Data Analysis in Python for Lean Six Sigma Professionals

Unlock Advanced Data Analysis Skills with Python for Six Sigma Professionals

As a Lean Six Sigma Professional, you're already proficient in Six Sigma Data Analysis using tools like Minitab, Excel, JMP, or SPSS. In today's data-driven world, Data Science skills are highly sought after. Leverage your existing expertise and gain a significant advantage by mastering Python for Six Sigma. Don't be at a disadvantage – expand your analytical capabilities with this comprehensive Python course!

What You'll Get in This Course:

• Step-by-Step Python Setup: We guide you through the entire process, from Python installation to performing all essential Six Sigma Data Analysis techniques.
• No Programming Experience Required: This course is designed for individuals with no prior programming experience.
• Data Manipulation Fundamentals: Gain a solid understanding of data manipulation techniques before diving into advanced analysis.
• Hands-on Practice: Download all Python source files to practice and reinforce your learning.
• Real-World Case Study: Apply your new skills to a complete, end-to-end Six Sigma Analysis case study.

Course Curriculum:

This course covers a wide range of Six Sigma Tools implemented using Python:

• Data Manipulation in Python: Learn to clean, transform, and prepare your data for analysis.
• Descriptive Statistics: Master techniques like Histograms, Distribution Curves, and Confidence Levels to understand your data.
• Data Visualization: Create insightful visualizations using:
  • Boxplots
  • Stem & Leaf Plots
  • Scatter Plots
  • Heat Maps
• Inferential Statistics:
  • Pearson’s Correlation
  • Multiple Linear Regression
  • ANOVA – t-tests (1-sample, 2-sample), and Paired t-tests
  • Proportions Test – 1-sample, 2-sample
  • Chi-square Test
• Statistical Process Control (SPC): Implement Control Charts for monitoring process stability:
  • mR charts (Moving Range)
  • XbarR charts (Moving Range of Means)
  • XbarS charts (Moving Range of Variances)
  • NP charts (Number of Defects)
  • P charts (Proportion of Defective)
  • C charts (Count of Defects)
  • U charts (Defects per Unit)

Python Packages You'll Use:

• Numpy: For numerical computing.
• Pandas: For data manipulation and analysis.
• Matplotlib: For basic plotting.
• Seaborn: For advanced data visualization.
• Statsmodels: For statistical modeling.
• Scipy: For scientific computing.
• PySPC: For Statistical Process Control chart generation.
• Stemgraphic: For creating stem and leaf plots.