Improving Business with Data Mining
11th & 12th November 2022
About the Faculty
- Over 20 years of experience in IT
- Setting up and managing large software product group (top 3 in the segment world-wide) as a profit center, with 120+ Marketing Executives, Project Managers, Software Engineers and Quality Manager.
- Setting up and managing Quality practice group with 10+ quality executives
- Managing Projects with varied technology (Internet, Client-Server, Main Frame), customers (USA, Europe [UK, Germany], Middle-east, Asia (India, Malaysia)], and methodology
- Involved in all phases of software development life cycle
- Marketing Support
- Member SEPG, Assessment Team Member P-CMM
- Domain: Logistics (Ports & Cargo)
Dr. Sunil D. Lakdawala,
BI Trainer & Consultant Entrepreneur
Business Intelligence and Data Mining
In one sentence, “Business Intelligence” can be defined as “Data Based Decision”. Decisions based on data provide a competitive edge to today’s business. Taking the cognizance of the fact that this field is growing very rapidly, many large domestic IT consultancy and service companies have already established a separate “BI” practice.
BI systems need data and techniques of storing and analyzing the same. Data Mining is an emerging technology for analyzing the data through which valuable ‘knowledge’ is discovered from the databases of business. For example, using data mining technology a business can improve their sales turnover significantly by spotting the more likely buyers and cross selling products.
The technology is founded in machine learning, statistical, mathematical and data management sciences. The data mining software enables a business manager to find patterns in data and classify or cluster records or predict values based on multiple variables. The techniques include Neural Networks, k-Means clustering, Logistic Regression, Classification Trees, k-Nearest Neighbors and so on.
Day 1
|
Session no. |
Theme |
|
1,2 |
Whom to Give Loan? Supervised – Classification Data Mining Concepts, Techniques, Applications: · Training, Validation and Test Data · Decision Tree · Confusion Matrix |
|
3 |
Predicting Market Value of an Asset Supervised – Regression Data Mining Concepts, Techniques, Applications: · Linear Regression · RMS and MAPE |
|
4 |
Target Marketing Supervised – Data Mining Concepts, Techniques, Applications: · Lift Chart · Logistic and Linear Regression |
Day 2
|
5 |
What goes together Unsupervised – Association Data Mining Concepts, Techniques, Applications: · Support, Confidence, Lift · Support Cutoff and Taxonomy |
|
6 |
Big Data Analytics Concepts, Techniques, Applications: · Text Mining · Introduction to Sentiment Analysis, Social Network Analysis |
|
7 |
Introduction to Business Analytics and Big Data Analytics · Objective · Normal and Big Data · Requirements · Data Warehouse · Data Analytics · Big Data Analytics · Business Analytics · Metrics · Maturity Models |
Concept Note
Objective
This workshop is designed to provide a comprehensive overview of how Data Mining techniques are applied in business situations to improve Business Performance. The participants undertake hands-on exercises using R and excel.
Takeaways
At the end of the workshop participants will be able to identify opportunities of using Data Mining for improving performance in their business and sponsor and manage Data Ming projects in their organization.
Pedagogy
The program will have interactive sessions, including:
- Cases to discuss the application of Data Mining in business
- Presentation of Data Mining techniques and discussion on their application
- Discussion on opportunities of using Data Mining techniques in business
- Exercises for hands-on use of a Data Mining software
