Certified Data Science With Lean Six Sigma Yellow Belt

·       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

o   Introduction to Python

o   Python installation & configuration

o   Python Features

o   Basic Python Syntax with implementation

o   Statements, Indentation, and Comments

·       Data Analytics using R 

o   Introduction to R

o   RStudio installation & configuration

o   Basic Python Syntax 

o   Basic visualization and data analysis

·       Statistics and Mathematics for Machine Learning

o   Statistical Inference

o   Descriptive Statistics

o   Introduction to Probability, Conditional probability, Bayes theorem

o   Probability Distribution

o   Introduction to inferential statistics

o   Normality, Normal Distribution

o   Measures of Central Tendencies

o   Hypothesis Testing

o   Data visualization using python

·       Machine Learning in Python

o   Machine Learning introduction

o   Machine Learning applications & use-cases

o   Machine Learning Flow

o   Machine Learning categories

o   Exploratory data analysis

o   Data cleaning and Imputation Techniques 

o   Linear regression

o   Gradient descent

o   Model evaluation

·       Supervised Learning 

o   What is Supervised Learning?

o   Logistic Regression in Python

o   Classification & implementations

o   Decision Tree

o   Different algorithms for Decision Tree Induction

o   How to create a Perfect Decision Tree

o   Confusion Matrix

o   Random Forest

o   Tree based Ensemble

o   Hyper-parameter tuning

o   Evaluating model output

o   Naive Bayes Classifier

o   Support Vector Machine

·       Unsupervised Learning

o   What is Unsupervised Learning

o   Clustering

o   K-means Clustering

o   Hierarchical Clustering

·       Data Mining

o   Association Rules

o   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)