This in-person graduate degree program—offered in a full- or part-time format—will give students thorough knowledge of techniques in the field of analytics, and the ability to apply them to real-world scenarios in the business world.
About the Program
Building from a core in applied statistics, the MS in Analytics provides students with advanced analytical training to develop their ability to draw insights from big data, including: data collection, preparation and integration; statistical methods and modeling; and other sophisticated techniques for analyzing complex data. The program is highly applied in nature, integrating business strategy, project-based learning, simulations, case studies, and specific electives addressing the analytical needs of various industry sectors. Through partnerships with key employers, the program also provides students with applied projects and data sets as well as access to career networks and employment pathways upon graduation.
The MSc in Analytics is a full- or part-time program designed for adult learners who are working professionals. All courses are taught weekday evenings and on Saturday mornings at the University of Chicago’s Gleacher Center in downtown Chicago. The curriculum consists of eleven (11) courses, plus a capstone project. Time to degree completion may range between twelve (12) months to four (4) years.
The program accepts applicants twice annually, offering students the ability to enter the program in the spring and autumn quarters. Applications may be submitted year round; deadlines apply.
11 Courses, plus a Capstone Project:
Foundation Courses (7 courses)
- MSCA 31001 Research Design for Business Applications
- Prerequisites: MSCA 31007 Statistical Analysis, or concurrent enrollment
This course covers the translation of business problems into researchable issues, development of data sources which can address each key research able issue, and the initial translation of research results back to business implications. At the end of the course, students will be able to: frame a business problem, map alternative solutions, and communicate the plan back to a non-technical manager; identify potential sources or relevant data, explain the pros and cons of the selected methodology to the analytical team as well as non-analysts; understand analytical principles that can be applied to design data-gathering experiments; apply statistical tools from Statistical Analysis, including descriptive statistics, regression and ANOVA, categorical data analysis, and clustering methods. (Instructor: John Watts, Sema Barlas)
- MSCA 31007 Statistical Analysis
- Prerequisites: MSCA 31000 Introduction to Statistical Analysis or college level statistics
This course provides a comprehensive and practical introduction to statistical and data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real-world problems based on data-driven decisions. Throughout the course, students will learn concepts and fundamentals of statistical inference and regression analysis by studying theory, developing intuition, and working through several practical examples. Students will become proficient in interpreting standard regression output and conducting model selection and validation. Students will also learn the statistical programming language used to construct examples and homework exercises. Examples will be constructed using SAS or R. Students will have many opportunities to apply the new concepts to real data and develop their own statistical routines. The course also addresses the importance of quality control and reproducibility when conducting research and developing work product. (Instructors: Jonathan Gemmell, Francisco Azeredo, Ming-Long Lam,Yuri Basalanov, Sema Barlas, Veena Mendriatta)
- MSCA 31005 Database Design and Implementation
- A fundamentally sound database design and implementation is typically a key building block for a successful analytics initiative. This course provides students with a thorough grounding in the fundamentals of good database design, for both operational and reporting data, while also enabling them to gain experience with pragmatic aspects of implementing databases as part of bringing analytics solutions to fruition in typical organizational situations of constrained resources and knowledge limitations. Because achieving a good quality database for analytics solutions requires knowledge of the application domain as well as database design principles, students will have the opportunity to construct databases from real-life data for use by various types of analytics applications. By the conclusion of the course, students will be able to design and build databases capable of supporting sustainable analytics solutions. (Instructor: Roger Teal, Levita Goodwin).
- MSCA 31006 Time Series and Forecasting
- Prerequisites: MSCA 31007 Statistical Analysis
Time Series Analysis is a science as well as the art of making rational predictions based on previous records. It is widely used in various fields in today’s business settings. For example, airline companies employ time series to predict traffic volume and schedule flights, financial agencies measure market risk via stock price series, marketing analysts study the impact of a newly proposed advertisement by the sales series. A comprehensive knowledge of time series analysis is essential to the modern data scientist/analyst. This course covers important issues in applied time series analysis: a solid knowledge of time series models and their theoretical properties; how to analyze time series data by mainstream statistical software; practical experience in real data analysis and presentation of their findings in a logical and clear way to various audiences. (Instructor: Arnab Bose)
- MSCA 31008 Data Mining Principles
- Prerequisites: MSCA 31007 Statistical Analysis
Drawing on statistics, artificial intelligence, and machine learning, the data mining process aims at discovering novel, interesting and actionable patterns in large data sets. This class will introduce students to the fundamentals of data mining: association rules, Markov models, decision trees, naive Bayes, clustering, and memory-based reasoning. The class follows a learn-by-doing approach in which the students will complete bi-weekly assignments using R on real-world data sets. Students will also propose and complete a quarter-long data mining research project of their own design. At the end of the course, students will know how to prepare data sets; use a wide range of data mining tools in R; understand the advantages and disadvantages of common algorithms; analyze the results of data mining tools; and plan and critique data mining projects. (Instructor: Jonathan Gemmell)
- MSCA 31009 Advanced Data Mining and Predictive Analytics
- Prerequisites: MSCA 31007 Statistical Analysis; MSCA 31008 Data Mining Principles
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques. It will emphasize practice over mathematical theory, and students will spend a considerable amount of class time gaining experience with each algorithm using existing packages in R, Python, and Linux libraries. The course will cover the following topics: regression and logistic regression, regularized regression including the lasso and elastic net techniques, support vector machines, neural networks, decision trees, boosted decision trees and random forests, online learning, k-means and special clustering, and survival analysis. (Instructor: Michelangelo D’Agostino)
- MSCA 31010 Linear and Nonlinear Models in Business Applications
- Prerequisites: MSCA 31007 Statistical Analysis
This course covers simple and multiple linear regression, nonlinear techniques, diagnostics, model selection, and modeling with categorical variables, as relevant to business applications. (Instructors: Francisco Azeredo)
Business Strategy/Project Management (2 courses)
- MSCA 31004 Leadership and Management I: Projects and Teams
- Prerequisites: MSCA 31007 Statistical Analysis or concurrent enrollment
The goal of this course is to teach students how to create, manage, and lead a team so that it can execute its organizational function or primary task successfully. By the end of the course, students will know how to assemble a group of individuals to create a goal-oriented team; manage and promote conflict and collaboration within a team; generate and embed processes, procedures, and standards of performance within a team to help it function effectively; and lead the team toward accomplishing its desired outcomes in a technical context.(Instructor: Arnie Aronoff)
- MSCA 31002 Leadership and Management II: Strategy and Communication
- Prerequisites: MSCA 31004 Leadership & Management I: Projects and Teams; MSCA 31007 Statistical Analysis
This course places analytics in the broader organizational context and its goals, including the different points of view and different importance that may be attached to those goals by managers in various functions. Of necessity, communication is a key to success, weather this is applied to aligning with sponsors’ and other stakeholders’ needs and interests, working with functional and legal teams to ensure that data are both sourced and resulting decision models are used in appropriate ways, or in finding means of presenting results in clear, compelling and actionable formats to different audiences. The development of these “soft” skills is crucial to both an individual analyst’s success, and to the ability of the analytical team overall to earn a seat at the decision-making table. At the end of the course, students will be able to: place analytics in the organizational context, design an analytic project that meets business goals, and present results in a compelling fashion. (Instructor: John Watts)
Electives (2 courses)
Electives (first offering in January 2015) focus on specific analytic methodologies, techniques, and tools applied to functional and sector-specific issues. Topics change quarterly. Additional electives starting in 2015 include risk analytics; health analytics; and survey design and implementation.
- MSCA 32001 Financial Analytics
- Prerequisites: MSCA 31006 Time Series Analysis or concurrent enrollment; MSCA 31007 Statistical Analysis
Businesses as well as consumers are affected by daily fluctuations in consumer prices, industrial production, housing starts, and the price of crude oil. Now that large data sets are widely available, market practitioners are stepping up their efforts to use algorithms to measure econometric patterns and predict their behavior in the future. Financial Analytics applies classic statistical models to the financial markets, including investment and portfolio metrics. Analytics applied in this area address relationships that occur in practice every day in time-critical fashion as investors, speculators, and producers of valued securities and commodities trade across the country and the globe. This course focuses on helping students master the models most relevant to financial analytics. Students will implement these models using hand-written code in the R language using real market data sets to gain a deep understanding of the model and the data’s behavior. (Instructor: Mark Bennett)
- MSCA 32002 Marketing Science and Predictive Analytics
- Prerequisites: MSCA 31007 Statistical Analysis; MSCA 31008 Data Mining Principles or concurrent enrollment
This course explores recent developments in the collection and use of internal and external data/information needed for marketing decisions. Course presentations and materials offer advanced methods of marketing analysis for marketing decisions, including factor analysis, principal components analysis. Such applications are relevant in the areas of strategic marketing, marketing segmentation, new product development, sales promotion analysis, pricing, and direct marketing. The class follows a “learning-by-doing” philosophy based on a real-life case study of a global CPG company. Although all of the techniques are based on the theory of statistics, the approach is logic based, rather than formula based. Students gain real-world knowledge in applying marketing science to solve complex marketing problems, interpret results and gain insights to build data-driven marketing strategies. By the end of the course, students will know how to generate basic and advanced analyses using SPSS/SAS with a consumer survey dataset; develop the ability to analyze a large data and carefully sift through it until a coherent “story” emerges that results in an effective business decision; develop analytical and communication skills to make presentations to clients based on data analyses that “hold” a client’s attention and make an impact. (Instructor: Jan Gollins)
- MSCA 32007 Data Visualization Techniques
- A variety of data is being generated by businesses, government entities, and human activities at increasing rates and complexity. The goal of this course is to expose students to key design principles and techniques that can increase the understanding of complex data and gain valuable insights from the data. Good visualizations present a visual interpretation of the data and also improve comprehension, communication, and decision making. Concepts, techniques, and methods for creating effective data visualizations will be covered. The course will also have a focus on how to present information clearly and effectively. (Veena Mendriatta, Nick Kadochnikov)
- MSCA 32010 Linear Algebra and Matrix Analysis
- The objective of this course is to provide students a strong foundation on linear equations and matrices. On completion of this course, students will be able to formulate, apply and interpret systems of linear equations and matrices, interpret data analytics problems in elementary linear algebra, and demonstrate understanding of various applications using linear transformations. (Instructor: Arnab Bose)
- MSCA 32011 Big Data and Text Analytics
- Prerequisites: MSCA 31007 Statistical Analysis, MSCA 31008 Data Mining Principles
This course covers applications in web analytics, introduces students to big data related tools such as Hadoop and MapReduce, and develops student skills for the analysis of un-structured data. (Instructor: Nick Kadochnikov)
All students must complete a Capstone Project to receive their degree. Students are encouraged to work with a partner or in groups, and are given considerable freedom in selecting their topic. Once they have identified a suitable topic, partner, and advisor, students must submit a proposal to the program director, usually after finishing five courses. Groups present their findings during the final quarter of study to panel of program faculty and industry experts.
Upon completion of the program, students will be awarded the Master of Science degree from the University of Chicago Graham School of Continuing Liberal and Professional Studies.
- View Application Requirements
The Master of Science in Analytics (MScA) is an in-person program offered in both a full- and part-time format. International applicants are encouraged to apply. If admitted, students will receive University assistance with securing an F-1 or J-1 Visa. The MScA is an OPT/STEM approved program of the University of Chicago.
- Completed Application (online)
- $75 Application Fee (non-refundable)
- Transcripts. One unofficial transcript from each university attended must be uploaded within the application. Please do not mail transcripts as part of your admission application; we only accept unofficial uploads for application evaluation. If you are offered admission, one official transcript for each university attended will be required prior to the first day of the term. An unofficial transcript for undergraduate coursework is still required even if you hold an advanced degree(s).
- English Proficiency. If your native language is not English, we require a TOEFL or IELTS test score*. Our TOEFL and College Board code is 1832. The required proficiency standards include a score of 104 on the TOEFL (with 4 sub scores of 26 each), or 7 overall on the IELTS (with sub scores of 7 each; Note that students are required to take the Academic Reading/Writing test within IELTS, not the General Training Reading/Writing test). Applicants who have completed an undergraduate or graduate degree in the United States are exempt from providing proof of English proficiency.
- Letters of Recommendation. Three letters of recommendation from individuals who can assess your academic or professional qualifications. Letters from family members or peer-level colleagues are not acceptable.
- Candidate Statement. Applicants are required to demonstrate the motivation, academic potential, and ability to undertake work at the University of Chicago by writing a candidate statement (typed, double-spaced, not to exceed four pages) describing his or her interest.
- Applicants with international transcripts must provide English translation with their application. An official course-by-course evaluation of transcripts is required and should be requested through an accredited NACES member. A course-by-course evaluation will translate courses, degree, and grades to U.S. equivalency. Please visit http://www.naces.org/members.htm for more details. Evaluation reports should be mailed to: The Graham School of Continuing Liberal and Professional Studies; Attention - Graduate Admissions; 1427 East 60th Street, Second Floor; Chicago, Illinois 60637. Credentialing agencies may also submit electronically to email@example.com. A course-by-course evaluation report will be considered an official academic transcript from your home institution.
- Resume or CV
- Admissions interview. An interview with the program director may be conducted at the request of the applicant or the admissions committee.
- Official GRE or GMAT scores. Standardized test scores are not required for the MScA program.
Applicants are required to apply online for the MSc Analytics program. Select the Graham School from the list of programs, divisions, and departments and then follow the prompts.
Once you are admitted to the program, you may register for courses.