精益六西格码的数据分析

Data Analytics for Lean Six Sigma

1327 次查看
阿姆斯特丹大学
Coursera
  • 完成时间大约为 16 个小时
  • 初级
  • 英语, 其他
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

Welcome to this course on Data Analytics for Lean Six Sigma.

In this course you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course you are able to analyse and interpret data gathered within such a project. You will be able to use Minitab to analyse the data. I will also briefly explain what Lean Six Sigma is.

I will emphasize on use of data analytics tools and the interpretation of the outcome. I will use many different examples from actual Lean Six Sigma projects to illustrate all tools. I will not discuss any mathematical background.

The setting we chose for our data example is a Lean Six Sigma improvement project. However data analytics tools are very widely applicable. So you will find that you will learn techniques that you can use in a broader setting apart from improvement projects.

I hope that you enjoy this course and good luck!
Dr. Inez Zwetsloot & the IBIS UvA team

课程大纲

Data and Lean Six Sigma

This module introduces Lean Six Sigma and shows you where data and data analytics have their place within the DMAIC framework. It also introduces the software package Minitab. This package is used throughout the videos for data analytics. It is not mandatory to use this package. I just really like it!

Understanding and visualizing data

This module explains how to visualize data. It discusses visualizing single variables as well as visualizing two variables. You will learn to select the appropriate graph. For this it is essential to first learn the distinction between numerical and categorical data.

Using probability distributions

In this module on using probability distributions, you will learn how to quantify uncertainty. Furthermore you will learn to answer an important business question: “what percentage of products or cases meet our specifications?".

Introduction to testing

You will learn to model your CTQ and influence factor(s) and to use a decision tree to select the appropriate tool for data based testing of this model. Furthermore, causality is introduced.

Testing: numerical Y and categorical X

In this module on statistical testing, you will learn how to establish relationship between a numerical Y variable (the CTQ) and categorical influence factors (the X variables).

Testing: numerical Y and numerical Y

What is the relation between the length of stay and the age of a patient? In this module you will learn to answers these types of questions using statistical tests to relate a numerical CTQ (the Y variable) to a numerical influence factor (the X variable).

Testing: categorical Y

Finally you will learn how to test a relationship between a Y and a X variable whenever your Y variable (the CTQ) is a categorical variable.

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