Because many of the interesting applications in data analytics are in the field of marketing, Marketing Research is also a foundational course. While this course is technically an elective, students should be fluent in its material to be prepared for more-advanced courses in data analytics.
This course provides a fundamental understanding of marketing research methods employed by well-managed firms. The course focuses on integrating problem formulation, research design, questionnaire construction, sampling, data collection, and data analysis to yield the most valuable information. The course also examines the proper use of statistical applications as well as qualitative methods, with an emphasis on the interpretation and use of results.
This course will introduce students to the elements of administrative claims data and analyses of demographics and severity, outcomes and quality, and prices. In-class exercises will allow students the opportunity to learn firsthand about the analyses, methodologies, and techniques.
An executive's most prized network contact for crafting new strategies and making evidence-based judgments may no longer be a person, but a machine. Machines help solve complex problems, lessen decision bias, scale human effort, and spot hidden patterns in big data. However, they lack the creativity and insight that top executives possess. Together, executives and machines have the potential to make powerful “thought partnerships.” Using hands-on cases and applications --- including IBM’s Deep Blue and Google’s AlphaGo that beat Chess and Go Grand Masters --- this course shows how to use a critical set of machine learning decision tools, such as natural language processing, sentiment analysis, and pattern recognition to discover new competitive strategies, turn raw numbers into convincing stories, and make less biased judgments. The overarching goal is to enable you to confidently lead data science and design teams, know the expansiveness and limits of machine-learning complex decision support tools, and be capable of applying human+machine thought partnerships to grow businesses or disrupt Grand Masters in any field.
Most strategic decisions businesses make require an assessment of cause and effect. What will happen to prices and sales if I open a new location in a particular geographic area? How will consumers respond if I begin posting the caloric content of my food products at the point of purchase? What is the effect of seasonal bonuses on employee productivity? This course covers in depth the empirical tools that are most valuable for linking cause to effect: regression analysis and field experiments. You will learn how to perform convincing data analyses to answer specific questions, how to evaluate analyses others have done, and how to present data analysis in a clear and accessible way.
Firms are increasingly obtaining data not just about purchase decisions, but also about individual consumers' pre- and post-purchase behavior. But few firms have the managerial and data analytics expertise to act intelligently on such data. For the key marketing problems in customer acquisition, development, and retention, this hands-on course introduces sophisticated data analytics techniques tailored to those problems, including predictive analytics and large-scale testing. Students apply each technique to a large consumer-level database, learning how to target consumers individually and how to derive customer insights
How does a "sale" sign change customer behavior? How has the Internet changed customer price sensitivity? How has the expansion of retail stores, factory stores, and the Internet changed customer behavior? This data- and analytics-driven course seeks to answer these questions and provides a cohesive framework for studying consumer behavior. Most of the data is from real-world managerial problems, and students learn how to make informed pricing and retailing decisions using data.
The objective of this course is to develop a strong conceptual and practical understanding of how to use digital data to improve marketing decisions. Students will gain familiarity with key areas of digital marketing including search and display advertising, search engine optimization, and social media marketing. Working with real data sets, students will practice using quantitative tools to improve campaigns, estimate ROI, and gain customer insights.
In the competitive business world, companies increasingly rely on sophisticated data analytics to make management decisions related to people (e.g., hiring and promotion decisions; team composition; compensation). The purpose of this course is to use data analytics to solve problems in the management of people, focusing specificaly on how such tools are used in the sports industry. Competitive sports helped usher in data analytics given the vast availability of data on individual and team performance. At first only a few teams, like the Oakland A's as depicted in Moneyball, used data analytics to gain a performance advantage, but since those early days, the business of sports has become dominated by data science. In this course, we will use data analytics in sports to better understand how to evaluate and solve real world problems that managers face everyday.
Social Dynamics and Network Analytics covers cutting edge research on social media, digital data, and crowdsourcing, and provides you with the tools to practically apply this research in your own career. By the end of the course you will know how to: measure volume and location of Internet search data to understand and forecast trends; measure volume and sentiment of Twitter conversations; collect network data and create meaningful network visualizations; use the wisdom of crowds, including setting up a prediction market, to create better forecasts; and use Amazon Mechanical Turk for crowdsourcing.
This course was formerly known as MGMT 440 Hiring, developing and retaining the right employees is crucial for success in modern firms. Big data is transforming how firms recruit and develop talent. Hiring, training and promotion practices increasingly rely on both economic principles and quantitative analysis. The purpose of this course is to introduce a powerful set of economic concepts for human resource management and to use analytics to make better informed decisions on personnel strategy. For example, we will use statistical software to predict the potential of applicants to improve hiring decisions or we will use experimental designs to evaluate the effectiveness of training and talent development programs. Examples of additional topics covered in the course are productivity estimation, turnover and employee satisfaction, promotions, and discrimination. In modern firms, these topics take up a significant mindshare of CEOs and other senior leaders. This course is designed for MBA students who aspire to start, lead, and build businesses; this is not a course for those interested in careers as administrators of human resource management systems.
Be persuasive in presenting your ideas. Learn to convince your clients, customers, and colleagues of the merits of your views, using the latest breakthroughs in cognitive science, computer science, and graphic design. Through interactive exercises, the course will provide hands-on experience and tools for presenting data-based evidence with impact, across images, graphics, and visualizations of big data. Leave this course with expertise in the principles of effective data visualization, as well as a practical toolkit for conveying your ideas in ways that are convincing, catchy, and contagious.
Technology is inextricably linked with the generation and management of data for use in analytics. This course provides an overview of enterprise and cloud computing technology building blocks and how they enable data analytics. We discuss today's technology infrastructure for data analytics and where that technology seems to be headed. Finally, we touch on the different ways of organizing a data analytics function and what you need to know about foundational data management topics such as data privacy and security. The course helps students obtain a high-level view of the technology landscape and to understand how organizational considerations around technology affects the overall performance of data analytics in organizations.
Analytics systems are software systems. In this course, you'll learn what engineers know about building software that is reliable and accurate, integrates multiple technologies, scales and performs well, and adapts to changing requirements. We'll move beyond simply programming to design of software systems for analytics. Topics will include: typical idioms and design patterns, designing for scalability and change, testing, and performance tuning. Through rigorous examples, students will build the perspective necessary to lead technical teams and avoid costly engineering failures.
Course exercises will be conducted primarily in the Python programming language and an introductory online Python course is a prerequisite.
The Analytical Consulting Lab provides real-world learning experiences for students to work with sponsoring companies on business questions that revolve around analytics. Students work in teams to answer current and important business questions using analytics tools learned in other data analytics courses. The course also provides an excellent opportunity to hone consulting skills such as presentation of analytics findings and recommendations, and client-relationship management. Many of the projects will benefit from having taken one or more of the "Competitive Advantage" courses.
Evidence-based decisions are no longer a luxury, as firms are capitalizing on the tremendous value that data analytics can bring to their decision-making. These analytic techniques and tools are widely applicable to myriad industries and business contexts. This course will give students the opportunity to practice their existing data analytics skills to solve diverse real-world cases. Students will also deepen their ability to select the appropriate method to solve each problem, clearly and concisely present results, and clearly articulate the strengths and limitations of their analyses.
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