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.
We will use Google Classroom. Google Classroom is a blended learning platform to enhance your learning experience. You can ask questions, submit assignments, download materials, check your grades, share interesting things and help each other. To access Google Classroom page, please use this code (Spring 2019): sh9rf05.
At the end of this course, you will be able to:
Demonstrate Analytical Literacy by identifying and explaining the process of data management and analysis, and the interpretation and dissemination of results.
Demonstrate data-driven Problem-Solving skills by employing commonly used methods of predictive and prescriptive analytics to formulate business strategies.
Demonstrate Critical Thinking by diagnostically analyzing data to evaluate different business cases and make data-driven business decisions.
Knowledge of BI
- Introduction to the Business Analytics
- Business Data Processing
- Visual Analytics
- Business Dashboard Development & Dissemination
- Web analytics
- Online Text Mining and Sentiment Analysis
- Introduction to Data Mining
- Clustering (Hierarchical & K-Means)
- Classification (Decision Tree, Logistic
- Regression, K-NN, Naive Bayes, Neural Network)
- Monte Carlo Simulation
- Linear Programming & Integer Optimization
- Risk and Decision Analysis
- Process Mining
Big Data Analytics
- Introduction to Apache framework & Big Data Analytics
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 on the selected lab activities(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 Google Classroom for feedback.
You will be asked to write three group reports (900 words) on the three assignments and submit to Google Classroom. 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.
- DESCRIPTIVE ANALYTICS (15% LAB + 5% REPORT)
- WEB ANALYTICS (10%)
- PREDICTIVE ANALYTICS (40% LAB + 5% REPORT)
- PRESCRIPTIVE ANALYTICS (20% LAB ACTIVITIES +5% REPORT)
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: firstname.lastname@example.org | Phone: 619 594-0746