Data Science with Lean six sigma Yellow 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
  • Comprehending and creating the SIPOC Diagram
  • Overview of Process Mapping
  • C&E Matrix
  • Effects Analysis along with Failure Modes
  • Fundamental Statistics
  • Minitab Introduction
  • Developing Graphs (fundamental quality tools)

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

Clustering

K-means Clustering

Hierarchical Clustering

  • Data Mining
  • Association Rules
  • Recommendation Engines
  •     Module1: Comprehending and creating the SIPOC Diagram
  •     Module 2: Overview of Process Mapping
  •     Module 3: C&E Matrix
  •     Module 4: Effects Analysis along with Failure Modes
  •     Module 5: Fundamental Statistics
  •     Module 6: Minitab Introduction
  •     Module 7: Developing Graphs (fundamental quality tools)