Six Sigma Green Belt with Marketing Analytics- Henry Harvin®

Module 1: Evaluation and Introduction to Lean Six Sigma
This module will guide the candidate through the basics of Lean & Six Sigma. Know about the COPQ, Variation and DMAIC Phases. Gain knowledge about the various necessary Roles & Responsibilities of Lean Six Sigma
- History & Evolution of Six Sigma
- Six Sigma & Lean Definition
- COPQ
- Variation
- DMAIC Phases
- Six Sigma Roles & Responsibilities
Module 2: Define
This module will help the candidate with comprehensive knowledge about the DMAIC Phase: Define. Learn about the project charter and its content. Gain knowledge about the SIPOC, KANO model, RACI model and establish VOC to CTQ
- Project Charter
- Project Charter Contents
- Process Mapping
- SIPOC
- Identifying Customers & VOC
- Establishing VOC to CTQ
- KANO Model
- RACI Model
Module 3: Measure
This module will help the candidate with comprehensive knowledge about the DMAIC Phase: Measure. Learn about Data Classification and its type. Be acquainted with sampling strategies. Learn extensively about the mean, median, mode, range, variance
- What is Data?
- Data Classification & Type
- Data collection plan
- Sampling & Sampling Strategies
- Mean
- Median
- Mode
- Range
- Variance
- Standard Deviation
- Case Study – Basic Statistics
- Normal Distribution
- Testing Normality
- Histogram
- Dot Plot
- Box Plot
- Time Series Plot
Module 4: Analyse
This module will help the candidate with comprehensive knowledge about the DMAIC Phase: Analyse. Learn about Measurement System Analysis, Accuracy & Precision, Repeatability, and Reproducibility. Gain mastery on the 7QC tools and hypothesis testing. Be aware of various risks such as Alpha Risks and Beta Risks
- Measurement System Analysis
- Accuracy & Precision
- Repeatability
- Reproducibility
- Gage R & R study
- Process Capability
- Capability Formulas
- DPMO, DPU, DPO
- Rolled Throughput Yield
- 7 QC Tools
- Central Limit Theorem
- Hypothesis testing
- Alpha risk
- Beta Risk
- t-test
Module 5: Improve
This module will help the candidate with comprehensive knowledge about the DMAIC Phase: Improve. Gain extensive knowledge about the Improvement techniques and Strategies. Learn about regression analysis, 5S, Piloting Solutions, and more
- Improvement Strategy
- Regression
- Correlation
- Scatter Plot
- Brainstorming
- 5S
- FMEA
- Piloting Solutions
- Automation
- Mistake Proofing
Module 6: Control
This module will help the candidate with the comprehensive knowledge about the DMAIC Phase: Control. Learn about the various techniques for Statistical Process Control. Develop a Control Plan and initiate an SPC Selection Plan
- Statiscal Process Control
- SPC Selection Process
- Control Plan
Module 7: Analytics- General
This module will guide the candidate with the knowledge of Analytics. Learn about the different roles in Analytics. Know about the tools and techniques in Analytics. Gain knowledge about Data Science, Data Mining, STatistics, MAchine learning, and more. Learn about the CRISP Modeling Framework.
- What is Analytics (BI, BA, Levels, etc)
- Why Analytics (Appl in various domains..)
- Different Roles in Analytics
- Tools and Techniques in Analytics
- Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
- CRISP Modeling Framework
- Scales of Measurements
Module 8: Python Environment
This module will help the candidate to gain knowledge about the Python Environment. Learn about Ananconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
Module 9: Basic Programming and Data Structures
This module will help the candidate with knowledge of basic programming and data structures. Gain extensive knowledge about Libraries, Numpy, pandas, Matplotib
- Basic Data Structures & Programming Constructs
- Libraries
- Numpy
- Pandas
- Matplotlib
Module 10: Data Manipulation and Descriptive Summary
This module will guide the candidate with the knowledge of Data Manipulation and Descriptive summary. Know about Group summaries, crosstab, pivot, reshape data and manage missing values. Learn to manage indexes in Pandas, Scaling of data, and more
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Various types of Joins, merge
- Managing indexes in pandas
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
Module 11: Statistics
This module will guide the candidate through the basics of statistics in Marketing Analytics. Learn extensively about Hypothesis testing, Probability distribution, and Sampling Techniques
- Basic Statistics (mean, median, mode)
- Other Statistics (sd, var, quantile, skewness, kurtosis)
- Hypothesis Tests (t-test, Chi-sq tests, etc)
- Probability Distributions (normal, binomial, etc)
- Sampling Techniques
Module 12: Graphical Representation of Data
This module will guide you through the techniques of Graphical Representation of Data. Learn about the selection of graphs and types of graphs. Manage plot parameters and advanced graphs such as correlations, heatmap, mosaic, and more
- Selection of Graph
- Basic Graphs (histogram, barplot, boxplot, pie, etc)
- Libraries (matplotlib, seaborn, plotline)
- Managing plot parameters(size, title, axis, legend, etc)
- Advanced Graphs (correlation, heatmap, mosaic, etc)
- Exporting graphs
Module 13: Linear Regression
This module will guide you through the basic understanding of Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
Module 14: Logistic Regression
This module will guide the learner with knowledge of Logistic Regression. Know the metrics of logistic regression. Predict the probability of DV on IV. Know extensively about applications of Logistic regression
- Difference between Linear and Logistic
- Logistic Regression
- Metrics of Logistic Regression (confusion matrix, ROC curve)
- Predict the probability of DV on IV
- Applications of Logistic Regression
Module 15: Classification
This module will guide the candidate with the knowledge of classification in Marketing Analytics. Understand the tree from the plot and know about the classification tree. Learn to improve tree accuracy using random forests. Know the applications of the decision tree, KNN, Neural Networks, SVM, and more
- Difference between classification and regression decision trees from CART models
- Understanding tree from the plot
- Classification Tree - predict class, plot, accuracy
- Regression Tree - predict numerical value, plot, RMSE
- Improving tree accuracy using Random Forests
- Bagging and Boosting
- Applications of Decision Tree
- KNN (k-nearest neighbors)
- Neural Networks
- Gradient Descent
- SVM (Support Vector Machine)
Module 16: Cluster Analysis
This module will guide you through the knowledge of Cluster Analysis. Know about the Clustering for grouping data and its types. Learn about extracting data in clusters and application of clustering
- Clustering for Grouping Data
- Types - Hierarchical & Non-Hierarchical
- K Means - output metrics (iter, error, plot)
- Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
- Extracting the data in clusters, Cluster Centers
- Applications of Clustering
Module 17: Association Rule Analysis
This module will guide you through the knowledge of the Association Rule analysis. Learn to apply AR to the grocery store for market basket analysis. Know about the frequent Itemsets and rules and application of AR
- Applying AR to the grocery store for Market Basket Analysis
- Metrics- Support, Confidence, Lift
- Frequent Itemsets and Rules, Filtering rules
- Applications of AR
Module 18: Text Mining
This module will guide the candidate through the understanding of Text Mining. Manage unstructured data and extract tweets from Twitter and words for sentiment analysis. Know the application of text mining
- Managing Unstructured Data, Unstructured to Structured Data
- Extracting Tweets from Twitter
- Extracting words for Sentiment Analysis
- Wordcloud to visualize the frequency of occurrence of words in the text
- Applications of Text Mining

Know the complete offerings of our Lean Six Sigma Combo (Green Belt + Marketing Analytics Practitioner)

Checkout Learner's Experiences on LinkedIn

Ashwani
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Upender
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RAMA SRIVIDYA
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SUBHASH CHAND
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AUROBINDA
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KALLOL
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Sindhura
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