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)