Data Analysis in Python for Lean Six Sigma Professionals

🎓 Course Title: Data Analysis in Python for Lean Six Sigma Professionals

Course Headline:

👉 Perform Six Sigma Data Analysis using Python like a Data Scientist - No Programming Experience Needed! 👈

Why you should consider this PYTHON course?

As a Lean Six Sigma Professional, you are already adept at performing Six Sigma Data Analysis & Discovery using tools like Minitab, Excel, JMP or SPSS. But in today's data-driven world, knowing Python is a game-changer. It's the language that data scientists and analysts rely on to extract meaningful insights from data. Without Python skills, you're at a significant disadvantage.

What you will Get in this Course?

🎁 Step-by-Step Procedure: Starting with Python installation, we'll guide you through all the necessary steps to perform Six Sigma Data Analysis entirely within Python.

• No Programming Experience Needed: We've designed this course for professionals with no prior programming experience.
• Data Manipulation: Learn how to prepare your data for analysis using Python.
• Exposure to Python Packages: Get hands-on with Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, Scipy, PySPC, and Stemgraphic.
• Download All Source Files: Receive all the Python source files for your analysis, saving you time and effort.
• End to End Case Study: Work through a comprehensive Six Sigma Analysis Case Study from start to finish.

Course Curriculum:

🚀 Six Sigma Tools Covered using Python

• Data Manipulation in Python: Get your data ready for analysis with the right tools and techniques.
• Descriptive Statistics: Understand and visualize the central tendency, variability, and shape of your data.
• Visualization Techniques: Master plotting Histograms, Distribution Curves, Confidence levels, Boxplots, Stem & Leaf Plots, Scatter Plots, and Heat Maps.
• Statistical Analysis Tools: Learn Pearson’s Correlation, Multiple Linear Regression, ANOVA, T-tests (1t, 2t, Paired), Proportions Test (1P, 2P), Chi-square Test, and SPC (Control Charts - mR, XbarR, XbarS, NP, P, C, U charts).
• Python Packages: Dive into Numpy, Pandas, Matplotlib, Seaborn, Statsmodels, Scipy, PySPC, and Stemgraphic to perform your analysis.

Python Packages Overview:

• Numpy: For numerical computations in Python.
• Pandas: For data manipulation and analysis.
• Matplotlib: For creating static, interactive, and animated visualizations in Python.
• Seaborn: A statistical data visualization library based on Matplotlib.
• Statsmodels: For estimating and interpreting models for predicting future reactions.
• Scipy: Used for scientific computing and optimization.
• PySPC: An open-source tool for quality control and process monitoring.
• Stemgraphic: For plotting stem-and-leaf plots, box plots, and histograms.

Join us in this comprehensive course and transform your Six Sigma skills with the power of Python! 🚀📊🎉