Skills Transfer

pphoto2

 

Based on my experience as successful analytical practitioner I have put together following skill transfer modules:

 

MODULE1: DATA SCIENCE CLASS AND MENTORSHIP PROGRAM

  • Introduction to field of data science (day 1)
    • Overview of basic principles of knowledge deduction and induction from the data
    • Overview of Data Science
    • Overview of Analytical Science
    • High level overview of 5 pillars of knowledge

 

  • Knowledge Pillar 1 – methodologies and best practices (day 2 )
    • Project methodologies
    • Process Methodologies
    • Best and Worst of data science practices

 

  • Knowledge Pillar 2 – Data Engineering / Preparation (day 3)
    • Purpose of data engineering
    • Foundational overview of Data Measurements in respect to IV (Information Value)
    • Different data preparation techniques for different purposes
    • Mathematical transformations
    • Advanced transformations (Weight Of Evidence, Knowledge compression)
    • Creative Transforms
    • Missing Value Challenges
    • Data reduction
    • Variable Selection

 

  • Knowledge Pillar 3 – Analytical disciplines, techniques, tasks, technologies and software) (day4 and 5)
    • Reporting, Monitoring, Stat Analysis, Advanced Analytics and Data Mining, Forecasting, Operational Research
    • Analytical Tasks (Description, Classification, Prediction, Association, Sequencing, Clustering, Time Series)
    • Overview of Hypothesis based and Discovery based inductive methods
    • Overview of most common analytical techniques (Regressions, Trees, Association and Sequencing algorithms, Clustering
    • Overview of Machine /Deep Learning and Neural Network technologies
    • High level overview of most common commercial software and programing languages for analytics (SAS Enterprise Miner, SAS, Python, R, IBM Modeler, Matlab)
    • Overview of Big-data technologies (Hadoop eco), issues and challenges and ways around it

 

  • Knowledge Pillar 4 – Analytical applications (day6 and 7)
    • Segmentation
    • Customer Lifetime Value (CLV)
    • Cross/sell and upsell
    • Reactivation
    • Marketing Response
    • Customer Churn
    • Fraud detection
    • Credit Risk
    • Policy Lapse

 

  • Knowledge Pillar 5 – Soft Skills (day 8)
    • Soft Skills
    • Do’s and Don’ts
    • Tricks and Tips and “war Stories”
    • Overview of Project documentation needed for successful implementation of data science projects

 

  • Practical (day 9 and 10)
    • Scope, build and Implement real-life advanced analytic project and take it through all the phases of project and process methodology steps. Use all the knowledge from first 8 days to put this together with my supervision and deliver effective business presentation.

 

MODULE 2: ANALYTICAL PROJECT METHODOLOGY - ANALYTICAL LIFECYCLE

  • Full steps of lifecycle/methodology
  • Documentation (statement of work, charter, scoping document..)
  • Best practice
  • Do's and Don'ts
  • Roles and responsibilities

 

MODULE 3: DATA PREPARATION AND ENRITCHEMENT FOR BIG-DATA ANALYTICS TRAINING:

  • Purpose of data preparation
  • Data preparation challenges and how to overcome them
  • Data structures
  • Variables measurements
  • Concept of information content
  • Variable reduction
  • Data quality from the analytical perspective
  • From simple to complex data derivations
  • Binning and classing strategies
  • Data mapping
  • Mathematical and power transformations
  • Creative transformations

 

MODULE 4: BIG DATA ANALYTICS PRIMER

  • Project methodologies
  • Model Building process method
  • Best Practices
  • Data preparation
  • Modeling techniques
  • Analytical applications
  • Pointers when dealing with big data
  • Success criteria and common pitfalls to avoid

 

 

ANALYTICAL APPLICATIONS COURSES

  • Overview of common analytical applications across industries (1 day)
  • Segmentation Primer course (1 day, 2 days)
  • Customer retention course (1 day, 2 days)
  • Customer churn primer for Telco's (1 day, 2 days)
  • Effective Market Basket Analysis course (1 day)
  • Fraud detection in banking course (card, online) (1 day, 2 days)
  • Policy lapse prediction in insurance industry (1 day)
  • Cross-sell and upsell primer (1 day)
  • Response modeling (both, classic and IRM approach covered) (1 day)
  • Customer Lifetime value and Customer Potential (1 day)