantitative Analysis

    This course is an introduction to using statistical approaches to solve business problems. The main components of the course include methods for describing and summarizing data, the fundamentals of probability, the basics of study design and data collection, and statistical inference. Data analyses, simulation, and design issues are implemented in the statistical computing package R run within the RStudio interface.

  • Leadership and People Analytics

    Participants will build hands-on skills to analyze data in ways that complement the frameworks and intuitions they would normally use to guide their managerial actions on people issues. Anchored in data, this course will equip participants with an analytic approach to diagnosing the varied forces that influence individual, team, and organizational performance, leading to more effective interventions and actions.

  • Data Driven Marketing

    What used to be a qualitative and instinct-driven business function (think “Mad Men”) has now become a data-driven profession that relies on quantitative insights on how best to optimize ad creation and placement and influence consumer purchase behavior. This course will examine the ways in which marketing has changed and the new skills and capabilities needed to succeed in this function.

  • Data Science Pipeline and Critical Thinking

    This course will take a holistic approach to helping participants understand the key factors involved, from data collection to analysis to prediction and insight. Projects will give students hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

  • Immersions

    In addition to the online learning components of the program, you will attend two weekend-long, in-person immersions hosted on campus at Harvard Business School in Boston. Note: given COVID-19 restrictions, these immersions will take place virtually until further notice.

    During these in-person experiences, you will meet face-to-face with your classmates, network with faculty and industry leaders during nightly events, tour the Harvard campus, and participate in hands-on guided learning exercises. You will also use the Harvard Business School case method, formulating solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics of discussion have included reputation systems, data sharing and security, and organizational leadership.

Instructors

  • Charles Edward Wilson Professor of Business Administration, Harvard University
  • Edward W. Carter Professor of Business Administration and Chair of the General Management Program, Harvard Business School
  • Jesse Philips Professor of Manufacturing
  • An Wang Professor of Computer Science, Harvard University
  • Professor of the Practice in Statistics, Harvard University
  • Senior Lecturer on Statistics, Harvard University
  • Thomas D. Casserly, Jr. Professor of Business Administration; Chair, MBA Elective Curriculum
  • Senior Preceptor in Statistics, Harvard University
  • Senior Lecturer on Statistics, Harvard University
  • David Sarnoff Professor of Business Administration
  • Senior Lecturer on Computer Science, Harvard University.
  • Herchel Smith Professor of Computer Science, Harvard University
  • Assistant Professor of Business Administration
  • George F. Colony Professor of Computer Science
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