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Predictive Analytics with Decision Trees. Professors Leo Pipino & Luvai Motiwalla OIS Department, MSB 16.711 (203) Special Topics: Computational Data Modeling (Prof Chandra). Visualization skills to interpret data and present in meaningful ways.

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predictive analytics with decision trees

Predictive Analytics with Decision Trees

Professors Leo Pipino & Luvai Motiwalla

OIS Department, MSB

16.711 (203) Special Topics: Computational Data Modeling (Prof Chandra)

skills categories are in demand for big data

Visualization skills to interpret data and present in meaningful ways

Executive and management skills to know when and how to use data for making decisions

Domain strategy skills to develop the right questions, determine which data is important

Tool developers to mask the complexity of data and analytics to lower skill boundaries

Skills Categories are in Demand for “Big Data”

Data experts to manipulate and integrate big data

Mathematical and operations research to develop analytics algorithms

four key skills in analytics
Four key skills in Analytics

Analytics and Big Data

The Business Analyst

Applies business intelligence, predictive analytics, and other techniques to turn information into business insight

The Data Scientist

Combines the skills needed to collect, store, manage, and understand patterns and trends in data

60% of enterprises face a shortage of business analytics skills today

40% of enterprises report a skills shortage in ability to manage information

Information Security

Software Engineering & Mobile Dev

The Cyber Security Professional

Requires a broad portfolio of security skills and systems thinking applied to business priorities

The Next Generation Software Engineer

Employs the skills and methodologies needed to keep pace with the rapidly evolving software engineering discipline

39% of organizations adding IT staff plan to hire information security professionals

65% of enterprises face a shortage of mobile development skills today

Source: IBM Tech Trends report 2012

what is predictive analytics
What is Predictive Analytics?
  • Discover relevant, new patterns with speed and flexibility.
  • Analyze data to find useful insights.
  • Make better decisions and act quickly.
  • Monitor models to verify continued relevance and accuracy.
  • Manage a growing portfolio of predictive assets effectively
components of predictive analytics and data mining
Components of Predictive Analytics and Data Mining
  • Exploratory Data Analysis – Visually explore data sets of any size to spot trends, patterns and hidden insights that you can use to design a strategy, confirm a hypothesis or identify a new idea.
  • Model Development and Deployment – Streamline the data mining process to create highly accurate descriptive and predictive analytic models.
  • High-Performance Data Mining – Generate accurate, timely insights and solve complex problems using big data.
  • Credit Scoring – Build, validate and deploy credit risk models using in-house expertise.
  • Analytics Acceleration – Produce faster results and improve data governance with in-database analytics.
  • Scoring Acceleration – Maximize the performance and accuracy of your analytic models.
  • Model Management and Monitoring – Create, manage, deploy, monitor and operationalize analytical models.
  • Text mining applies analytical techniques to text-based documents. The knowledge gleaned from data and text mining can be used to fuel strategic decision making.
decision trees
Decision Trees
  • Oldest machine learning model
  • Recursively divides training data sets into homogeneous buckets thru most discriminative dividing criteria
  • "homogeneity" is measured thru output variable
    • If its’ a numeric value, the measurement will be the variance of the bucket
    • If its’ a categorical value, the measurement will be the entropy or gini index of the bucket
decision tree algorithms quinlan
Decision Tree Algorithms - Quinlan
  • Based on Shannon’s definition of information (entropy)
  • Compute information needed (bits) to classify the set
  • Compute decrease in entropy for each attribute
  • Choose attribute that gives greatest decrease in entropy (gain of information) as first node
  • Repeat procedure for resulting subsets to select next node(s) of the tree
  • Repeat until leaves reached
slide9

IBM Analytics tools like SPSS Modeler turns information into insight and insight into business outcomes.

Transform

Align

Anticipate

Act

your organization around information

with confidence at the point of impact to optimize outcomes

see, predict and shape business outcomes

Deploy an information and big data strategy that flows from your business strategy.

Leveraging business analytics to deliver actionable insights

Embed analytics into your processes and empower a culture of data-driven decision making

  • Business Intelligence
  • Performance Management
  • Predictive and Advanced Analytics
  • Risk Analytics
  • Sentiment Analytics
  • Big Data Analytics
  • Content Analytics
  • Web and Digital Analytics
  • Online Benchmark
  • Spend Analytics
  • Decision Management
  • Advanced Case Management
  • Digital Marketing Optimization
  • Cross-channel Selling and Marketing
  • Pricing, Promotion, and Assortment Optimization
  • Marketing Performance Optimization
  • Supply Chain Optimization
  • Organization and Workforce Transformation
  • Big Data Platform
  • Data Warehousing
  • Information Integration and Governance
  • Data Management
  • Enterprise Content Management
  • Defensible Disposal

Learn

slide10

IBM’s Business Analytics Software

http://www-03.ibm.com/ibm/university/academic/pub/page/business_analytics