Six Sigma Certification with Business Analytics- Henry Harvin® in Philadelphia

Module 1: Evaluation & Introduction to Lean Six Sigma
- History & Evolution of Six Sigma
- Six Sigma & Lean Definition
- COPQ
- Variation
- DMAIC Phases
- Six Sigma Roles & Responsibilities
Module 2: Define
- Project Charter
- Project Charter Contents
- Process Mapping
- SIPOC
- Identifying Customers & VOC
- Establishing VOC to CTQ
- KANO Model
- RACI Model
Module 3: Measure
- 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: Analyze
- 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
- Improvement Strategy
- Regression
- Correlation
- Scatter Plot
- Brainstorming
- 5S
- FMEA
- Piloting Solutions
- Automation
- Regression
- Correlation
- Scatter Plot
- Brainstorming
- Automation
- Mistake Proofing
Module 6: Control
- Statiscal Process Control
- SPC Selection Process
- Control Plan
Module 7: Analytics - General
- 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
- 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
- Basic Data Structures & Programming Constructs
- Libraries
- NumPy
- Pandas
- Matplotlib
Module 10: Pre-Process and Descriptive Summary
- Pre Processing Data
- 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
- 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
- Selection of Graph
- Basic Graphs (histogram, Bar Plot, 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: Modeling Techniques & Linear Regression
- Modeling Techniques
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodels
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
Module 14: 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
- 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
- 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
- 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
- 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 (Six Sigma Green Belt + Python Business Analyst)

Lean Six Sigma Combo ( Six Sigma Green Belt + Python Business Analyst) Course in Philadelphia

25 Nov 2024

60 Hours

25 Nov 2024

60 Hours

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Know the complete offerings of our Lean Six Sigma Combo (Six Sigma Green Belt + Python Business Analyst) in Philadelphia

Key Highlights

60 Hours of Instructor-Led Sessions

20 Hours of Live Interactive Doubt Solving Sessions

10 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

Job-Ready Portfolio of 6 Capstone Projects

142 Auto-Graded Assessments

12+ Industry Case Studies

90 Guided Hands-On Exercises

6+ Assignments and Mini Projects

1 Mock Interview and 1 Hackathon

Get 1-Year Gold Membership of Henry Harvin® Quality Management Academy

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage

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10 Hours of Mentorship by Industry Experts

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About the Lean Six Sigma Combo (Six Sigma Green Belt + Python Business Analyst) in Philadelphia

Lean Six Sigma Combo (Six Sigma Green Belt + Python Business Analytics) Course

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Curriculum For Lean Six Sigma Combo (Six Sigma Green Belt + Python Business Analyst) in Philadelphia

Module 1: Evaluation & Introduction to Lean Six Sigma

Module 2: Define

Module 3: Measure

Module 4: Analyze

Module 5: Improve

Module 6: Control

Module 7: Analytics - General

Module 8: Python Environment

Module 9: Basic Programming and Data Structures

Module 10: Pre-Process and Descriptive Summary

Module 11: Statistics

Module 12: Graphical Representation of Data

Module 13: Modeling Techniques & Linear Regression

Module 14: Logistic Regression

Module 15: Classification

Module 16: Cluster Analysis

Module 17: Association Rule Analysis

Module 18: Text Mining

Admission Closes On 25 Nov 2024

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What you'll Learn in this course

Process Improvement Methodologies

Performing Complex Data Analysis

Organizational Improvements

Project Management and Leadership Skills

Market Trend Analysis

Manufacturing Process Optimization

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