Business Analytics (ANOP)
Organizations of all types increasingly rely on data and analytics to inform their decision-making processes. To this end, both for-profit and not-for-profit organizations must have the ability to transform data into information. Business analytics is the scientific process of transforming data or quantitative models into actionable insight to improve decision-making. Business analytics rests on three broad functional pillars: descriptive, predictive and prescriptive analytics.
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Descriptive analytics prepares, displays and analyzes historical data; it identifies data patterns to report trends.
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Predictive analytics forecasts future probabilities and trends, and determines relationships in data that may not be readily apparent with descriptive analysis.
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Prescriptive analytics evaluates and determines new ways to operate based upon meeting certain objectives while balancing operational constraints.
This modeling and analysis cannot be done in isolation. Across the curriculum we will address and investigate the ethical implications of both the intended and unintended use of our analysis. The business analytics major allows students with an interest in quantitative analysis (broadly defined) to further explore their interests in an organizational context, to appreciate the inherent broad social and ethical issues within the field, and to become effective managers in any data-driven organization. The major provides our graduates the necessary analytical and communication skills built upon a broad management education to address challenges in the 21st century.
Business analytics majors are also eligible to pursue a BA in Data Science as a co-major. Complementing the depth a student receives from their business analytics major, the BA in data science provides breadth across the interdisciplinary field of data science. The co-major is not intended as – nor can it be declared as – a stand-alone course of study.
In the spring of their sophomore year, all BSBA students will, in consultation with their advisers, select a major in one of the Freeman College of Management departments and will complete the specific major requirements in addition to the Freeman College core curriculum requirements.
Business Analytics Requirements
Beyond completion of the Freeman College core requirements, all business analytics majors must take the following courses:
Business Analytics Core Requirements
| MATH 201 | Calculus I | 1 |
| or MATH 192 | Topics in Calculus | |
| MATH 216 | Statistics I | 1 |
| ANOP 203 | Introduction to Programming for Business Analytics | 1 |
| or CSCI 203 | Introduction to Computer Science | |
Business Analytics Major Requirements
| ANOP 270 | Data Visualization for Business Analytics | 1 |
| or HUMN 270 | Data Visualization for the Digital Humanities | |
| ANOP 330 | Predictive Analytics: Machine Learning Fundamentals for Business | 1 |
| ANOP 350 | Predictive Analytics: Forecasting & Simulation | 1 |
| ANOP 370 | Prescriptive Analytics: Decision Modeling & Optimization | 1 |
| or MATH 358 | Topics in Operations Research | |
| ANOP 400 | Business Analytics Practicum | 1 |
Culminating Experience
All BSBA majors must satisfy the Culminating Experience component of the Freeman College of Management General Education Curriculum by taking a course designated as such on campus no earlier than the second semester of the junior year. Business analytics majors will fulfill this by taking ANOP 400 Business Analytics Practicum.
Data Science Co-Major
Students pursuing a BSBA in business analytics are eligible to pursue a BA in Data Science as a co-major. Complementing the depth a student receives from their business analytics major, the BA in data science provides breadth across the interdisciplinary field of data science. The co-major is not intended as – nor can it be declared as – a stand-alone course of study.
Business Analytics Minor
The business analytics minor provides students with the opportunity to complement their major field of study with a foundation in descriptive, predictive and prescriptive analytics.
The business analytics minor consists of three required courses and two electives for a total of five courses, as follows1:
| Required courses: | ||
| ANOP 102 | Spreadsheet Modeling & Data Analysis | 1 |
| ANOP 270 | Data Visualization for Business Analytics | 1 |
| ANOP 330 | Predictive Analytics: Machine Learning Fundamentals for Business | 1 |
| Two courses from the following list: | 2 | |
| Introduction to Programming for Business Analytics | ||
| Database Management and Applied Data Analysis | ||
| Financial Decision Modeling using Spreadsheets | ||
| Supply Chain Analytics | ||
| Predictive Analytics: Forecasting & Simulation | ||
| Prescriptive Analytics: Decision Modeling & Optimization | ||
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Other ANOP courses (excluding ANOP 202 and ANOP 301) may be considered with approval by the ANOP department chair.
Drawing upon their professional and liberal education, Freeman College of Management students will be able to collaboratively create positive societal impacts through:
Analysis
- Students will demonstrate the ability to understand organizations and analyze them rigorously.
Integrity
- Students will possess the judgment, vision and integrity necessary to serve society and their professions.
Morality
- Students will identify creative and morally responsible solutions to organizational and societal issues.
Beyond the learning goals of the Freeman College Core Curriculum, Business Analytics majors will:
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Understand and critically apply the concepts and methods of the discipline to support predictions and decisions in real-world situations.
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Foster the ability to bridge the gap between technical teams and stakeholders by working effectively as a member or leader of an interdisciplinary team and communicate the outcomes to the relevant decision makers.
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Learn how to evaluate and quantify uncertainty and risk, incorporating their inherent influence on both the decision process and the outcomes.
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Commit to ethical decision-making and the long-run welfare of both organizations and the communities they serve.
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Develop an appreciation and dedication for personal continuous improvement, leading to a long-lasting and relevant knowledge base and skill set.
Courses
ANOP 102. Spreadsheet Modeling & Data Analysis. 1 Credit.
Offered Both Fall and Spring; Lecture hours:3,Other:1
This course serves as the introduction to quantitative modeling and basic statistical analysis in a spreadsheet-based environment, especially as they apply to managerial decision making.
ANOP 202. Operations Management. 1 Credit.
Offered Both Fall and Spring; Lecture hours:3
This course introduces students to the ways in which to model, analyze, and improve processes for producing services and goods. Prerequisite: ANOP 102. ENGR 226 or MATH 216 or MATH 227 or PSYC 215 accepted with permission of the instructor.
ANOP 203. Introduction to Programming for Business Analytics. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
Overview of programming tools and methods for analytics. Students solve computational and modeling problems using Python. This foundation of programming logic will help students understand advanced analytic tools in the upper-level Business Analytics courses. Not open to students who have taken CSCI 203.
ANOP 204. Sports Analytics. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
This course focuses on analytical techniques and their application to the sports industry. Course topics include player evaluation, team ratings, scheduling and coaching strategies. A basic knowledge of Excel spreadsheets, probability, statistics and sports (specifically, baseball, basketball and football) is assumed.
ANOP 242. Database Management and Applied Data Analysis. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
Databases are essential for organizations to collect, store and use data in an effective way. This course introduces students to how databases are designed and operated. The students will learn the principles of database design, the relational model and the SQL language. Prerequisite: ANOP 102 or permission of the instructor.
ANOP 270. Data Visualization for Business Analytics. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
An introduction to the principles of data visualization and data preparation. Instruction in software to prepare and present data through the creation of tables, charts, and dashboards to aid in communication of insights. Prerequisites: ANOP 102 or ENGR 226 or MATH 216 or MATH 227 or PSYC 215.
ANOP 301. Global Supply Chain Management. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
In this course students will learn the concepts and tools to model, analyze and improve global supply chain operations under a variety of contexts. Prerequisites: ANOP 102. ENGR 226 or MATH 216 or MATH 227 or PSYC 215 accepted with permission of the instructor.
ANOP 302. Financial Decision Modeling using Spreadsheets. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
Decision making of individuals and groups in organizations. Topics include linear and nonlinear optimization with applications in finance and accounting, fundamentals of portfolio and risk management, and the application of Monte Carlo methods to the pricing of derivatives. Prerequisites: ACFM 203 or ACFM 210 and ANOP 102, or equivalents.
ANOP 310. Independent Study in Analytics and Operations Management. .25-1 Credits.
Offered Either Fall or Spring; Lecture hours:Varies,Other:3; Repeatable
Independent Study in Analytics and Operations Management. Prerequisite: permission of the instructor.
ANOP 311. Supply Chain Analytics. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
In this course students will learn basic concepts in quantitative supply chain modeling and simulation. Students learn methods that are used extensively in business organizations to solve large, structured problems. Prerequisite: ANOP 102. ENGR 226 or MATH 216 or MATH 227 or PSYC 215 accepted with permission of instructor.
ANOP 315. Special Topics in Analytics and Operations Management. .25-1 Credits.
Offered Fall, Spring or Summer; Lecture hours:Varies
Special Topics in Analytics and Operations Management. Prerequisite: permission of the instructor.
ANOP 330. Predictive Analytics: Machine Learning Fundamentals for Business. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
An exploration of the concepts and applications of machine learning toward problems in business. Topics include prediction and classification methods, clustering, and association rules. Two prerequisites: (ANOP 102 or ENGR 226 or MATH 216 or PSYC 215) and (ANOP 203 or CSCI 203).
ANOP 350. Predictive Analytics: Forecasting & Simulation. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
Study of statistical modelling and simulation techniques for data and model-based forecasting (Time Series, Smoothing Methods, Regression, ARIMA, Simulation, etc.) using a variety of software tools. Two prerequisites: (ANOP 102 or ENGR 226 or MATH 216 or MATH 227 or PSYC 215) and (ANOP 203 or CSCI 203).
ANOP 370. Prescriptive Analytics: Decision Modeling & Optimization. 1 Credit.
Offered Either Fall or Spring; Lecture hours:3
An introduction to decision modeling and analysis using deterministic optimization models and solution methodologies. Two prerequisites: (ANOP 102 or ENGR 226 or MATH 216 or MATH 227 or PSYC 215) and (ANOP 203 or CSCI 203).
ANOP 390. Honors Course in Analytics and Operations Management. .5-1 Credits.
Offered Either Fall or Spring; Lecture hours:Varies,Other:3; Repeatable
Honors Course in Analytics and Operations Management. Prerequisite: permission of the instructor.
ANOP 400. Business Analytics Practicum. 1 Credit.
Offered Both Fall and Spring; Lecture hours:3
A culminating experience course where student teams collaborate with external clients on semester long projects leveraging the skills and concepts acquired within the Business Analytics major. Prerequisite: (ANOP 330) and (ANOP 350 or ANOP 370 or MATH 358). Open only to senior Business Analytics majors.
Faculty
Professors: Matthew D. Bailey (Chair), Mihai Banciu (Associate Dean of Faculty for the Freeman College of Management)
Associate Professors: Jimmy Chen, Alia C. Stanciu, Joseph Wilck (Practice)
Assistant Professors: Joyaditya Laik, James Paine, Thiago Serra
Visiting Assistant Professors: Xinwei Chen, Behnaz Malaei