Course topics
Part 1. Introduction
- Business Analytics
- Business Analytics Examples
- Text Analytics
- Big Data
- Cloud
- Startups
- Crowd-Sourced Analytics
- Literature
- Business Analytics Education
- Online and In-Person Education
- Volunteering and Networking
- Analytics Consortium
Part 2. Review of Linear Algebra
- Review of derivatives, gradients and Hessians
- Computing gradients and Hessians
- Taylor series expansion
- Finite difference methods
- Convex functions
- Checking a matrix for PD and PSD by computing principal minors
- Checking if symmetric matrix is PD or PSD by computing its eigenvalues
- Composition rules
Part 3. Basic Statistics
- Before you analyze your data
- Sources of uncertainty
- Summarizing and interpreting your data
- Quantitative data
- Categorical data
Part 4. Mathematical Modelling
- Modeling
- Predictive Maintenance
- Modeling Process
- Model Examples
Part 5. Simulations
- Simulation Modeling
- Business Case Study
Part 6. Optimization
- Overview of Optimization
- Optimization Techniques
- Multi-Objective Optimization
- Mean-Variance Portfolio Selection
Part 7. Data Mining
- Data mining application classes of problems
- Classification
- Clustering
- Regression
- Forecasting
- Hypothesis or discovery driven
- Iterative
- Scalable
Part 8. Artificial Intelligence
- Era of Cognitive Computing
- Artificial Intelligence
- Cognitive Computing
- Spatio-Temporal Analytics
Part 9. Visual Analytics
- Visual Grids
- Dashboards
- Spreadsheets vs. Visual Analytics
- HR Analytics
- Infographics
Part 10. Decision-Making Based on Analytics
- Decision Making and Risk Management
- Analytics Software
Practice
As a part of the project activities, students participate in the Queen’s International Innovation Challenge — an international contest on Data Science and Business Analytics from Queens University.
UCU Data Science master students gained the 3rd place in 2016 and won the contest in 2017.
Prerequisites
None