Business Analytics - A Hot Topic!
This program will provide you with data management, analytical and problem solving skills, and an analytical tool set that will help you address modern data-intensive business problems. This is not your ordinary statistics course. With access to high end statistical applications, you will learn how to apply rational statistical approaches to create actionable information from massive amounts of data.
Most companies today have massive amounts of data at their disposal. But, few organizations are successful in creating intelligence and gleaning real insight from this data. Data analytics allow organizations and their employees to utilize this data in meaningful ways to make key decisions and drive organizational improvement. According to a recent study by Accenture, two-thirds of executives from major US companies say that analytical capabilities need to be improved at their organization, and McKinsey & Company predicts a shortage of 1.5 million managers with big data experience by 2018. A 2011 study by researchers at MIT and Wharton finds that "firms that adopt data-driven decision making have output and productivity that is 5-6% higher than the competition."
This program is open to all. Instructors are willing to work with you and use your own data from work. Tuition reimbursement is available as a payment option.
- Introduction to Business & Data Analytics
- Data and Information Management
- Predictive Analysis
- Advanced Predictive and Pattern Discovery Analytics
- Text Mining
- Information Visualization
- Prescriptive Analytics
- Analytics Seminars
What is business analytics?
How are analytics used in the current business environment?
What are the impacts of analytics on business performance and profitability?
What are the key elements in building analytics capability?
An overview of data mining techniques
Database and data warehouses
Data modeling and relational database
Data difficulties in business analytics
Introduction to predictive modeling
Predictive modeling using Linear Regression
Predictive modeling using Decision Trees
Predictive modeling using Logistic Regression
Survey and Experiment Design
Introduction to survey and experiment design
Introduction to pattern discovery
Market Basket Analysis
Introduction to forecasting
Time series characteristics and components
Time series regression models
Time series data and hierarchical data structure
Introduction to simulation and optimization
Monte Carlo Simulation