Analytics At Questrom

COURSE CODE: is841

The widespread proliferation of IT-influenced economic activity leaves behind a rich trail of micro-level data about consumer, supplier and competitor preferences. This has led to the emergence of a new form of competition based on the extensive use of analytics, experimentation, and fact-based decision making. In virtually every industry the competitive strategies organizations are employing today rely extensively on data analysis to predict the consequences of alternative courses of action, and to guide executive decision making. This course provides a hands-on introduction to the concepts, methods and processes of business analytics. We will learn how to obtain and draw business inferences from data by asking the right questions and using the appropriate tools. Topics to be covered include data preparation, data visualization, data mining, text mining, recommender systems as well as the overall process of using analytics to solve business problems, its organizational implications and pitfalls. Students will work with real world business data and analytics software. Where possible cases will used to motivate the topic being covered. Prior courses in data management and statistics will be helpful but not required.

COURSE CODE: IS843

This Level 3 analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python (e.g. IS717/IS756), and basic analytics (e.g. IS833/IS834) are prerequisite.

COURSE CODE: IS860

This Level 3 analytics course will introduce concepts, methods, and processes of data mining and machine learning within projects that have been sponsored by partner companies. Through practice in this live setting, we will develop our analytical problem solving skills, and understand how to organize and manage agile analytical projects in the most realistic possible situation. We will learn how to collect, wrangle, and analyze both primary and secondary data sources in multiple business contexts and apply this knowledge to the client data. We will also learn how to communicate the essential elements of our analytic efforts to C-suite level consumers by delivering our recommendations through a business intelligence dashboard.

COURSE CODE: IS889

Examines the data communication hardware and software characteristics that are relevant to the applications software designer. A general overview of communications network design is presented. Topics covered include issues in the design and use of both local area networks and wide area networks. The impact of communications technology on organizations as well as trends in the communications industry are studied.

COURSE CODE: IS842

This seminar focuses on computerization and change in a postindustrial society, including an in-depth look at computers as they relate to productivity in workplaces, social progress, public pol-icy, privacy, safety and moral values. This course is designed to help students understand the range of impacts that computing has now and can have when used by business, public agencies, and individuals.

COURSE CODE: QM870

This course teaches students the core elements of the R programming language. Students will also explore popular methods and principles in data analysis and data visualization.

COURSE CODE: IS833

This course will introduce students to programming-based tools and techniques for becoming analytically-minded managers. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. We will review variables, data types, conditionals, loops, and functions, and use these to introduce data structures, including DataFrames. We will also cover reading and writing raw files and the core APIs in analysis and visualization. With the basics under our belt, we will complement it with some of the most popular libraries for data analysis in Python, such as Pandas and Numpy for data manipulation, Matplotlib and Seaborn for visualization, and Jupyter Notebook for reporting. These packages will facilitate workflow and enhance the basic Python functionalities. Using them, one can effortlessly clean up a dataset, create elaborate plots, analyze and summarize the data, and produce presentable reports. Throughout the final project, we will learn to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise data preprocessing, explanatory analysis, and machine learning techniques. Prior programming experience in Python is required.

COURSE CODE: IS811

COURSE CODE: IS876

This course focuses on the programming tools used in e-business related applications such as; JavaScript; DTML; ASP. This will give students hands on experience on both the web client side as well as the server side interfacing with backened databases. This is a technical and hand-on programming course that is suited for the more technical students.

COURSE CODE: BA755

This course focuses on how to learn from data, specifically to 1) organize, portray, and summarize data; 2) assess the validity of conclusions that have been drawn from statistical analyses to support business (and other) decisions; and 3) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.

COURSE CODE: BA765

This course will cover the fundamentals of programming for data science using R, the command line, and version control. These skills will be reinforced via lectures and hands-on exercises focused on elevating common programming challenges and highlight best practices. The aim of this course is to provide the pathway to fluency in the tools required to analyze data and fully manage data science projects both as an individual contributor as well as in team settings.

COURSE CODE: BA775

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

COURSE CODE: BA780

Further development of the concepts introduced in Introduction to Data Analytics I. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to confirm the validity of the original business question or to refine it. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

COURSE CODE: BA810

The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in- class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and test predictive models.

COURSE CODE: BA820

It has been reported that as much as eighty percent of the world’s data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

COURSE CODE: BA830

This course teaches students how to measure impact in business situations and how to evaluate others’ claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in R. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness ‘big data’ to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.

COURSE CODE: BA886

The analytics project will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester effort.

COURSE CODE: BA840

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

COURSE CODE: BA860

This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.

COURSE CODE: BA865

This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).

COURSE CODE: BA870

This is an introductory course on Financial Analytics providing students with knowledge about key “financial” concepts (financial accounting, financial statements, managerial accounting, corporate finance, and investments) so that they can intelligently apply their prior analytics knowledge and tools to real-world financial applications.

COURSE CODE: BA875

This is an introductory course on principles, methods, and techniques used in operations and supply chain analytics. Emphasis is given on the big data age where firms are continuously designing, assessing, and improving the systems that create and deliver their products and services. Students will learn visual representation techniques to enhance their understanding of complex data and models. Such visual techniques will be paired with network analysis to better identify patterns, trends and differences from datasets across categories, space, and time. The course will also draw on real-world applications to demonstrate their use in a variety of contexts.

COURSE CODE: BA880

This course focuses on developments in People Analytics, an evolving data-driven approach to employee decisions and practices. The course covers theory, practice, and methods that are critical for addressing people-related challenges at companies, such as hiring, retaining, evaluating, rewarding performance, and managing teams and social networks, to name a few. By drawing on the latest company practices, research, and cases studies, this course will help you apply people analytics to achieve organizational objectives and to advance in your own career.

COURSE CODE: BA885

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise-scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. Whenever possible, we will draw examples related to global challenges such as climate crisis.

COURSE CODE: BA887

Continuation of work begun in QST BA886

COURSE CODE: MF793

This course provides an introduction to R and Exploratory Data Analysis, Time Series Analysis, Multivariate Data Analysis, and Elements of Extreme Value Theory. This course also covers an array of statistical techniques used for simulation, parameter estimation, and forecasting in Finance.

COURSE CODE: MF840

This is the second course of the econometrics sequence in the Mathematical Finance program. The course quickly reviews OLS, GLS, the Maximum Likelihood principle (MLE). Then, the core of the course concentrates on Bayesian Inference, now an unavoidable mainstay of Financial Econometrics. After learning the principles of Bayesian Inference, we study their implementation for key models in finance, especially related to portfolio design and volatility forecasting. We also briefly discuss the Lasso and Ridge methods, and contrast them with the Bayesian approach Over the last twenty years, radical developments in simulation methods, such as Markov Chain Monte Carlo (MCMC) have extended the capabilities of Bayesian methods. Therefore, after studying direct Monte Carlo simulation methods, the course covers non-trivial methods of simulation such as Markov Chain Monte Carlo (MCMC), applying them to implement models such as stochastic volatility.

COURSE CODE: MF810

The course introduces students to a number of efficient algorithms and data structures for fundamental computational problems across a variety of areas within data science and blockchains. A special programming language for blockchain technology, such as Solidity, will be taught. Advanced techniques for improving computational performance, including the use of parallel computation and GPU acceleration are surveyed. Frameworks for big data analysis such as Apache Hadoop and Apache Spark are studied. Students will have the opportunity to employ these techniques and gain hands-on experience developing advanced applications.

COURSE CODE: MF815

This course surveys the application of machine learning techniques to data characterized by low signal-to-noise ratios and non-stationarity, properties of many financial datasets. Challenges associated with the application of “data-hungry” techniques such as deep learning to small-to-medium size datasets, often encountered in finance, are addressed.

COURSE CODE: QM717

The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.

COURSE CODE: QM717

The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.

COURSE CODE: IS878

COURSE CODE: IS853

(pending)

COURSE CODE: QM880

The modeling process illustrated throughout the course will significantly improve students’ abilities to structure complex problems and derive insights about the value of alternatives. You will develop the skills to formulate and analyze a wide range of models that can aid in managerial decision-making in the functional areas of business. These areas include finance (capital budgeting, cash planning, portfolio optimization, valuing options, hedging investments), marketing (pricing, sales force allocation, planning advertising budgets) and operations (production planning, workforce scheduling, facility location, project management). The course will be taught almost entirely by example, using problems from the main functional areas of business. This course is not for people who want a general introduction to or review of Excel. This course is for students who are already comfortable using Excel and would like to use it to create optimization and simulation models.

COURSE CODE: QM716

The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.

COURSE CODE: IS823

This Level 2 (non-programming-based) analytics course examines how the abundance of data has transformed decision making in organizations and the strategic implications of this transformation. We explore how data are being used, ranging from the core principles of properly identifying data sources to the actual analytical methods being used to solve a wide range of business problems. Students will have some hands-on work with advanced Excel, Tableau, and two database applications, Microsoft Access and Neo4j (Neo4j is used to compare and contrast SQL and NoSQL databases in an analytics context). At the end of this course, students will have gained a big-picture perspective on business analytics as well as hands-on experience with commonly-used business analytics software.

COURSE CODE: QM717

The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.