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Your Career Success

Take the next step to advance your career with UChicago’s MS in Applied Data Science.

The In-Person program admits full- and part-time students for entrance in Autumn quarter annually. The MS in Applied Data Science program is STEM/OPT eligible. Please visit the Online program page if you are interested in those full- and part-time options.

Your Engagement

If you learn best in an in-person classroom environment and prefer to live in or near to Chicago, IL, the Master’s in Applied Data Science In-Person program is ideal for you. Your high-tech classrooms are located in downtown Chicago (NBC Tower, Gleacher Center), and you will have access to tailored, in-person student services and program amenities. You will complete 12 courses for the MS degree and can graduate in 12-18 months full-time. Part-time options available. Most courses are from 6-9pm Monday through Thursday with some offered on Fridays and Saturdays. This allows you to work in an internship and/or job during the program. Select courses are offered during the day. Learn more about Tuition, Fees, & Aid.

Your Student Experience

As an In-Person program student, you will have access to expert faculty and instructors with industry expertise, a full-service student affairs team, and an unparalleled network of global alumni. Our team is passionate about supporting a Signature Student Experience tailored to your needs.

Program Director, Greg Green, PhD

Your Outcomes

Your success is our success. Graduates of UChicago’s Master’s in Applied Data Science program consistently demonstrate competitive outcomes. You will have full access to our tailored career services and external partnerships team to help you advance your career in data science–whether you are launching your career, interested in pivoting, or want to move up within your current company. You can take advantage of in-house career services advising and coaching, tailored networking events, career fairs to connect directly with employers, internship placement support, and more.

By and For Data Science Innovators

You will earn UChicago’s Master’s in Applied Data Science by successfully completing 12 courses (6 core, 4 elective, 2 Capstone) and our tailored Career Seminar*.

To keep up with the rapidly evolving field and job market, you will be challenged by our rigorous curriculum that is designed by and for data science innovators and leaders. Courses are reviewed annually to ensure the content keeps pace with the rapidly evolving landscape of data science.

You have the flexibility to pursue the Master’s in Applied Data Science degree on a part- or full-time schedule. Part-time students enroll in two courses each quarter and take their courses in the evenings or on Saturdays. Full-time students take three courses per quarter. Some of their courses may be offered during the day. All courses are taught at the NBC Tower or Gleacher Center in downtown Chicago.

Foundational Courses (noncredit, optional)

Foundational noncredit courses are designed and taught by Master’s in Applied Data Science faculty and instructors. These optional courses provide the basis for the rigorous Applied Data Science degree. Course content undergirds the theoretical, strategic, and practical data science studies you will encounter in the rest of the curriculum.

Beginning in academic year 2024-25, all entering students will complete a required online Foundational Skills Assessment. The assessment helps faculty and advisors understand how to best support you once you begin in the program. The four Foundational noncredit courses are listed below. Please note that Introduction to Statistical Concepts and R are considered pre-quarter courses and therefore take place during the 5 weeks leading up to your first quarter in the program. All Foundational courses are completed virtually for all students regardless of enrollment in the In-Person or Online Program.

Career Seminar (noncredit, required)

Increasingly, employers demand data scientists and analytics professionals who are not only technically excellent but also superior collaborators, reliable communicators, ethical problem-solvers, and more. To ensure that our graduates remain as top candidates in the job market, we provide tailored support through our required, multi-quarter Career Seminar. The Seminar is designed to counter-balance students’ time commitments in other courses. *Students with significant, relevant full-time work experience may be eligible to waive this requirement.

Core Courses (6)

You will complete six core courses toward your Master’s in Applied Data Science degree. Core courses allow you to build your theoretical data science knowledge and practice applying this theory to examine real-world business problems.

Elective Courses (4)

Explore advanced analytics strategies and applications. You will complete four required electives toward your 12-course degree program. We continually add electives to evolve with the data science landscape. Past electives include: Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Methods, Data Science for Algorithmic Marketing, Data Visualization Techniques, Digital Marketing Analytics in Theory and Practice, Financial Analytics, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, Supply Chain Optimization.

Capstone (2)

The required Capstone Project is completed over two quarters and covers research design, implementation, and writing. Full-time students start their Capstone Project in their third quarter. Part-time students generally begin the Capstone Project two quarters before their projected graduation quarter. Students choose among industry- and research-focused projects.

Get in Touch

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  • Noncredit Courses
  • Core Courses
    • Time Series Analysis and Forecasting

      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.

    • Statistical Models for Data Science

      In a traditional linear model, the observed response follows a normal distribution, and the expected response value is a linear combination of the predictors.  Since Carl Friedrich Gauss (1777-1855) and Adrien-Marie Legendre (1752-1833) created this linear model framework in the early 1800s, the “Linear Normal” assumption has been the norm in statistics/data science for almost two centuries.  New methods based on probability distributions other than Gaussian appeared only in the second half of the twentieth century. These methods allowed working with variables that span a broader variety of domains and probability distributions. Besides, methods for the analysis of general associations were developed that are different from the Pearson correlation.

    • Machine Learning I

      This course is aimed at providing students an introduction to machine learning with data mining techniques and algorithms. It gives a rigorous methodological foundation in analytical and software tools to successfully undertake projects in Data Science. Students are exposed to concepts of exploratory analyses for uncovering and detecting patterns in multivariate data, hypothesizing and detecting relationships among variables, conducting confirmatory analyses, and building models for predictive and descriptive purposes. It will present predictive modeling in the context of balancing predictive and descriptive accuracies.

    • Machine Learning II

      The objective of this course is three-folds–first, to extend student understanding of predictive modeling with machine learning concepts and methodologies from Machine Learning 1 into the realm of Deep Learning and Generative AI. Second, to develop the ability to apply those concepts and methodologies to diverse practical applications, evaluate the results and recommend the next best action. Third, to discuss and understand state-of-the machine learning and deep learning research and development and their applications.

    • Data Engineering Platforms for Analytics or Big Data and Cloud Computing

      Data Engineering Platforms teaches effective data engineering—an essential first step in building an analytics-driven competitive advantage in the market.

      Big Data and Cloud Computing teaches students how to approach big data and large-scale machine learning applications. There is no single definition of big data and multiple emerging software packages exist to work with it, and we will cover the most popular approaches.

    • Leadership and Consulting for Data Science

      The Leadership and Consulting for Data Scientist course is focused on:

      • Learning techniques and proven methods to effectively grasp the business domain including organizational dynamics of consultancies and client organizations
      • Developing relevant solutions to enterprise problems using the sampling methods, traditional statistical techniques and modern machine learning models that deliver value to the organization
      • Practicing successful project delivery through effective data discovery, influential team membership and leadership, project management, and communication at every stage

      This course will not only make you a better data scientist; it will make you and your analyses more approachable, more persuasive, and ultimately more successful.

  • Sample Elective Courses
    • Advanced Computer Vision with Deep Learning

      Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data in the same way that humans do. Deep learning is a subset of machine learning and a branch of Artificial Intelligence (AI). It involves the training, deployment, and application of large complex neural network architectures to solve cutting-edge problems. Deep Learning has become the primary approach for solving cognitive problems such as Computer Vision and Natural Language Processing (NLP) and has had a massive impact on various industries such as healthcare, retail, automotive, industrial automation, and agriculture. This course will enable students to build Deep Learning models and apply them to computer vision tasks such as object recognition, detection, and segmentation. Students will gain an in-depth understanding of the Deep Learning model development process, tools, and frameworks. Although the focus of the course will primarily be computer vision, students will work on both image and nonimage datasets during class exercises and assignments. Students will gain hands-on experience in popular libraries such as Tensorflow, Keras, and PyTorch. Students will also learn to apply state of the art models such as ResNet, EfficientNet, RCNNs, YOLO, Vision Transformers, etc. for computer vision and work on datasets such as CIFAR, ImageNet, MS COCO, and MPII Human Poses.

    • Advanced Machine Learning and Artificial Intelligence

      Since the era of big data started, challenges associated with data analysis have grown significantly in different directions: First, the technological infrastructure had to be developed that can hold and process large amounts of data from different sources and of multiple not always well formalized formats. Second, data analysis methods had to be reviewed, selected and modified to work in distributed computational environments like combinations of in-house clusters of servers and cloud. But the biggest challenge of all is learning to think differently in order to ask new types of questions that could not be answered by analyses of less complex data streams with less complex technological infrastructure. In recent years significant progress has been achieved in creating technological ecosystems for big data analysis. Innovative technologies such as open source projects MapReduce, Hadoop, Spark, Storm, Kafka, TensorFlow, H2O, etc. allowed us to look at depths of data unseen before. We now have a growing number of sources and educational courses introducing these new tools. However, developing new data analysis methods appropriate to these new data ecosystems is more difficult than it appears.

    • Bayesian Methods

      Bayesian inference is a method of learning in which Bayes’ theorem is used to combine the previous knowledge with the new evidence in the data to form an improved posterior knowledge. Another name for such methods is probabilistic inference. Probabilistic Bayesian models form the foundation of the most modern algorithms of Machine Learning and Artificial Intelligence. The focus of this course is an introduction to the Bayesian approach. Many methods learned by students in Statistical Analysis, Linear and Nonlinear Models, Data Mining and Machine Learning will be reviewed from the point of view of probabilistic inference. We will look at hierarchical, mixture, robust, and non-parametric Bayesian models and learn how to use them in practical applications. Content will include using probabilistic models to make business decisions under uncertainty, analyzing causation in the data, using probabilistic inference to assess the risk of black swan events, accounting for uncertainty in project management and other applications. Students will learn necessary facts of probability theory, Bayesian reasoning, Markov chain Monte Carlo using JAGS, STAN and PyMC. The course contains large number of interactive demonstrations, and workshops with examples through which the lecturer shares his own hands-on experience with the students.

    • Data Science for Algorithmic Marketing

      This course focuses on marketing science methods and algorithms for undertaking competitive analysis in the digital landscape: market segmentation, mining databases for effective digital marketing, design of new digital and traditional products, forecasting sales and product diffusion, real time product positioning, intra omni-channel optimization and inter omni-channel resource allocation, and pricing across both omni-channel marketing effectiveness and ROI. The course will use a combination of lecture, in-class discussions, group assignments, and a final group project. The course lays special emphasis on algorithms. Hence it draws heavily from the fields of optimization, machine-learning based recommendation systems, association rules, consumer choice models, Bayesian estimation, experimentation and analysis of covariance, advanced visualization techniques for mapping brand perceptions, and analysis of social media data using advanced NLP techniques.

    • Data Visualization Techniques

      In today’s data driven enterprise, data storytelling using effective visualization strategies is an essential skill for analytics practitioners in almost every field to explore and present data. This course focuses on modern data visualization technologies, tools, and techniques to convert raw data into actionable information. Modern data visualization tools are at the forefront of the “self-service analytics” architectures which are decentralizing analytics and breaking down IT bottlenecks for business experts. Moreover, with its foundations rooted in statistics, psychology, and computer science, data visualization shows you how to better understand the data, present clear evidence of your findings to your intended audience and tell engaging data stories through charts and graphics. This course is designed to introduce data visualization as a medium of effective communication using strategic storytelling, and the basis for interactive information dashboards.

    • Digital Marketing Analytics in Theory and Practice

      Successfully marketing brands today requires a well-balanced blend of art and science. This course introduces students to the science of web analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide marketers with the foundation needed to apply data analytics to real-world challenges they confront daily in their professional lives. Students will learn to identify the web analytic tool right for their specific needs; understand valid and reliable ways to collect, analyze, and visualize data from the web; and utilize data in decision making for their agencies, organizations or clients. By completing this course, students will gain an understanding of the motivations behind data collection and analysis methods used by marketing professionals; learn to evaluate and choose appropriate web analytics tools and techniques; understand frameworks and approaches to measuring consumers’ digital actions; earn familiarity with the unique measurement opportunities and challenges presented by New Media; gain hands-on, working knowledge of a step-by-step approach to planning, collecting, analyzing, and reporting data; utilize tools to collect data using today’s most important online techniques: performing bulk downloads, tapping APIs, and scraping webpages; and understand approaches to visualizing data effectively.

    • Financial Analytics

      This course concentrates on the following topics: review of financial markets and assets traded on them; main characteristics of financial analytics: returns, yields, volatility; review of stochastic models of market price and their statistical representations; concept of arbitrage, elements of arbitrage pricing approach; principles of volatility analyses, implied vs. realized volatility; correlation, cointegration and other relationships between various financial assets; market risk analytics and management of portfolios of financial assets. The course puts special emphasis on covering main steps of building analytics from visualizing data and building intuition about their structure and patterns to selecting appropriate statistical method to interpretation of the results and building analytical models. Topics are illustrated by data analysis projects using R. Basic familiarity with R is a requirement.

    • Health Analytics

      Given the breadth of the field of health analytics, this course will provide an overview of the development and rapid expansion of analytics in healthcare, major and emerging topical areas, and current issues related to research methods to improve human health. We will cover such topics as security concerns unique to the field, research design strategies, and the integration of epidemiologic and quality improvement methodologies to operationalize data for continuous improvement. Students will be introduced to the application of predictive analytics to healthcare. Students will understand factors impacting the delivery of quality and safe patient care and the application of data-driven methods to improve care at the healthcare system level, design approaches to answering a research question at the population level, become familiar with the application of data analytics to impacting care at the provider level through Clinical Decision Systems, and understand the process of a Clinical Trial.

    • Machine Learning Operations

      The objective of this course is two-fold: first, to understand what Machine Learning Operations (MLOps) is and why it is a key component in enterprise production deployment of machine learning projects, and second, to expose students to software engineering, model engineering and state-of-the-art deployment engineering with hands-on platform and tools experience. This course crosses the chasm that separates machine learning projects/experiments and enterprise production deployment. It covers three pillars in MLOps: software engineering such as software architecture, Continuous Integration/Continuous Delivery and data versioning; model engineering such as AutoML and A/B experimentation; and deployment engineering such as docker containers and model monitoring. The course focuses on best practices in the industry that are critical to enterprise production deployment of machine learning projects. Having completed this course, a student understands the machine learning lifecycle and what it takes to go from ideation to operationalization in an enterprise environment. Furthermore, students get exposure to state-of-the-art MLOps platforms such as allegro, xpresso, Dataiku, LityxIQ, DataRobot, AWS Sagemaker, and technologies such as gitHub, Jenkins, slack, docker, and kubernetes.

    • Natural Language Processing and Cognitive Computing

      Extracting actionable insights from unstructured text and designing cognitive applications have become significant areas of application for analytics. Students in this course will learn foundations of natural language processing, including: concept extraction; text summarization and topic modeling; part of speech tagging; named entity recognition; semantic roles and sentiment analysis. For advanced NLP applications, we will focus on feature extraction from unstructured text, including word and paragraph embedding and representing words and paragraphs as vectors. For cognitive analytics section of the course, students will practice designing question answering systems with intent classification, semantic knowledge extraction and reasoning under uncertainty. Students will gain hands-on expertise applying Python for text analysis tasks, as well as practice with multiple IBM Watson services, including: Watson Discovery, Watson Conversation, Watson Natural Language Classification and Watson Natural Language Understanding.

    • Real Time Intelligent Systems

      Developing end-to-end automation and intelligent systems is now the most advanced area of application for analytics. Building such systems requires proficiency in programming, understanding of computer systems, as well as knowledge of related analytical methodologies, which are the skills that this course aims to teach to students. The course focuses on python and is tailored for students with basic programming knowledge in python. The course is partially project based. During the first three sessions, we will review basic python concepts and then learn more advanced python and the ways to use python to handle large data flows. The later sessions are project based and will focus on developing end-to-end analytical solutions in the following areas: Finance and trading, blockchains and crypto-currencies, image recognition, and video surveillance systems.

    • Reinforcement Learning

      This course is an introduction to reinforcement learning, also known as neuro-dynamic programming. It discusses basic and advanced concepts in reinforcement learning and provides several practical applications. Reinforcement learning refers to a system or agent interacting with an environment and learning how to behave optimally in such an environment. An environment typically includes time, actions, states, uncertainty and rewards. Reinforcement learning combines neural networks and dynamic programming to find an optimal behavior or policy of the system or agent in a complex environment setting. Neural network approximations are used to circumvent the well-known ‘curse of dimensionality’ which has been a barrier to solving many practical applications. Dynamic programming is the key learning mechanism that the system or the agent uses to interact with the environment and improve its performance. Students will master key learning techniques and will become proficient in applying these techniques to complex stochastic decision processes and intelligent control.

    • Supply Chain Optimization

      “Big Data” continues to grow exponentially in our large-scale transactional world where 100,000s of SKUs and millions of customers are interacting with 1:1 offers that include differential pricing, shipping timing/costs and even made to order “custom” product configurations. These consumer behaviors are quickly advancing the availability of new data and techniques within the discipline of Data Science. This elective course will give students the opportunity to apply their skills in data visualization, data mining tools, predictive modeling, and advanced optimization techniques to address Supply Chain challenges. The course focuses on the use of Advanced Predictive Modeling, Machine Learning, AI and other Data Science insight and activation tools to automate and optimize the performance of the Supply Chain. Students will also learn how to optimize the performance of the Supply Chain from the lens of multiple related disciplines including: Sales Forecasting, Warehousing/Inventory Management, Promotion, Pricing, Logistics Network Optimization, Freight Cost Management, Manufacturing, Retail POS Information, Ecommerce, Consumer Data, and Product Design/Packaging. After completing this course, you will be prepared to work in any of the numerous specialty areas possible in the world of Supply Chain Management.

    • Data Science Capstone Project

      The required Capstone Project is completed over two quarters and covers research design, implementation, and writing. Full-time students start their capstone project in their third quarter. Part-time students generally begin the capstone project in their fifth quarter.