Business Analytics

Co-Major: Business Analytics
Degree Awarded: Bachelor of Science in Business Administration (BSBA)
Calendar Type: Quarter
Total Credit Hours: 186.0
Co-op Options: Three Co-op (Five years); One Co-op (Four years); No Co-op (Four years)
Classification of Instructional Programs (CIP) code: 52.1301
Standard Occupational Classification (SOC) code: 11-1021

The Business Analytics program is a  "co-major"

About the Program

How does a company design an effective social media campaign for its brand new product? How does a bank make credit card offers or detect fraud? How does a chain store stock its shelves with just the right products at the right price? Technology has made it possible to collect, store, process and analyze massive data sets that can help businesses make better decisions. However, there remains a gap that can only be filled by those with a background in business analytics. From the junior analyst providing daily reports on production to the CEO seeking to transform his or her business, all are looking for guidance and talent in business analytics.

LeBow students are uniquely positioned to address descriptive, diagnostic, predictive, prescriptive and pre-emptive questions across the business analytics lifecycle from the corporate generation of data through the application and impact on managerial and leadership decision-making and innovation.

Ranked second in a Computerworld survey on the most difficult skills to find, Business Analytics expertise is not only scarce, but in demand. McKinsey Global Institute reports that the United States could face a shortage of between 140,000 and 190,000 individuals who possess Business Analytics skills and an additional 1.5 million managers with the skills to implement the results.

Example business analytics jobs include, BA Strategy Consultants, Business Intelligence and Performance Management Consultants, Advanced Analytics, Optimization Consultants.

Because students in this co-major are required to choose a major in one of the functional areas of business, the curriculum enables students to tailor the program to their interests and anticipated career path.

Students complete the business analytics co-major in conjunction with one of the following majors: 


An additional distinguishing feature of the business analytics co-major is the required senior project (BUSN 460) where students work in small teams on real business analytics projects from LeBow College’s corporate partners. The projects require students to bring together all the key elements of the business analytics curriculum to derive business insights for a company’s current business challenges. Experiencing this data driven decision-making process is invaluable career preparation.

 Degree Requirements

General Education Requirements
CIVC 101Introduction to Civic Engagement1.0
COM 270 [WI] Business Communication3.0
ENGL 101Composition and Rhetoric I: Inquiry and Exploratory Research3.0
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
MATH 101Introduction to Analysis I4.0
MATH 102Introduction to Analysis II4.0
PHIL 105Critical Reasoning3.0
PSY 101General Psychology I3.0
UNIV B101 [WI] The Drexel Experience2.0
English Literature elective3.0
Fine Arts elective3.0
History elective4.0
Science Requirement6.0
Select two courses from the following:
Applied Cells, Genetics & Physiology
Applied Biological Diversity, Ecology & Evolution
Applied Chemistry
Applied Physics
General Education electives *21.0
Business Requirements
ACCT 115Financial Accounting Foundations4.0
ACCT 116Managerial Accounting Foundations4.0
BLAW 201Business Law I4.0
BUSN 101Foundations of Business I4.0
BUSN 102Foundations of Business II4.0
ECON 201Principles of Microeconomics4.0
ECON 202Principles of Macroeconomics4.0
FIN 301Introduction to Finance4.0
INTB 200International Business4.0
MGMT 450Strategy and Competitive Advantage4.0
MIS 200Management Information Systems4.0
MKTG 201Introduction to Marketing Management4.0
OPM 200Operations Management4.0
ORGB 300 [WI] Organizational Behavior4.0
STAT 201Introduction to Business Statistics4.0
STAT 202Business Statistics II4.0
Primary Major Courses **32.0
Business Analytics Requirements
BUSN 260Introduction to Business Analytics4.0
BUSN 460Business Analytics Senior Project4.0
Select one of the following:4.0
Programming for Data Analytics
Predictive Business Analytics with Relational Database Data
Business Analytics Electives
Select three of the following:12.0
Programming for Data Analytics
Microeconomics
Applied Econometrics
Time Series Econometrics
Systems Analysis and Design
Database Design and Implementation
Predictive Business Analytics with Relational Database Data
Information System Project Management
Marketing Insights
Customer Analytics
Data-Driven Digital Marketing
Linear Models for Decision Making
Advanced Decision Making and Simulation
Decision Models for the Public Sector
Introduction to Data Mining for Business
Introduction to Experimental Design
Total Credits186.0
*

 Students select seven (21.0 credits) of additional general education electives with a minimum of one course in each of the following categories:

  • Society and Culture (Communication, English, Fine Arts, International Area Studies, Language, Philosophy)
  • Social Science (Anthropology, History, Sociology, Political Science, Psychology)
  • Math and Science (Computer Science, Information Systems, Math, Science)
**

 Students completing the Business Analytics co-major must do so in conjunction with a primary business major.  Students must select a primary major from the following list:

  • Accounting
  • Entrepreneurship
  • Finance
  • Legal Studies
  • Management Information Systems
  • Marketing
  • Operations & Supply Chain Management
  • Technology and Innovation Management
***

Occasionally, departments can also offer special topics courses and independent studies on emerging areas of analytics. These courses may be substituted with department chair approval.

The following groupings of courses are recommended by departments for their respective career pathways.  Students are strongly encouraged to complete three courses for at least one career pathway, based on their other major(s) and career goals. 

Accounting:
STAT 331: Introduction to Data Mining for Business
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
OPR 320: Linear Models for Decision Making

Economics:
ECON 301: Microeconomics
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
MIS 343: Database Design and Implementation
STAT 331: Introduction to Data Mining for Business
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing
Complete both of the following courses:
BUSN 360: Programming for Business Analytics (R)
MIS 349: Predictive Analytics (SAS)

Finance:
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
STAT 331: Introduction to Data Mining for Business
OPR 320: Linear Models for Decision Making

Management Information Systems:
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
MIS 361: Information System Project Management

Marketing: (Even though only three will be counted toward the BA co-major/minor, we recommend that the students use their primary major or free business electives to complete all of the courses below in order to develop a solid foundation.  Note that MKTG 366 and STAT 331 employ similar techniques and MKTG 367 and STAT 335 employ similar techniques.)
MKTG 326: Marketing Insights
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing
STAT 331: Data Mining
STAT 335: Introduction to Experimental Design

Operations and Supply Chain Management:
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
STAT 331: Introduction to Data Mining for Business
STAT 335: Introduction to Experimental Design
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
OPR 320: Linear Models for Decision Making
OPR 330: Advanced Decision Making and Simulation
OPR 340: Decision Models for the Public Sector
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing

Sample Plan of Study

Term 1Credits
BUSN 101Foundations of Business I4.0
ECON 201Principles of Microeconomics4.0
ENGL 101Composition and Rhetoric I: Inquiry and Exploratory Research3.0
MATH 101Introduction to Analysis I4.0
UNIV B101 [WI] The Drexel Experience1.0
 Term Credits16.0
Term 2
BUSN 102Foundations of Business II4.0
ECON 202Principles of Macroeconomics4.0
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
MATH 102Introduction to Analysis II4.0
 Term Credits15.0
Term 3
ACCT 115Financial Accounting Foundations4.0
CIVC 101Introduction to Civic Engagement1.0
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
PSY 101General Psychology I3.0
Select one of the following:3.0
Applied Cells, Genetics & Physiology
Applied Biological Diversity, Ecology & Evolution
 
Applied Chemistry 
Applied Physics 
General Education elective3.0
 Term Credits17.0
Term 4
ACCT 116Managerial Accounting Foundations4.0
BLAW 201Business Law I4.0
COM 270 [WI] Business Communication3.0
STAT 201Introduction to Business Statistics4.0
 Term Credits15.0
Term 5
INTB 200International Business4.0
MIS 200Management Information Systems4.0
STAT 202Business Statistics II4.0
Select one of the following:3.0
Applied Biological Diversity, Ecology & Evolution
Applied Cells, Genetics & Physiology
 
Applied Chemistry 
Applied Physics 
 Term Credits15.0
Term 6
Any 200-399 English (ENGL) course3.0
FIN 301Introduction to Finance4.0
MKTG 201Introduction to Marketing Management4.0
OPM 200Operations Management4.0
 Term Credits15.0
Term 7
BUSN 260Introduction to Business Analytics4.0
ORGB 300 [WI] Organizational Behavior4.0
PHIL 105Critical Reasoning3.0
Primary Major Course*4.0
 Term Credits15.0
Term 8
History elective4.0
Primary Major Course*4.0
Science elective3.0
Pick one of the following:4.0
Programming for Data Analytics 
Predictive Business Analytics with Relational Database Data 
 Term Credits15.0
Term 9
Society and Culture elective3.0
Business Analytics Elective4.0
Primary Major Courses*8.0
 Term Credits15.0
Term 10
UNIV B201Career Management1.0
Primary Major Course*4.0
Business Analytics Elective4.0
Fine Arts elective3.0
General education elective3.0
 Term Credits15.0
Term 11
MGMT 450Strategy and Competitive Advantage4.0
Primary Major courses*8.0
Business Analytics Elective4.0
 Term Credits16.0
Term 12
BUSN 460Business Analytics Senior Project4.0
Primary Major course*4.0
Social Science elective3.0
General education elective6.0
 Term Credits17.0
Total Credit: 186.0
*

See degree requirements for a list of business majors that may be completed in conjunction with the business analytics major.


 

Co-Op/Career Opportunities

Visit the Drexel Steinbright Career Development Center page for more detailed information on co-op and post-graduate opportunities. To learn more about career opportunities and resources see the Career Guides provided by the Steinbright Career Development Center.

Requirements

  • No more than 2 courses or 8.0 credits required by a student’s major may be counted towards this minor.
  • A grade of “C” (2.0) or better must be earned for each course in this minor for it to be counted.
  • No more than two transfer courses may be used to complete this minor. Transfer credits must be taken before matriculated at Drexel.
  • Students should check the pre-requisites of all classes when selecting electives. It is the responsibility of the student to know pre-requisites.
  • Business administration, business & engineering and economic students may complete any of the business minors, including: economics, finance, international economics, legal studies, management information systems, marketing, organizational management, technology innovation management, and operations & supply chain management. 
  • Cannot do a major and a minor in the same field of study. 

All prospective students should meet with an advisor from the College as soon as possible. Call 215.895.2110 to set up an appointment.

Minor in Business Analytics

How does a company design an effective social media campaign for its brand new product? How does a bank make credit card offers or detect fraud? How does a chain store stock its shelves with just the right products at the right price? Technology has made it possible to collect, store, process and analyze massive data sets that can help businesses make better decisions. However, there remains a gap that can only be filled by those with a background in business analytics. From the junior analyst providing daily reports on production to the CEO seeking to transform his or her business, all are looking for guidance and talent in business analytics.

LeBow students are uniquely positioned to address descriptive, diagnostic, predictive, prescriptive and pre-emptive questions across the business analytics lifecycle from the corporate generation of data through the application and impact on managerial and leadership decision-making and innovation.

Ranked second in a Computerworld survey on the most difficult skills to find, Business Analytics expertise is not only scarce, but in demand. McKinsey Global Institute reports that the United States could face a shortage of between 140,000 and 190,000 individuals who possess Business Analytics skills and an additional 1.5 million managers with the skills to implement the results.

The Business Analytics minor at LeBow consists of basic courses in statistics, operations research, and management information systems as well as advanced courses in management information systems, statistics/econometrics, and modeling. The curriculum enables students to tailor the program to their interests and anticipated career path.

One of the distinguishing features of the business analytics minor is the required senior project (BUSN 460) where students work in small teams on real business analytics projects from LeBow College’s corporate partners. The projects require students to bring together all the key elements of the business analytics curriculum to derive business insights for a company’s current business challenges. Experiencing this data driven decision making process is invaluable career preparation.

BUSN 260Introduction to Business Analytics4.0
BUSN 360Programming for Data Analytics4.0
or MIS 349 Predictive Business Analytics with Relational Database Data
BUSN 460Business Analytics Senior Project4.0
Business Analytics electives (select three of the following):12.0
Programming for Data Analytics
Microeconomics
Applied Econometrics
Time Series Econometrics
Systems Analysis and Design
Database Design and Implementation
Predictive Business Analytics with Relational Database Data
Information System Project Management
Marketing Insights
Customer Analytics
Data-Driven Digital Marketing
Linear Models for Decision Making
Advanced Decision Making and Simulation
Decision Models for the Public Sector
Introduction to Data Mining for Business
Introduction to Experimental Design
Total Credits24.0
*

The following groupings of courses are recommended by departments for their respective career pathways.  Students are strongly encouraged to complete three courses for at least one career pathway, based on their other major(s) and career goals. 

Accounting:
STAT 331: Introduction to Data Mining for Business
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
OPR 320: Linear Models for Decision Making

Economics:
ECON 301: Microeconomics
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
MIS 343: Database Design and Implementation
STAT 331: Introduction to Data Mining for Business
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing
Complete both of the following courses:
BUSN 360: Programming for Business Analytics (R)
MIS 349: Predictive Analytics (SAS)

Finance:
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
STAT 331: Introduction to Data Mining for Business
OPR 320: Linear Models for Decision Making

Management Information Systems:
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
MIS 361: Information System Project Management

Marketing: (Even though only three will be counted toward the BA co-major/minor, we recommend that the students use their primary major or free business electives to complete all of the courses below in order to develop a solid foundation. Note that MKTG 366 and STAT 331 employ similar techniques and MKTG 367 and STAT 335 employ similar techniques.)
MKTG 326: Marketing Insights
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing
STAT 331: Data Mining
STAT 335: Introduction to Experimental Design

Operations and Supply Chain Management:
ECON 350 [WI] : Applied Econometrics
ECON 360: Time Series Econometrics
STAT 331: Introduction to Data Mining for Business
STAT 335: Introduction to Experimental Design
MIS 342: Systems Analysis and Design
MIS 343: Database Design and Implementation
OPR 320: Linear Models for Decision Making
OPR 330: Advanced Decision Making and Simulation
OPR 340: Decision Models for the Public Sector
MKTG 366: Customer Analytics
MKTG 367: Data-Driven Digital Marketing

Facilities

In fall 2013, LeBow College opened its 12-story, Gerri C. LeBow Hall, with a finance trading lab, behavioral studies lab and integrated teaching technology in all classrooms. The new building features two lecture halls, 15 classrooms of varying sizes and seating configurations, including case study rooms and cluster classrooms designed to facilitate group work. Other amenities consist of extensive areas of student spaces, including 12 collaboration rooms, two quiet study areas, and 3,500 square feet of student lounges. Gerri C. LeBow Hall brings together faculty, students and staff, in a state of the art building on the University City campus. Please visit the LeBow College of Business webpage to learn more about Gerri C. LeBow Hall.

Business Analytics Faculty

Pramod Abichandani, PhD. Assistant Clinical Professor.
Murugan Anandarajan, PhD (Drexel University) Department Chair, Management; Department Head, Decision Sciences and MIS. Professor. Cyber crime, strategic management of information technology, unstructured data mining, individual internet usage behavior (specifically abuse and addiction), application of artificial intelligence techniques in forensic accounting and ophthalmology.
Orakwue B. Arinze, PhD (London School of Economics). Professor. Client/Server computing; Enterprise Application Software (EAS)/Enterprise Resource Planning Software (ERP); knowledge-based and decision support applications in operations management.
Hande Benson, PhD (Princeton University) Assistant Department Head, Decision Sciences & MIS. Associate Professor. Interior-point methods, Large Scale Optimization, Mathematical Programming, Nonlinear Optimization, Operations and Supply Chain Optimization, Optimization Software, Portfolio Optimization
Qizhi Dai, PhD (University of Minnesota). Associate Professor. Business Value of Information Technology, eCommerce, Economics of Information Technology, Information System Management.
Michaela Draganska, PhD (Kellogg School of Management, Northwestern University) Department of Marketing. Associate Professor. Advertising strategy, product assortment decisions, new product positioning, distribution channels. Marketing analytics and big data, marketing communications, marketing research, marketing strategy, technology and innovation.
Elea Feit, PhD (University of Michigan) Department of Marketing. Assistant Professor. Bayesian hierarchical models, interactive (eCommerce), marketing research, missing data.
David Gefen, PhD (Georgia State University) Provost Distinguished Research Professor. Professor. Strategic IT management; IT development and implementation management; research methodology; managing the adoption of large IT systems, such as MRP II, ERP, and expert systems; research methodology, eCommerce; Online Auctions; Outsourcing; SAS; Technology Adoption.
Merrill W. Liechty, PhD (Duke University). Clinical Professor. Bayesian statistics, portfolio selection, higher moment estimation, higher moment estimation, Markov Chain Monte Carlo
Chuanren Liu, PhD (Rutgers University). Assistant Professor. Data Mining, Decision Models, Risk Assessment, Sequential Analysis.
Bruce D. McCullough, PhD (University of Texas Austin). Professor. Applied Econometrics, Data Mining, Econometric Techniques, Reliability of Statistical and Econometric Software.
Samir Shah, DPS (Pace University). Associate Clinical Professor. Drexel University's Provost Fellow India Partnerships
Chaojiang Wu, PhD (University of Cincinnati). Assistant Professor. Business Analytics, Computational Statistics, Healthcare Analytics, Semiparametric Regression, Statistical Data Mining.
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