Business Analytics for Students at Emory University's Goizueta Business School

Business Analytics For Students

  • Marketing Analytics in Excel (David Schweidel)

    While more powerful tools may be available in some organizations, the ubiquity of Excel makes comfort with it an essential business skill. This course develops students’ familiarity with Microsoft Excel as a tool for analyzing business problems. No prior experience with Excel is necessary. Drawing on commonly encountered marketing scenarios, tools are developed using Microsoft Excel to provide guidance to support managerial decisions. Among the topics discussed are customer valuation, pricing, forecasting and the analysis of survey data.

    Advanced Data Science (George Easton)

    This course is an advanced analytics course focusing on data science and the development of data products. Data science is an emerging interdisciplinary field that draws from computer science, statistics, business, as well as other fields. All of the typical phases of data science projects will be discussed in the course: data acquisition, data cleaning, storage and retrieval, data analysis, and production product development. The course will use the programming language Python and the R statistical package as the primary software tools. The course will also introduce the cloud computing environment which is where we will do our heavy number crunching (probably on Amazon Web Services) and will discuss strategies for computationally intensive problems. We will analyze both structured and unstructured (e.g., text) data. The goal of the course is to give students some real experience with data science so that they have the understanding necessary to function as a part of a data science team and contribute to business decisions in that context. This course involves programming and debugging and students should expect to face unstructured problems that will produce significant frustration (all a part of functioning in this arena).

    Forecasting and Predictive Analytics (Steve Stuk)

    One of the most important rolls of a person in business is to make better decisions. In this course, you will begin to understand the decision process and how to incorporate information. This course expands on that basic structure in major ways:

    1. New method of modeling data;
    2. Adaptive and non-linear models (regression +);
    3. General non-linear methods (Synthetic Neural Networks-SNN) Time series methods; and
    4. Development of an automated structure to support decisions tied to data Decision Support Systems (DSS).

    In this course, we developed models to predict the expected dollars contributed by a town and then use that information to decide which towns to canvas first. This is a problem that is analogous to many business situations that follow the path of: need to make a decision, collect data, develop model, predict values, make decision, repeat often or do only once. This course is a very "hands on" working with data course, working with sample data sets or even better bringing problems from work. Through sharing of experience and discussion of MANY data sets and problems we gain years of experience in a few months. Grading is based on 4 projects and a presentation that, normally group work. The course is structured to challenge the very good quant people while providing a path to success for the numerically challenged.

  • Annual Emory Marketing Analytics Center Conference

    David SchweidelEach spring, EmoryMAC brings together over 200 marketing professionals, faculty, and students from the Atlanta area to discuss and learn about the latest trends in marketing analytics.

    Learn More

  • Chellappa discusses J.P. Morgan Chase, cyber security

    Goizueta Business School's Ram Chellappa, Associate Professor of Information Systems and Operations Management, sat down with The Wall Street Journal earlier this week to discuss cyber security issues. The most recent firm attacked, J.P. Morgan Chase, resulted in compromised customer information including names and addresses.

    Learn More

Marketing Analytics in Excel (David Schweidel)

While more powerful tools may be available in some organizations, the ubiquity of Excel makes comfort with it an essential business skill. This course develops students’ familiarity with Microsoft Excel as a tool for analyzing business problems. No prior experience with Excel is necessary. Drawing on commonly encountered marketing scenarios, tools are developed using Microsoft Excel to provide guidance to support managerial decisions. Among the topics discussed are customer valuation, pricing, forecasting and the analysis of survey data.

Advanced Data Science (George Easton)

This course is an advanced analytics course focusing on data science and the development of data products. Data science is an emerging interdisciplinary field that draws from computer science, statistics, business, as well as other fields. All of the typical phases of data science projects will be discussed in the course: data acquisition, data cleaning, storage and retrieval, data analysis, and production product development. The course will use the programming language Python and the R statistical package as the primary software tools. The course will also introduce the cloud computing environment which is where we will do our heavy number crunching (probably on Amazon Web Services) and will discuss strategies for computationally intensive problems. We will analyze both structured and unstructured (e.g., text) data. The goal of the course is to give students some real experience with data science so that they have the understanding necessary to function as a part of a data science team and contribute to business decisions in that context. This course involves programming and debugging and students should expect to face unstructured problems that will produce significant frustration (all a part of functioning in this arena).

Forecasting and Predictive Analytics (Steve Stuk)

One of the most important rolls of a person in business is to make better decisions. In this course, you will begin to understand the decision process and how to incorporate information. This course expands on that basic structure in major ways:

  1. New method of modeling data;
  2. Adaptive and non-linear models (regression +);
  3. General non-linear methods (Synthetic Neural Networks-SNN) Time series methods; and
  4. Development of an automated structure to support decisions tied to data Decision Support Systems (DSS).

In this course, we developed models to predict the expected dollars contributed by a town and then use that information to decide which towns to canvas first. This is a problem that is analogous to many business situations that follow the path of: need to make a decision, collect data, develop model, predict values, make decision, repeat often or do only once. This course is a very "hands on" working with data course, working with sample data sets or even better bringing problems from work. Through sharing of experience and discussion of MANY data sets and problems we gain years of experience in a few months. Grading is based on 4 projects and a presentation that, normally group work. The course is structured to challenge the very good quant people while providing a path to success for the numerically challenged.

Annual Emory Marketing Analytics Center Conference

David SchweidelEach spring, EmoryMAC brings together over 200 marketing professionals, faculty, and students from the Atlanta area to discuss and learn about the latest trends in marketing analytics.

Learn More

Chellappa discusses J.P. Morgan Chase, cyber security

Goizueta Business School's Ram Chellappa, Associate Professor of Information Systems and Operations Management, sat down with The Wall Street Journal earlier this week to discuss cyber security issues. The most recent firm attacked, J.P. Morgan Chase, resulted in compromised customer information including names and addresses.

Learn More

1.7

Megabytes of data created each second for every human on the planet (by 2020, Forbes)

40,000

Google searches performed every second (Forbes)

50%

Surveyed CFOs who say they have made "substantial investments" in consumer analytics (60% say they plan more over the next three years, Deloitte)

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