MIS401 | BUSINESS INTELLIGENCE & DATA ANALYTICS

Kaveh Abhari, Ph.D. | MIS Department

Fowler College of Business

Business intelligence is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end-users make informed business decisions. This course provides an introduction to the field of business intelligence and data analytics at the operational, tactical, and strategic levels. The aim of this course is to furnish you with the basic understanding of data science techniques and teach you how to use these tools to analyze complex business problems and arrive at solutions. The techniques to be studied are visual analytics, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. The instruction in this course is lab-based and student-centered, and it requires your active participation in all classes. This arrangement will allow you to develop in-depth functional understanding and gain practical knowledge of the course content.

Syllabus

Objectives

At the end of this course, you will be able to:

Analytical Literacy

Demonstrate Analytical Literacy by identifying and explaining the process of data management and analysis, and the interpretation and dissemination of results.

Problem-Solving

Demonstrate data-driven Problem-Solving skills by employing commonly used methods of predictive and prescriptive analytics to formulate business strategies.

Critical Thinking

Demonstrate Critical Thinking by diagnostically analyzing data to evaluate different business cases and make data-driven business decisions.

Knowledge of BI

Demonstrate Knowledge of business intelligence by selecting, justifying and utilizing appropriate analytical strategies and tools.

Communication

Demonstrate Communication skill by documenting, visualizing, explaining and presenting analytical procedures findings, and recommendations.

TOPICS

Introduction
  • Introduction to the Business Analytics
Descriptive Analytics
  • Business Data Processing
  • Visual Analytics
  • Business Dashboard Development & Dissemination
Web Analytics
  • Web analytics
  • Online Text Mining and Sentiment Analysis
Predictive Analytics
  • Introduction to Data Mining
  • Clustering (Hierarchical & K-Means)
  • Classification (Decision Tree, Logistic Regression, K-NN, Naive Bayes, Neural Network)
  • Forecasting
  • Monte Carlo Simulation
Prescriptive Analytics
  • Optimization
  • Linear Programming & Integer Optimization
  • Risk and Decision Analysis
  • Process Mining
Big Data Analytics
  • Introduction to Apache framework & Big Data Analytics

Evaluation

Attainment of course objectives and learning outcomes will be assessed by: (1) Successful completion of 17 individual lab assignments (85%), and (2) Submission of three group reports (15%). You will receive up to 5% extra credit for successful completion of two individual online training courses (R programming & SQL analytics). 

NOTE: There is a 50% penalty for submitting an assignment after the deadline.

The lab assignments are designed to give you firsthand experience with the basic and intermediate skills for using popular business analytics tools. You will complete the lab assignments in class and upload the final files to Canvas for feedback. 

 You will be asked to write three group reports (900 words) on the three assignments and submit to Canvas. The work will be carried out in small groups (3 students per group) in such a way that each individual’s work can be identified and assessed. Each member will be responsible to work on and present a key part of the project including Problem, Analysis, and Outcomes. 

Contact

Please feel free to contact me if you are having any questions, or difficulty with the course assignments, class projects, feel overwhelmed, or if instructions are unclear. I welcome your feedback, comments, and suggestions on the course content and instruction.

Office: SSE3200 | email: kabhari@sdsu.edu | Phone: 619 594-0746