Data Science with Lean six sigma Master Black Belt

3 Months

  • Python for Data Science
  • Data Analytics using R
  • Statistics and Mathematics for Machine Learning
  • Machine Learning in Python
  • Supervised Learning
  • Unsupervised Learning
  • Data Mining
  • Association Rules
  • Recommendation Engines
  • Organizational Competencies for Deployment
  • Project Portfolio Management
  • Training Design and Delivery
  • Coaching and Mentoring Responsibilities
  • Executives and Champions
  • Teams and Individuals
  • Advanced Data Management and Analytic Methods
  • Data Management and Analytics

Course Outline

  •     Introduction
  • Python for Data Science

Introduction to Python

Python installation & configuration

Python Features

Basic Python Syntax with implementation

Statements, Indentation, and Comments

  • Data Analytics using R 

Introduction to R

RStudio installation & configuration

Basic Python Syntax 

Basic visualization and data analysis

  • Statistics and Mathematics for Machine Learning

Statistical Inference

Descriptive Statistics

Introduction to Probability, Conditional probability, Bayes theorem

Probability Distribution

Introduction to inferential statistics

Normality, Normal Distribution

Measures of Central Tendencies

Hypothesis Testing

Data visualization using python

  • Machine Learning in Python

Machine Learning introduction

Machine Learning applications & use-cases

Machine Learning Flow

Machine Learning categories

Exploratory data analysis

Data cleaning and Imputation Techniques 

Linear regression

Gradient descent

Model evaluation

  • Supervised Learning 

What is Supervised Learning?

Logistic Regression in Python

Classification & implementations

Decision Tree

Different algorithms for Decision Tree Induction

How to create a Perfect Decision Tree

Confusion Matrix

Random Forest

Tree based Ensemble

Hyper-parameter tuning

Evaluating model output

Naive Bayes Classifier

Support Vector Machine

  • Unsupervised Learning

What is Unsupervised Learning


K-means Clustering

Hierarchical Clustering

  • Data Mining
  • Association Rules
  • Recommendation Engines
  • Organizational Competencies for Deployment – This module will guide you through the organizational Competencies for deployment. Learn to Apply systems thinking to anticipate the effect that components of a system can have and execute Leadership Roles. Support functioning of Master Black Belts through plans. Learn about the organizational performance metrics.

Organizational Design

Executive and Team

Organizational Challenges

Organizational Change Management

Organizational Feedback

Organizational Performance Metrics

  • Project Portfolio Management – Learn about Project Portfolio Management in this module. Evaluate critical projects and apply phases of project management life cycle. Learn to create document tracking tools and more. Execute positioning of multiple projects and measure the performance

Project Management Principles and Life Cycle

Project Portfolio Infrastructure and Management

Project Portfolio Financial Tools

  • Training Design and Delivery – This module will guide you through various training design and delivery methodologies. Discover training requirements by using tools as a gap analysis & design training plans. Learn to outsource training methods and monitor the training effectiveness

Training Needs Analysis

Training Plan Elements

Training Materials and Curriculum Development

Training Program Effectiveness

  • Coaching and Mentoring Responsibilities – Learn about the various Coaching and mentoring responsibilities in this module. Candidates will use coaching, mentoring and intervention skills to create a career progression ladder for belts. Learn to create guidelines for project reviews
  • Executives and Champions
  • Teams and Individuals
  • Advanced Data Management and Analytic Methods – This module will equip you with the knowledge of advanced data management and analytic methods. Learn about the measurement Systems analysis, Process capability, and more. Gain skills and knowledge of Design of experiments and data management and analytics

Measurement Systems Analysis (MSA), Process Capability, and Control

Measuring and Modelling Relationships Between Variables

Design of Experiments (DOE)

Data Management and Analytics

DFSS (Design for Six Sigma)