Download Business Analytics crash course on Data Mining, Predictive Modeling

Business Analytics crash course on
Data Mining, Predictive Modeling
Introduction
This course will change the way you think about data and its role in business. Increasingly, decisionmakers and systems rely on intelligent tools and techniques to analyze data systematically to
improve decision-making.
We will examine how data analysis technologies can be used to improve decision making. We will
study the fundamental principles and techniques of data mining, and we will examine real-world
examples and cases to place data-mining techniques in context, to develop data-analytic thinking,
and to illustrate that proper application is as much an art as it is a science. In addition, we will work
“hands-on” with data
This course trains the members to turn unstructured business problems into mathematical models
and to use such models to make better managerial decisions. This is a hands-on course that focuses
on how to understand the raw data, how design analysis for unstructured problems, how to improve
the usual reports by imparting data driven insights, how to prepare & present analytical insights etc.,
The application areas are diverse and they originate from problems in finance, marketing and
operations.
Course Details
Business Analytics training is a four day long instructor-led classroom course
Class Timings: Saturday & Sunday 10:00 to 17:00
Address: Trendwise Analytics, Sigma Tech Park, Whitefield
Fee: INR 7,500
Participants who complete the program will be able to:
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Design the analysis flow for a real world business problem
Use powerful quantitative methods that will help in making better, more informed, and
more effective business decisions
Use accurate statistical techniques to analyze the data
Analytical Skills taught
Data cleaning, designing the analysis, Generating enhanced BI Reports & Dashboards,
Advanced analysis, regression analysis, cluster analysis, segmentation, forecasting and
Predictive Modeling. Please refer to course overview for details.
Audience: Anybody who works on data is eligible.
This course is for Analysts or Managers who want to use data analytics effectively and prepare
enhanced deep dive analysis on data related to Cost/ Budget / Performance/ ROI/ Balanced
Scorecard/ Inventory/ Sales/ Operations/ SLA/ Demand/ Risk/ Monthly Dashboards /HR
/Marketing/ Services.
Prerequisite: Before attending this course, candidate should
Have experience using applications, such as word processors or spreadsheets, in the Microsoft
Windows, Macintosh or Linux environment. No statistical background is necessary
Tools
The principles and practice of data analysis are illustrated using SAS/R/SPSS/Excel.
Contents
Module-1: Understanding the data –Get a feel of the data and Prepare the data for analysis
We need to have a clear understanding of all fields and their distributions before we jump on to
analysis. Data preparation or preprocessing is a big issue for both data warehousing and data
mining. No quality data, no quality mining results! Quality decisions must be based on quality data
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Data exploration
o Data contents & Univarate Analysis
o Means & Frequencies & cross tabs
 Data Validation & Data sanitization
o Missing values & outlier treatment
 Lab: Data Exploration, Validation & Data sanitization
Module-2: Data Mining using Cluster Analysis & Decision Trees
Cluster Analysis and Decision trees help us identify the customer segments with most relevant
characteristics with respect to objective
 Data Mining using Cluster Analysis
 Lab: K –Means clustering
 Customer Segmentation using Decision trees
 Lab: CHAID Analysis
Module-3: Predictive Modeling using logistic and Liner regression
 Model building using Linear Regression
 Model interpretation & Multicollniarity removal
 Non Linear regression for (Yes/No) type output
 Lab-Multiple Liner regression
 Lab- Logistic regression
Module-4: Main steps in credit Risk Model building
 Philosophy of credit risk and Market response model building
 Water fall analysis to decide the bad
 Variable selection/elimination techniques
 Model validation and recalibration