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Business Analytics Certificate
About the MIT Sloan Business Analytics Certificate
Competency in analytics – the ability to ask the right questions, parse large quantities of structured and unstructured data, translate analytic insights into actions and influence key business decisions – is an essential skill needed to be successful within modern organizations. In response to the growing demand from students to dive deeper into analytics and data science, MIT Sloan launched the Business Analytics Certificate.
Benefits
Receive a Certificate in Business Analytics from MIT Sloan indicating your knowledge of analytic fundamentals.
Join a cohort and extended community of passionate and dedicated leaders, with access to alumni at multiple events throughout the year.
Tailor your education to your professional goals.
Open to All Current Graduate Students at MIT
The certificate is currently not available for MIT Undergraduate students and Cross Registered students. If you are not a current student, you can explore other certificate options below:
• All students (except for EMBA) must complete two core courses and a minimum of 30 elective units. At least two elective courses must be taken from different academic groups.
• EMBA students must complete two core courses and three elective courses. EMBA students should plan to count 15.734 (required for the EMBA program) as one of their three electives.
Please note: A course cannot be counted for both a core and elective. For example, if 15.071 is selected for the core, it cannot also count as an elective. Students may complete up to two Sloan certificates. Students who choose to complete two certificates may have a maximum overlap of two courses between the certificates.
Some courses may not be offered this academic year and/or may experience scheduling or unit changes. Consult the Sloan Course Browser for the most current scheduling information about Sloan subjects (those numbered 15.xxx), and visit the MIT Subject Listing & Schedule for up-to-date information about courses taught in other departments.
CORE REQUIREMENT All students must complete two core classes
—(Complete 1 of the below 4 courses)
Course Title
15.071 OR
The Analytics Edge
Fall
| 12 Cr.
The Analytics Edge
Fall
12 Cr.
The course is offered Fall and Spring
Develops models and tools of data analytics that are used to transform businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, criminal justice, the internet, and beyond. Covers analytics methods such as linear regression, logistic regression, classification trees, random forests, neural networks, text analytics, social network analysis, time series modeling, clustering, and optimization. Uses mostly R programming language with some Python. Includes team projects. Meets with 15.0711 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
15.727 OR
The Analytics Edge (EMBA Only)
Spring
| 9 Cr.
The Analytics Edge (EMBA Only)
Spring
9 Cr.
The Analytics Edge - EMBA 2nd Years
Introduces modern analytics methods (data mining and optimization), starting with real-world problems where analytics have made a material difference. Modern data mining methods include clustering, classification, logistic regression, CART, random forest methods, and association rules. Modern optimization methods include robust, adaptive and dynamic optimization. Applications include health care, hospital operations, finance, energy, security, internet, and demand modeling. Uses R programming language for data mining and ROME for robust optimization. Restricted to Exeuctive MBA students.
15.774 OR
The Analytics of Operations Management
Fall
| 12 Cr.
The Analytics of Operations Management
Fall
12 Cr.
Introduces core concepts and methods in data-driven modeling that inform and optimize decisions under uncertainty. Teaches modeling and computational skills (R and Julia). Covers topics such as time series forecasting, choice modeling, queuing theory, network models, dynamic programming, mixed-integer programming, stochastic optimization, matching algorithms, multi-armed bandits. Draws on real-world applications from retail, healthcare, logistics, supply chain, social and online networks, sports analytics, social applications, and online learning.
6.7900 formerly 6.867
Machine Learning
Fall
| 12 Cr.
Machine Learning
Fall
12 Cr.
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.3900 or other previous experience in machine learning. Enrollment may be limited.
—AND this course
Course No.
Course Title
Term
Credit
15.773 formerly 15.S04
Hands-On Deep Learning
Spring
| 6 Cr.
Hands-On Deep Learning
Spring
6 Cr.
Hands-On Deep Learning (Spring H3, 6 units, formerly 15.S04)
Fast-paced introduction to Deep Learning, the engine behind modern artificial intelligence, with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data. Topics include the basics of deep neural networks and how to set up and train them, convolutional networks to process images and videos, transformers for natural language processing, generative large language models (such as ChatGPT), and text-to-image models (such as MidJourney). Prior familiarity with Python and fundamental machine learning concepts (such as training/validation/testing, overfitting/underfitting, and regularization) required.
ELECTIVE REQUIREMENT
All students (except for EMBA) must complete a minimum of 30 elective units from the list below. At least two electives must be from different academic groups. SF MBA students should plan to count 15.778 (required for the SF program) as one of their electives.
EMBA students must complete three elective courses. EMBA students should plan to count 15.734 (required for the EMBA program) as one of their three electives.
* Sloan students should register for Course 15 subject number for courses offered jointly (J) with another MIT department.
—Behavioral Policy Sciences
Course Title
15.236
Global Business of Artificial Intelligence and Robotics (GBAIR)
Spring
| 6 Cr.
Global Business of Artificial Intelligence and Robotics (GBAIR)
Spring
6 Cr.
Spring H3
Discussion based-course examines applications of artificial intelligence and robotics in the business world. Emphasizes understanding the likely direction of technology and how it is likely to be used. Students examine particular applications to deepen their understanding of topical issues. Also focuses on how global economies will change in light of this wave of technology. Preference to Sloan graduate students.
15.669
Strategies for People Analytics
Fall
| 6 Cr.
Strategies for People Analytics
Fall
6 Cr.
Fall H1
Focuses on the strategies used to successfully design and implement people analytics in one's organization. Draws on the latest company practices, research projects, and case studies - all with the goal of helping students deepen their understanding of how people analytics can be applied in the real world. Covers the most important aspects of human resource management and people analytics. Demonstrates how to apply those basic tools and principles when hiring, evaluating and rewarding performance, managing careers, and implementing organizational change. No listeners.
—Managerial Communication
Course Title
15.285
Sports Strategy and Analytics
Spring
| 6 Cr.
Sports Strategy and Analytics
Spring
6 Cr.
Spring H4
Explores how leaders and organizations apply data and analytics to gain a competitive edge in the multibillion-dollar global sports industry. Provides context on the structure and dynamics of the sports industry, discusses best practices in data-driven decision making both on- and off-the-field, and improves students' skills in analyzing and communicating data. Assignments include a decision analysis paper and a final team project in which students apply their skills to solve a problem in sports.
15.286
Communicating with Data
Spring
| 6 Cr.
Communicating with Data
Spring
6 Cr.
Spring H3/H4
Focuses on structuring the oral and visual communication of data. Introduces these concepts and a methodology of self-reflection to help one accelerate his or her life-long learning process. Improves students' ability to develop strategic communications that use data to persuade others to take action. Primary focus is on reducing barriers to action by making data as easy as possible for others to absorb through clear structure, clear design, and clear delivery. Significant time will be devoted to practice. Students give and receive substantial feedback on their work.
15.721
Communication and Persuasion Through Data for Executives (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Communication and Persuasion Through Data for Executives (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
(Executive Elective)
Explains how to better convey complex, quantitative information to non-experts inside and outside of one's organization. Aims to improve skill set and teach tools that can be used to demonstrate to others how to be more effective. Specific skills covered include improving ability to create effective visuals for communicating quantitative information, maximizing audience comprehension when presenting data, and cultivating ability to communicate complex ideas in writing. Restricted to Executive MBA and Sloan Fellow MBA students.
—Economics
Course No.
Course Title
Term
Credit
15.034
Econometrics for Managers: Correlation & Causality in a Big Data World
Spring
| 9 Cr.
Econometrics for Managers: Correlation & Causality in a Big Data World
Spring
9 Cr.
Introduces econometrics as a framework to go beyond correlations and get to causality, which is crucial for investment decisions in finance, marketing, human resources, public policy, and general business strategy. Through labs and projects, students get experience in many relevant applications. Students gain a deeper understanding of modeling using multivariate regression, instrumental-variable regression, and machine learning tools including regression trees, random forest, LASSO, and neural networks. No prior knowledge is necessary. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
—Finance
Course Title
15.450 OR 15.457
Analytics of Finance OR Advanced Analytics of Finance
Spring
| 12 Cr.
Analytics of Finance OR Advanced Analytics of Finance
Spring
12 Cr.
15.450 Analytics of Finance
Introduces a set of modern analytical tools that specifically target finance applications. Topics include statistical inference, financial time series, event study analysis, and basic machine learning techniques for forecasting. Focuses on how to apply these tools for financial and macro forecasting, quantitative trading, risk management, and fintech innovations such as Kensho's "financial answer machine'' and big-data lending platforms.
OR
15.457 Advanced Analytics of Finance
Introduces a set of modern analytical tools that specifically target finance applications. Topics include statistical inference, financial time series, event study analysis, and machine learning techniques. Focuses on how to apply these tools for financial and macro forecasting, quantitative trading, risk management, and fintech innovations such as big-data lending and robo-advisors. 15.457 is a more advanced version of 15.450. Students with a solid background in statistics and proficiency in programming are encouraged to register for 15.457
15.456
Financial Engineering
Fall
| 9 Cr.
Financial Engineering
Fall
9 Cr.
Exposes students to the cutting edge of financial engineering. Includes a deep immersion into 'how things work,' where students develop and test sophisticated computational models and solve highly complex financial problems. Covers stochastic modeling, dynamic optimization, stochastic calculus and Monte Carlo simulation through topics such as dynamic asset pricing and investment management, market equilibrium and portfolio choice with frictions and constraints, and risk management. Assumes solid undergraduate-level background in calculus, probability, statistics, and programming and includes a substantial coding component. Students are encouraged but not required to use R for coursework.
15.458
Financial Data Science and Computing
Fall
| 9 Cr.
Financial Data Science and Computing
Fall
9 Cr.
Covers methods of managing data and extracting insights from real-world financial sources. Topics include machine learning, natural language processing, predictive analytics, regression methods, and time series analysis. Applications include algorithmic trading, portfolio risk management, high-frequency market microstructure, and option pricing. Studies major sources of financial data, raw data cleaning, data visualization, and data architecture. Provides instruction in tools used in the financial industry to process massive data sets, including SQL, relational and multidimensional databases. Emphasizes computer implementations throughout.
15.481J/6.9350J (Formerly 6.935J)
Financial Market Dynamics and Human Behavior
Spring
| 9 Cr.
Financial Market Dynamics and Human Behavior
Spring
9 Cr.
Drawing on the latest research in psychology, evolutionary biology, neuroscience, and artificial intelligence, as well as in behavioral and mainstream financial economics, provides new perspectives and insights into the role that human behavior plays in the business environment and the dynamics of financial markets and institutions. Incorporates practical applications from several industries including finance, insurance, biotechnology, pharmaceuticals, and government policy. Students apply ideas from this perspective to formulate original hypotheses regarding new career opportunities and disruptive technologies in their industry of choice.
15.S08
SSIM: Advanced Data Analytics and Machine Learning in Finance
Fall
| 9 Cr.
SSIM: Advanced Data Analytics and Machine Learning in Finance
Fall
9 Cr.
(Fall 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024)
Opportunity for group study by graduate students on current topics related to management not otherwise included in curriculum.
—Information Technology
Course Title
15.561
Information Technology Essentials - Foundations of Digitization
Fall
| 9 Cr.
Information Technology Essentials - Foundations of Digitization
Fall
9 Cr.
Emphasizes programming in scripting languages (e.g., Python, R, spreadsheet) within the context of emerging trends underlying current and future uses of information technology (IT) in business. Provides a solid grasp of programming basics and foundations of computing. Other topics include web technologies, database systems, digital experimentation, crowdsourcing, and machine learning.
15.572
Analytics Lab: Action Learning Seminar on Analytics, Machine Learning, and the Digital Economy (application required)
Fall
| 9 Cr.
Analytics Lab: Action Learning Seminar on Analytics, Machine Learning, and the Digital Economy (application required)
Fall
9 Cr.
Student teams design and deliver a project based on the use of analytics, machine learning, large data sets, or other digital innovations to create or transform a business or other organization. Teams may be paired up with an organization or propose their own ideas and sites for the project. Culminates with presentation of results to an audience that includes IT experts, entrepreneurs, and executives.
15.S54
Leadership in the Age of Generative Al (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Leadership in the Age of Generative Al (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
This course is designed to equip managers and founders with the tools needed to excel in a future of generative AI. Students are expected to participate in seminar-style discussion, hands-on labs, and a series of lectures. This isn’t a technical course, but it’s intended to give students an understanding for those managing developer teams all the way to the executive level. Topics covered include LLMs, image generation, emerging generative AI use cases, the business, economic, and strategic implications of generative AI, prompt engineering, fine tuning, business case analysis, and the VC / startup landscape.
15.S68
SSIM: Generative AI for Managers
Spring
| 6 Cr.
SSIM: Generative AI for Managers
Spring
6 Cr.
Spring, 2024 (H3)
SSIM: Generative AI for Managers
Group study of current topics related to management not otherwise included in curriculum.
—Law
Course Title
15.622
The Law of AI, Big Data & Social Media
Fall
| 6 Cr.
The Law of AI, Big Data & Social Media
Fall
6 Cr.
Fall and Spring
Focuses on the emerging legal framework of cutting-edge digital technologies, including AI/machine learning, big data and analytics, blockchain, the internet, and social media. Considers the law's impact on the development and application of these technologies, and the legal response to beneficial and mischievous impacts. Topics include law-sensitive aspects of privacy and bias, fintech, fair competition and fair dealing in digital markets, political discourse on social media, digital technologies in the workplace, and intellectual property rights in software and other innovations. Gives special attention to the legal concerns of those planning careers built on cutting-edge skills, and of managers and entrepreneurs bringing innovations from ideas to impact. How to find and make good use of legal advice. Meets with 15.6221 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version.
15.S53
Al, Big Data, and the Law (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Al, Big Data, and the Law (EMBA & SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
This course will focus on the legal response to advances in AI, data analytics, and their expanding use in platforms and other business contexts. We will focus upon major law sensitive “hot spots,” including IP rights in innovations; privacy & cybersecurity; algorithmic bias; and heavily regulated or liability sensitive sectors, such as fintech and healthcare. We will consider the perspectives of individuals navigating careers built upon cutting-edge analytic skills; entrepreneurs bringing innovations to market; and managers deciding on adoption or non-adoption and the attendant risks. Special attention will be given to the rapidly emerging demands upon boards of directors – and those advising boards — to develop appropriate standards of governance and oversight. The format will be a mix of lectures and discussion, top-notch guest lawyers, and student engagement around realistic scenarios. The focus will be on practical advice and solutions.
—Marketing
Course Title
15.570
Digital Marketing and Social Media Analytics
Fall
| 6 Cr.
Digital Marketing and Social Media Analytics
Fall
6 Cr.
Provides a detailed, applied perspective on the theory and practice of digital marketing and social media analytics in the age of big data. Covers concepts such as the difference between earned and paid media, predictive modeling for ad targeting and customer relationship management, measuring and managing product virality, viral product design, native advertising, and engaging the multichannel experience. Stresses the theory and practice of randomized experimentation, AB testing and the importance of causal inference for marketing strategy. Combines lectures, case studies, and guest speakers with relevant industry experience that speak directly to the topics at hand.
15.818
Pricing
Fall
| 6 Cr.
Pricing
Fall
6 Cr.
Fall H1
Framework for understanding pricing strategies and analytics, with emphasis on entrepreneurial pricing. Topics include economic value analysis, elasticities, customization, complementary products, pricing in platform markets, and anticipating competitive responses.
15.819
Marketing and Product Analytics
Spring
| 9 Cr.
Marketing and Product Analytics
Spring
9 Cr.
Uses quantitative data to inform, make, and automate marketing decisions, including growth marketing, product design, pricing and promotions, advertising, and customer retention. Topics include creating metrics, randomized experiments, models for targeting, network effects, and analyzing launches. Features lectures, industry examples and guests, and data analysis assignments supported by in-class labs. Draws inspiration from the internet industry, but applications span many industries.
—Systems Dynamics
Course Title
15.737
Advanced System Dynamics (EMBA and SF only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Advanced System Dynamics (EMBA and SF only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
Workshops focus on two models: the dynamics of service quality within a firm; and industry dynamics (particularly investment cycles and bubbles), including the energy and housing markets. Emphasis on formulation, analysis, use, and decision-making. Develops modeling skills. Restricted to Executive MBA and Sloan Fellows students.
15.873
System Dynamics for Business and Policy
Fall
| 9 Cr.
System Dynamics for Business and Policy
Fall
9 Cr.
Fall or Spring
Focuses on developing the skills and tools needed to successfully apply systems thinking and simulation modeling in diverse real-world settings, including growth strategy, management of technology, operations, public policy, product development, supply chains, forecasting, project management, process improvement, service operations, and platform-based businesses, among others. Uses simulation models, management flight simulators, and case studies to deepen conceptual and modeling skills beyond what is introduced in 15.871. Exploring case studies of successful applications, students develop proficiency in how to use qualitative and quantitative data to formulate and test models, and how to work effectively with senior executives to successfully implement change. Prepares students for further work in the field. Meets with 15.871 in first half of term when offered concurrently. Students taking 15.871 complete additional assignments.
—Technological Innovation, Entrepreneurship, and Strategic Management
Course No.
Course Title
Term
Credit
15.376J
MAS.664J
AI for Impact: Solving Societal-Scale Problems
Spring
| 9 Cr.
AI for Impact: Solving Societal-Scale Problems
Spring
9 Cr.
MAS.664J
Seminar promotes internal and external entrepreneurship, based on artificial intelligence (AI) technologies, to increase understanding of how digital innovations grow into societal change. Cases illustrate examples of both successful and failed businesses, as well as difficulties in deploying and diffusing products. Explores a range of business models and opportunities enabled by emerging AI innovations. Students craft a business analysis for one of the featured technology innovations. Past analyses have become the basis for research publications, and new ventures. Particular focus on AI and big data, mobile, and the use of personal data.
—Operations Management
Course Title
15.731
Risk Management (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Risk Management (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
Provides several core analytical and management concepts, helping students identify, model, think about, analyze, and manage risk. Topics vary; examples include risk measures, the drivers-event-outcomes framework, low-probability high-impact risk events, hedging risk with financial options, real options, risk management in the supply chain, project risk management, modern portfolio management, systemic risk. Restricted to Executive MBA and Sloan Fellows students.
15.734 (EMBA only) OR 15.778 (SF MBA only)
Introduction to Operations Management
Summer
| 9 Cr.
Introduction to Operations Management
Summer
9 Cr.
15.734 Introduction to Operations Management
Provides concepts, techniques and tools to design, analyze and improve core strategic operational capabilities. Covers a broad range of application domains and industries, such as high-tech, financial services, insurance, automotive, health care, retail, fashion, and manufacturing. Emphasizes the effects of uncertainty in business decision making and the interplay between strategic and financial objectives and operational capabilities. Students play simulation games that demonstrate some of the central concepts. Restricted to Executive MBA students.
15.778 Introduction to Operations Management
Integrated approach to the analysis, design and management of supply networks for products and services. Provides a framework for analysis, design and operation of supply chains (SCs) that relies on fundamental concepts, such as the management of inventory, and operations and logistics planning. Discusses the value of (timely) information and of the need for collaboration and coordination between SC players. Also presents conceptual frameworks that focus on the emergence of a wide range of enabling services that are critical to the survival and growth of this class of system. Includes study and discussion of concepts, examples, and case studies from a wide range of industries. Guest speakers present personal experiences on various aspects of the service industry and supply chains. Restricted to Sloan Fellow MBAs.
15.761
Introduction to Operations Management
Fall
| 9 Cr.
Introduction to Operations Management
Fall
9 Cr.
Fall or Spring
Credit cannot also be received for 15.734, 15.778.
Imparts concepts, techniques, and tools to design, analyze, and improve core operational capabilities and apply them to a broad range of domains and industries. Emphasizes the effect of uncertainty in decision-making, as well as the interplay among high-level financial objectives, operational capabilities, and people and organizational issues. Covers topics in capacity analysis, process design, process and business innovation, inventory management, risk pooling, supply chain coordination, sustainable operations, quality management, operational risk management, pricing and revenue management. Underscores how these topics are integrated with different functions of the firm. Case studies and simulation games provide experience in applying central concepts and techniques to solve real-world business challenges. Meets with 15.7611 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details. Summer section is primarily for Leaders for Global Operations students.
15.762J
1.273J/IDS.735J
Supply Chain Analytics
Spring
| 12 Cr.
Supply Chain Analytics
Spring
12 Cr.
1.273J/IDS.735J
Focuses on effective supply chain strategies for companies that operate globally, with emphasis on how to plan and integrate supply chain components into a coordinated system. Students are exposed to concepts and models important in supply chain planning with emphasis on key tradeoffs and phenomena. Introduces and utilizes key tactics such as risk pooling and inventory placement, integrated planning and collaboration, and information sharing. Lectures, computer exercises, and case discussions introduce various models and methods for supply chain analysis and optimization.
15.763J
1.274J/IDS.736J
Supply Chain: Capacity Analytics - Not Offered in AY 23-24
Spring
| 6 Cr.
Supply Chain: Capacity Analytics - Not Offered in AY 23-24
Spring
6 Cr.
1.274J/IDS.736J
Spring H4
Focuses on decision making for system design, as it arises in manufacturing systems and supply chains. Students exposed to frameworks and models for structuring the key issues and trade-offs. Presents and discusses new opportunities, issues and concepts introduced by the internet and e-commerce. Introduces various models, methods and software tools for logistics network design, capacity planning and flexibility, make-buy, and integration with product development. Industry applications and cases illustrate concepts and challenges. Recommended for Operations Management concentrators. Second half-term subject.
15.764
1.271J/IDS.250J
The Theory of Operations Management
Spring
| 12 Cr.
The Theory of Operations Management
Spring
12 Cr.
1.271J/IDS.250J
Provides mathematical foundations underlying the theory of operations management. Covers both classic and state-of-the-art results in various application domains, including inventory management, supply chain management and logistics, behavioral operations, healthcare management, service industries, pricing and revenue management, and auctions. Studies a wide range of mathematical and analytical techniques, such as dynamic programming, stochastic orders, principal-agent models and contract design, behavioral and experimental economics, algorithms and approximations, data-driven and learning models, and mechanism design. Also provides practical experience in how to apply the theoretical models to solve OM problems in business settings. Specific topics vary from year to year.
15.770J
1.260J/IDS.730J/SCM.260J
Logistics Systems
Fall
| 12 Cr.
Logistics Systems
Fall
12 Cr.
1.260J/IDS.730J/SCM.260J
Provides an introduction to supply chain management from both analytical and practical perspectives. Taking a unified approach, students develop a framework for making intelligent decisions within the supply chain. Covers key logistics functions, such as demand planning, procurement, inventory theory and control, transportation planning and execution, reverse logistics, and flexible contracting. Explores concepts such as postponement, portfolio management, and dual sourcing. Emphasizes skills necessary to recognize and manage risk, analyze various tradeoffs, and model logistics systems. SCM.271 meets with SCM.260, but has fewer assignments.
15.774
Analytics of Operations Management
Fall
| 12 Cr.
Analytics of Operations Management
Fall
12 Cr.
Introduces core concepts and methods in data-driven modeling that inform and optimize decisions under uncertainty. Teaches modeling and computational skills (R and Julia). Covers topics such as time series forecasting, choice modeling, queuing theory, network models, dynamic programming, mixed-integer programming, stochastic optimization, matching algorithms, multi-armed bandits. Draws on real-world applications from retail, healthcare, logistics, supply chain, social and online networks, sports analytics, social applications, and online learning.
15.785 OR 15.786
Product Management OR Digital Project Management with Lab
Spring
| 6 Cr.
Product Management OR Digital Project Management with Lab
Spring
6 Cr.
Digital Product Management Spring H3
Introduction to product management with an emphasis on its role within technology-driven enterprises. Topics include opportunity discovery, product-technology roadmapping, product development processes, go-to-market strategies, product launch, lifecycle management, and the central role of the product manager in each activity. Exercises and assignments utilize common digital tools, such as storyboarding, wireframe mock-ups, and A/B testing. Intended for students seeking a role in a product management team or to contribute to product management in a new enterprise.
OR
Digital Project Management with Lab (IAP into Spring H3, 6 units in IAP, 6 units in Spring)
Adds an action learning component to 15.785. Students are matched with partner companies and contribute (over IAP) to a PM-related project at the company. Students must register for both IAP and spring to receive credit and participate in the company project.
15.S07
SSIM: Real-time Analytics for Digital Platforms
Spring
| 6 Cr.
SSIM: Real-time Analytics for Digital Platforms
Spring
6 Cr.
Spring, 2024 (H3)
SSIM: Real-time Analytics for Digital Platforms
Opportunity for group study by graduate students on current topics related to management not otherwise included in curriculum.
SCM.256
Data Science and Machine Learning for Supply Chain Management
Spring
| 12 Cr.
Data Science and Machine Learning for Supply Chain Management
Spring
12 Cr.
Introduces data science and machine learning topics in both theory and application. Data science topics include database and API connections, data preparation and manipulation, and data structures. Machine learning topics include model fitting, tuning and prediction, end-to-end problem solving, feature engineering and feature selection, overfitting, generalization, classification, regression, neural networks, dimensionality reduction and clustering. Covers software packages for statistical analysis, data visualization and machine learning. Introduces best practices related to source control, system architecture, cloud computing frameworks and modules, security, emerging financial technologies and software process. Applies teaching examples to logistics, transportation, and supply chain problems. Enrollment limited.
—Operations Research and Statistics
Course Title
15.062J
IDS.145J
Data Mining: Finding the Models and Predictions that Create Value
Spring
| 6 Cr.
Data Mining: Finding the Models and Predictions that Create Value
Spring
6 Cr.
IDS.145J
Spring 2023
Introduction to data mining, data science, and machine learning for recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, medical databases, etc. Topics include logistic regression, association rules, tree-structured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software (R and Python). Grading based on homework, cases, and a term project. Expectations and evaluation criteria differ for students taking undergraduate version; consult syllabus or instructor for specific details.
15.068
Statistical Consulting
Spring
| 9 Cr.
Statistical Consulting
Spring
9 Cr.
Addresses statistical issues as a consultant would face them: deciphering the client's question; finding appropriate data; performing a viable analysis; and presenting the results in compelling ways. Real-life cases and examples.
15.071 OR 15.072
Analytics Edge OR Advanced Analytics Edge
Spring
| 12 Cr.
Analytics Edge OR Advanced Analytics Edge
Spring
12 Cr.
15.071 Analytics Edge
Spring or Fall
Develops models and tools of data analytics that are used to transform businesses and industries, using examples and case studies in e-commerce, healthcare, social media, high technology, criminal justice, the internet, and beyond. Covers analytics methods such as linear regression, logistic regression, classification trees, random forests, neural networks, text analytics, social network analysis, time series modeling, clustering, and optimization. Uses mostly R programming language with some Python. Includes team projects. Meets with 15.0711 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
OR
15.072 Advanced Analytics Edge
Fall
More advanced version of 15.071 introduces core methods of business analytics, their algorithmic implementations and their applications to various domains of management and public policy. Spans descriptive analytics (e.g., clustering, dimensionality reduction), predictive analytics (e.g., linear/logistic regression, classification and regression trees, random forests, boosting deep learning) and prescriptive analytics (e.g., optimization). Presents analytics algorithms, and their implementations in data science. Includes case studies in e-commerce, transportation, energy, healthcare, social media, sports, the internet, and beyond. Uses the R and Julia programming languages. Includes team projects. Preference to Sloan Master of Business Analytics students.
15.077J
IDS.147J
Statistical Learning and Data science - Not Offered in AY 23-24
Spring
| 12 Cr.
Statistical Learning and Data science - Not Offered in AY 23-24
Spring
12 Cr.
IDS.147J
Advanced introduction to theory and application of statistics, data-mining and machine learning using techniques from management science, marketing, finance, consulting, and bioinformatics. Covers bootstrap theory of estimation, testing, nonparametric statistics, analysis of variance, experimental design, categorical data analysis, regression analysis, MCMC, and Bayesian methods. Focuses on data mining, supervised learning, and multivariate analysis. Topics chosen from logistic regression; principal components and dimension reduction; discrimination and classification analysis, trees (CART), partial least squares, nearest neighbors, regularized methods, support vector machines, boosting and bagging, clustering, independent component analysis, and nonparametric regression. Uses statistics software R, Python, and MATLAB. Grading based on homework, cases, and a term project.
15.083
Integer Optimization - Not Offered in AY 23-24
Spring
| 12 Cr.
Integer Optimization - Not Offered in AY 23-24
Spring
12 Cr.
In-depth treatment of the modern theory of integer programming and combinatorial optimization, emphasizing geometry, duality, and algorithms. Topics include formulating problems in integer variables, enhancement of formulations, ideal formulations, integer programming duality, linear and semidefinite relaxations, lattices and their applications, the geometry of integer programming, primal methods, cutting plane methods, connections with algebraic geometry, computational complexity, approximation algorithms, heuristic and enumerative algorithms, mixed integer programming and solutions of large-scale problems.
15.093J formerly 6.255J
6.7200J/IDS.200J
Optimization Methods
Fall
| 12 Cr.
Optimization Methods
Fall
12 Cr.
6.7200J/IDS.200J
Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. Emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.
15.094J
1.142J
Robust Modeling, Optimization & Computation
Spring
| 12 Cr.
Robust Modeling, Optimization & Computation
Spring
12 Cr.
1.142J
Introduces modern robust optimization, including theory, applications, and computation. Presents formulations and their connection to probability, information and risk theory for conic optimization (linear, second-order, and semidefinite cones) and integer optimization. Application domains include analysis and optimization of stochastic networks, optimal mechanism design, network information theory, transportation, pattern classification, structural and engineering design, and financial engineering. Students formulate and solve a problem aligned with their interests in a final project.
15.095
Machine Learning Under an Optimization Lens
Fall
| 12 Cr.
Machine Learning Under an Optimization Lens
Fall
12 Cr.
Develops algorithms for central problems in machine learning from a modern optimization perspective. Topics include sparse, convex, robust and median regression; an algorithmic framework for regression; optimal classification and regression trees, and their relationship with neural networks; how to transform predictive algorithms to prescriptive algorithms; optimal prescriptive trees; and robust classification. Also covers design of experiments, missing data imputations, mixture of Gaussian models, exact bootstrap, and sparse matrix estimation, including principal component analysis, factor analysis, inverse co-variance matrix estimation, and matrix completion.
15.729 formerly 15.S56
Leadership: Quantitative and Qualitative Approaches (LQ^2) - (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
Leadership: Quantitative and Qualitative Approaches (LQ^2) - (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
IAP
Uses interdisciplinary approaches and real-world examples to show how analytics inform organizational change. Takes into account the human and cultural components of organizations. Restricted to Executive MBA and Sloan Fellow MBA students.
15.S08
SSIM: Optimization of Energy Systems
Spring
| 12 Cr.
SSIM: Optimization of Energy Systems
Spring
12 Cr.
Spring 2022 and Spring 2024,
Opportunity for group study by graduate students on current topics related to management not otherwise included in curriculum.
15.S51
SSIM: Innovation Through Analytics and Sensing in Food and Agriculture Systems (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
| 3 Cr.
SSIM: Innovation Through Analytics and Sensing in Food and Agriculture Systems (EMBA and SF MBA only)
IAP
Independent activities period is four weeks in January for independent study.
3 Cr.
IAP 2023 and IAP 2024
Group study of current topics related to management not otherwise included in curriculum.
6.7900 formerly 6.867
Machine Learning
Fall
| 12 Cr.
Machine Learning
Fall
12 Cr.
Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.3900 or other previous experience in machine learning. Enrollment may be limited.
6.7930J
HST.956J
Machine Learning for Healthcare
Spring
| 12 Cr.
Machine Learning for Healthcare
Spring
12 Cr.
HST.956J
Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning and transfer learning. Guest lectures by clinicians from the Boston area, and projects with real clinical data, emphasize subtleties of working with clinical data and translating machine learning into clinical practice. Limited to 55.
6.8300
Advances in Computer Vision
Spring
| 12 Cr.
Advances in Computer Vision
Spring
12 Cr.
Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Covers image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.8390. Students taking graduate version complete additional assignments.
6.C51
Modeling with Machine Learning: from Algorithms to Applications
Spring
| 6 Cr.
Modeling with Machine Learning: from Algorithms to Applications
Spring
6 Cr.
NOTE: This course requires students to simultaneously complete a 6-unit interdisciplinary module, such as 1.C51, 2.C51, 3.C51, 6.S952,7.C51, 22.C51, or SCM.C51 (The interdisciplinary modules also count as certificate elective)
Focuses on modeling with machine learning methods with an eye towards applications in engineering and sciences. Introduction to modern machine learning methods, from supervised to unsupervised models, with an emphasis on newer neural approaches. Emphasis on the understanding of how and why the methods work from the point of view of modeling, and when they are applicable. Using concrete examples, covers formulation of machine learning tasks, adapting and extending methods to given problems, and how the methods can and should be evaluated. Students taking graduate version complete additional assignments. Students cannot receive credit without simultaneous completion of a 6-unit disciplinary module. Enrollment may be limited.
