Data Analytics for Lean Six Sigma

Course Overview

This course, Data Analytics for Lean Six Sigma, provides an introduction to data analytics techniques that are typically useful within Lean Six Sigma improvement projects. Participants will learn how to analyse and interpret data gathered within such a project, using Minitab. The course will also provide an overview of Lean Six Sigma and its applications.

Throughout the course, emphasis will be placed on the use of data analytics tools and the interpretation of the outcome. Examples from actual Lean Six Sigma projects will be used to illustrate the tools. No mathematical background will be discussed.

The setting chosen for the data example is a Lean Six Sigma improvement project, however the data analytics tools are applicable in a broader setting.

Dr. Inez Zwetsloot and the IBIS UvA team wish participants the best of luck in this course.

[Applications]
Upon completion of this course, participants are able to apply the data analytics techniques they have learned to analyse and interpret data gathered within Lean Six Sigma improvement projects. They are also able to use Minitab to analyse the data. Furthermore, participants are able to apply the data analytics techniques they have learned in a broader setting apart from improvement projects.

[Career Paths]
1. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques to identify trends and patterns in data sets, and then present their findings to stakeholders. Data Analysts are in high demand as businesses increasingly rely on data-driven decision making.

2. Business Process Analyst: Business Process Analysts are responsible for analyzing and improving business processes. They use Lean Six Sigma and other process improvement techniques to identify areas of improvement, develop solutions, and implement changes. Business Process Analysts are essential for organizations looking to streamline their operations and increase efficiency.

3. Quality Assurance Manager: Quality Assurance Managers are responsible for ensuring that products and services meet quality standards. They use Lean Six Sigma and other quality management techniques to identify areas of improvement, develop solutions, and implement changes. Quality Assurance Managers are essential for organizations looking to maintain high standards of quality.

4. Data Scientist: Data Scientists are responsible for collecting, organizing, and analyzing large amounts of data to identify trends and patterns. They use a variety of tools and techniques to uncover insights from data sets, and then present their findings to stakeholders. Data Scientists are in high demand as businesses increasingly rely on data-driven decision making.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of skills in data analysis, data visualization, and machine learning. It also covers topics such as statistics, programming, and database management. This degree path is becoming increasingly popular as businesses and organizations are recognizing the value of data-driven decision making.

2. Master of Science in Business Analytics: This degree path focuses on the application of data analytics to business problems. It covers topics such as predictive analytics, data mining, and optimization. This degree path is ideal for those who want to use data to make informed decisions in the business world.

3. Master of Science in Artificial Intelligence: This degree path focuses on the development of skills in artificial intelligence and machine learning. It covers topics such as natural language processing, computer vision, and robotics. This degree path is ideal for those who want to use data to create intelligent systems that can make decisions autonomously.

4. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced skills in data analysis, data visualization, and machine learning. It also covers topics such as statistics, programming, and database management. This degree path is ideal for those who want to pursue a career in research and academia.