Data Analysis & Statistics Archives

About This Course:

Develop the skills necessary to create structured database environments using a relational database management system (RDBMS), such as MySQL, that incorporates basic processing functionality and allows for data management, data manipulation and data analysis. Learn about types of data and types of databases to store data, as well as design for scalability. You’ll also learn to prepare digital data storage using the relational model, including resolving integrity constraints, and proper assignments of primary and foreign keys. In addition, you’ll construct and analyze queries to address data requirements.

What You’ll Learn:

Understanding what SQL isHow to use SQL statements such as:SelectFromWhereHavingGroup byOrder byMaxMinCountCount distinctJoinsImport/exportReading schemasCreating database schemasImporting/exporting dataTroubleshooting queriesModifying tables and data structuresAnalyzing SQL outputs

Structured Database Environments with SQL

Discover Structured Query Language (SQL) programming basics in relation to database management and data manipulation. This course can provide you with an overview of topics like joins, database schemas, database design and importing data into a database.

16 Weeks 8-10 Hours per week

Complete Program $498.00

Next Start Open Access Until Dec 19, 2021

Program Details

Meet Your Instructors:

Scott Overmyer

Associate Dean of Information Technology at Southern New Hampshire University Dr. Overmyer started his career in the software field in 1983 with TRW, Inc. as a user interface designer and requirements engineer on the Space Defense Operations Center project in Colorado's Cheyenne Mountain NORAD Complex. After completing several government projects with TRW, he was appointed manager of TRW's Research and Technology Center in Virginia. He earned a Ph.D. in Information Technology (Information and Software Systems Engineering), while appointed as Research Instructor in George Mason University's Center of Excellence in Command, Control, Communications and Intelligence. Dr. Overmyer has nearly 10 years of industrial experience, followed by nearly 30 years of academic experience. During his academic career, he has been awarded 2 summer faculty fellowships at NASA's Johnson Space Center and has been a PI or co-PI on several National Science Foundation research grants. While an Associate Professor at Drexel University, he was also the Founding Director of the Pennsylvania Governor's School for Information Technology, a 5-week summer residential program for talented high school students. Internationally, he served at Subject Coordinator for both Software Engineering and Information Systems while at Massey University in NZ, and a (founding) Professor of Computer Science at Nazarbayev University in Astana, Kazakhstan, as well as Acting Chair of the Physics Department there. He is currently an Associate Dean for Information Technology for Southern New Hampshire University, and works remotely from his farm in South Dakota. On Dr. Overmyer’s free time, he enjoys researching artificial intelligence for automatic student assessment, online poker, and wine making.

Ben Tasker

Technical Program Facilitator – Data Science and Data Analytics at Southern New Hampshire University Ben Tasker owns his own consulting business, where he enjoys solving complex data science problems with innovative solutions for clients ranging from Fortune 500 companies, small businesses and even the White House. He believes any company can reach the next phase of machine learning, artificial intelligence or even basic data analytics and enthusiastically provides that expertise to his clients. He applies this knowledge daily as a technical program facilitator at Southern New Hampshire University, where he creates cutting edge data science programs. Ben also holds a clinical Professorship at the College System of New Hampshire, where he teaches data science topics at both the undergraduate and graduate level. Before he ventured into consulting and education, he was the manager of analytics and data warehousing for the Community College System of NH, where he managed 12 data scientists and helped developed an algorithm that kept students enrolled in college, flagging the students at the highest risk of failure. This flag allowed the colleges to deploy resources for each of those students, ultimately helping them complete college. He also earned a MS in Data Analytics and Data Science from the University of New Hampshire. In his free time, Ben can be found at his local Crossfit Gym, doing box jumps, hang power cleans and kettle bell swings

About This Course:

This course helps prepare you for positions that require the analysis of large data sets, providing the statistics foundation you’ll need for data analysis.

You’ll learn how to model real-world applications using statistical methods. You’ll also gain skills to set up those applications in statistical frameworks, solve the statistical problems using technology and apply those results to answer real business questions. By leveraging the pandas and matplotlib python libraries, you’ll gain exposure to tools used in data analysis, visualization, and data science.

What You’ll Learn:

Collect and classify dataSummarize data using visual and numeric techniquesQuantify answers to questions about estimating parameters using hypothesis test and confidence interval techniquesQuantify answers to questions about correlation between variables using linear regression techniques

MicroBachelors® Program in Business Analytics Foundations

Solve real business problems with scripting and statistics

Scripting with Python

Applied Statistics with Python

2 Courses 8 Months 8 - 10 Hours per week

Complete Program $996.00

Program Details

Meet Your Instructors:

Ben Tasker

Technical Program Facilitator – Data Science and Data Analytics at Southern New Hampshire University Ben Tasker owns his own consulting business, where he enjoys solving complex data science problems with innovative solutions for clients ranging from Fortune 500 companies, small businesses and even the White House. He believes any company can reach the next phase of machine learning, artificial intelligence or even basic data analytics and enthusiastically provides that expertise to his clients. He applies this knowledge daily as a technical program facilitator at Southern New Hampshire University, where he creates cutting edge data science programs. Ben also holds a clinical Professorship at the College System of New Hampshire, where he teaches data science topics at both the undergraduate and graduate level. Before he ventured into consulting and education, he was the manager of analytics and data warehousing for the Community College System of NH, where he managed 12 data scientists and helped developed an algorithm that kept students enrolled in college, flagging the students at the highest risk of failure. This flag allowed the colleges to deploy resources for each of those students, ultimately helping them complete college. He also earned a MS in Data Analytics and Data Science from the University of New Hampshire. In his free time, Ben can be found at his local Crossfit Gym, doing box jumps, hang power cleans and kettle bell swings

David Sze

Technical Program Facilitator – Mathematics at Southern New Hampshire University David Sze has been teaching statistics and mathematics courses, both online and on campus, for more than 15 years. His current position includes developing, teaching and overseeing statistics and mathematics courses at Southern New Hampshire University. **** Before entering university fields, he worked in the software and telecommunications industries for more than 20 years. He held positions as a director of technology and software consulting and testing and as a technical analyst specializing in applied stochastic techniques. David Sze has a Ph. D. in Mathematics (Probability) and an M. S. in Statistics.

About This Course:

The Python programming language is extremely powerful and commonly used to automate time-intensive activities/tasks for users. This makes Python a good skill to have for any job that requires automation to replace data in a file, rename multiple file names, update Excel spreadsheets or mine data from web pages. Python can be used as a steppingstone to enter some of the most exciting industries including data science, artificial intelligence, machine learning, software or full-stack development.

What You’ll Learn:

Translate requirements to solve problems computationallyWrite scripts using syntax and conventions in accordance with industry standard best practicesDevelop a fully functional program using industry-relevant tools

MicroBachelors® Program in Business Analytics Foundations

Solve real business problems with scripting and statistics

Scripting with Python

Applied Statistics with Python

2 Courses 8 Months 8 - 10 Hours per week

Complete Program $996.00

Program Details

Meet Your Instructors:

Gwen Britton

Associate Vice President, SNHU Global Campus STEM & Business Programs at Southern New Hampshire University

Dr. Gwen Britton has over 25 years in both private industry and academia within the science, technology, engineering and math (STEM) space. She has worked in varying positions spanning a gamut of content areas including software engineering, systems administration, network administration, data analysis, database administration and design, web development engineering, cyber-defense, mathematics and computer science.

Britton is passionate about expanding and growing STEM opportunities and access for individuals who otherwise would not have an opportunity to pursue a career in the STEM area. She has helped grow opportunities for non-traditional adult students from under-represented populations by spear heading virtual experiential learning opportunities and competitions in areas such as technology certification preparation, data analysis, internet of things and capture the flag. She has also helped organize and participate in events focused on under-represented K-12 student populations to engage and interest future STEM students in the field through events such as FIRST Robotics, the Hour of Code, Girls in Technology Day and the Science of Soccer.

Curtis George

Technical Program Facilitator for Computer Science at Southern New Hampshire University

Curtis George brings over 20 years of experience as a senior engineer working on various projects, from embedded systems for military aircraft to enterprise software for NASA/NOAA. Before working for SNHU, George worked as a senior software engineer for the NOAA Comprehensive Large Array-data Stewardship System (CLASS) project. He worked as a senior application engineer with NASA to create software for satellites for predicting hazardous weather patterns (GOES-R and JPSS), developed a roleplaying game to acclimate international students to American campuses and even created an award-winning Spanish verb conjugation mobile application. George started his career in the Navy on a special operations boat where he earned a Navy and Marine Corps Achievement medal, National Defense Service medal, Navy Expeditionary medal and a Meritorious Unit Commendation medal. He has worked for SNHU as the technical program facilitator for computer science for over 5 years. George earned his Bachelor of Science degree in computer science from Chapman University and his Master of Science degree in computer science from Nova Southeastern University. He is currently a PhD candidate for computer science at Northcentral University, which he expects to finish in 2021.

About this course

Want to study for an MBA but unsure of the basic data analysis still required? This online course prepares you for studying in an MBA program and in business generally.

Data analysis appears throughout any rigorous MBA program and in today’s business environment understanding the fundamentals of collecting, presenting, describing and making inferences from data sets is essential for success.

The goal of this course is to teach you fundamental data analysis skills so you are prepared for your MBA study and able to focus your efforts on core MBA curriculum, rather than continually playing catch-up with the underlying statistical knowledge needed.

We also hope that learning these data analysis skills will equip you with the ability to understand, to a greater degree, the data you encounter in your working lives and in the world around you – an essential life-skill in today’s data driven environment

This course assumes no prior knowledge of data analysis. Concepts are explained as clearly as possible and regular activities give you the opportunity to practice your skills and improve your confidence.

What you’ll learn

Presenting and summarising your data
Decision making under uncertainty
Data-based decision making
Modelling for decision making

Syllabus

Topic 1 – Presenting and summarising your data
Topic 2 – Decision making under uncertainty
Topic 3 – Data-based decision making
Topic 4 – Modelling for decision making

Meet your instructors

David Lefevre

David is the Director of the EdTech Lab at Imperial College Business School. He is also the course leader on the Business School’s Pre-study module in Maths. David holds an MSc in Computing Science and a PhD in the field of instructional systems from Imperial College London and, perhaps most importantly, a BSc in Mathematics from the University of East Anglia (UEA). David and his EdTech Lab team launched the Global Online MBA program in 2015 and have received awards along the way including a Gold award at the IMS Learning Impact awards in 2010 and an Effective Practice Award at the Sloan-C Blended Learning Conference in 2011.

Catarina Sismeiro

Dr. Catarina Sismeiro is an Associate Professor at Imperial College Business School where she teaches on the Full-Time and Executive MBA programs. Catarina’s research has been published in The Journal of Marketing Research, Management Science, and The International Journal of Research in Marketing and has won several awards. Previously she was an Assistant Professor at Marshall Business School, University of Southern California. She holds a PhD in Management from the Anderson School, UCLA, and a Licenciatura in Management from Faculdade de Economia do Porto (Portugal).

About MIT horizon

MIT Horizon is an expansive content library built to help you explore emerging technologies. Through easy-to-understand lessons, you’ll be guided through the complexities of the latest technologies and simplified expert-level concepts. Designed for both technical and non-technical learners, you can examine bite-size content that can lead to maximum career outcomes.

For a limited time, gain access to the complete MIT Horizon library.

Register today for exclusive entry.

About this course

Want to know how to avoid bad decisions with data?

Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

These big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.

We will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?

This course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor’s degree contemplating graduate study in a business field.

Opportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.

What you’ll learn

Variability in the real world and implications for decision making
Data types and data quality with appropriate visualizations
Apply data analysis to managerial decisions, especially in start-ups
Making effective decisions from no data to big data (what should we collect and then what do we do with all this data?)

Meet your instructors

Rick Cleary

Professor Rick Cleary is a statistician and mathematician with research and consulting interests in a variety of fields including sports, biomechanics, and statistical approaches to fraud detection and audit risk. Prior to coming to Babson College in 2013, Professor Cleary taught at St. Michael’s College in Vermont, Cornell University, Bentley University, and Harvard University. He has held many leadership positions in the Mathematical Association of America, including six years on the Executive Committee as Associate Treasurer and a term as chair of the Joint Data Committee. He is currently on the Nominations Committee and the Polya lecturer selection committee. Professor Cleary enjoys working with mathematics teachers at all levels to improve statistics education and he gives frequent talks and workshops on ways to encourage statistical thinking.

Nathan Karst

Dr. Nathan Karst received his B.S. in electrical and computer engineering from Franklin W. Olin College of Engineering in 2007 and his doctorate in applied mathematics from Cornell. He is an avid teacher and researcher, having won the Dean’s Award for Excellence in Undergraduate Teaching in 2014 and the Dean’s Award for Excellence in Scholarship in 2015. Most recently, his research has focused on the role of nonlinear dynamics in microvascular networks and event-scale streamflow recession variability.

Davit Khachatryan

Dr. Davit Khachatryan is an Assistant Professor of Statistics and Analytics at Babson College. He is an applied statistician with research interests in analyzing intellectual property data to study the formation and diffusion of knowledge in emerging industries. Davit’s current and former research has produced publications in academic, peer-reviewed journals such as Journal of the Royal Statistical Society (Series C), The American Statistician, IEEE Transactions on Engineering Management (forthcoming), and Quality and Reliability Engineering International. Prior to joining Babson College, Davit was a Senior Associate at the National Economics and Statistics practice of Pricewaterhouse Coopers (PwC). In the latter role he consulted in the area of predictive modeling and advanced data analytics, helping clients from financial, healthcare, and government sectors with building automatic predictive models and enhancing business intelligence solutions. Davit has earned his B.S. in Applied Mathematics and Informatics from Yerevan State University, M.S. in Statistics and Ph.D. in Management Science from the University of Massachusetts, Amherst.

George Recck

Mr. Recck has taught at Babson College since 1984. He currently serves as the Chair of the Business Analytics/Statistics Education special interest group for the American Statistical Association (ASA). Mr. Recck is also the founder of Total Information, Inc., an consulting firm specializing in providing information service to small businesses.

Babak Zafari

†Dr. Zafari is an Assistant Professor of Analytics and Statistics in the Math & Science Division. His area of interests are Predictive Modeling and Data Mining Methods for Business Applications, Bayesian Statistics, Healthcare Fraud Analytics and Online Auctions. Prior to joining Babson College, he was a Visiting Assistant Professor at The George Washington University School of Business teaching courses in Data Analysis and Decisions, Business Analytics and Data Mining. He was also a senior statistician consultant at Integrity Management Services where he was responsible for developing statistical models for fraud detection in Medicare and Medicaid programs. He received his B.S. in Applied Mathematics from Sharif University of Technology, M.S. in Operations Research/Computer Science from Bowling Green State University and Ph.D. in Decision Sciences from The George Washington University School of Business.

About MIT horizon

MIT Horizon is an expansive content library built to help you explore emerging technologies. Through easy-to-understand lessons, you’ll be guided through the complexities of the latest technologies and simplified expert-level concepts. Designed for both technical and non-technical learners, you can examine bite-size content that can lead to maximum career outcomes.

For a limited time, gain access to the complete MIT Horizon library.

Register today for exclusive entry.

About This Course:

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

What You’ll Learn:

The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?

Frequently Asked Questions:

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Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Meet Your Instructor:

Ansaf Salleb-Aouissi

Ansaf is a Lecturer in discipline of the Computer Science Department at the School of Engineering and Applied Science at Columbia University. She received her her BS in Computer Science in 1996 from the University of Science and Technology (USTHB), Algeria. She earned her masters and Ph.D. degrees in Computer Science from the University of Orleans (France) in 1999 and 2003 respectively.

About MIT Horizon:

MIT Horizon is an expansive content library built to help you explore emerging technologies. Through easy-to-understand lessons, you’ll be guided through the complexities of the latest technologies and simplified expert-level concepts. Designed for both technical and non-technical learners, you can examine bite-size content that can lead to maximum career outcomes.

For a limited time, gain access to the complete MIT Horizon library.

Register today for exclusive entry.

Course Overview:

Virtually all managerial and leadership positions in the digital economy increasingly rely on data-driven decision making. Recent studies have shown companies who adopt “Data-Driven Decision Management” achieve significant productivity gains over other firms.

Having a solid grasp of the end-to-end process of making effective decisions with data will give you an edge, both in performing such analyses yourself, as well as in effectively managing teams of business analysts and data scientists.

In this course, part of both the Digital Leadership and Digital Product Management MicroMasters programs, you will learn the tools and techniques to become a data-driven or “evidence-based” manager.

You will learn the process of reframing a business question as a data question, reasoning about what data might be of assistance and how to obtain it, integrating and cleaning the data, performing the analysis, deriving and communicating insights from the analysis, and building the managerial culture to operate in this way and create competitive advantages from enterprise data.

This course is unique in the sense that it aims squarely at the needs of a manager in an analytically focused enterprise by providing both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage.

Software Requirements

Completion of this course requires the use of Microsoft Power BI Desktop. Unfortunately, there is currently no version of Power BI Desktop for macOS or Linux operating systems. We encourage learners to secure access to a Windows environment, but if that is not possible, macOS and Linux users can run Power BI Desktop in a virtual Windows environment. The course provides steps for installing such an environment.

What You’ll Learn

Key analytic technologies and techniques, e.g. predictive modeling and machine learning, and how these can play a role in managerial decision making
How to effectively manage the analytical processes and use the results of these processes as the basis for making informed, evidence-based decisions
How companies can use analytics as the basis for creating value

Lebo Test

8 Weeks 3–6 hours per week

Complete Program $399.00

Next Start Oct 15, 2020 - Dec 12, 2020

Program Details

Prerequisites

Elementary college course in statistics.

Meet your instructors

John W. Byers

Professor of Computer Science at Boston University

John is Professor of Computer Science at Boston University and Founding Chief Scientist of Cogo Labs, founded in 2005. He received his Ph.D. in Computer Science from UC Berkeley in 1997 and his B.A. from Cornell University in 1991.

Chris Dellarocas

Richard C. Shipley Professor of Information Systems at Boston University

Chris is Richard C. Shipley Professor of Information Systems at the Questrom School of Business and Associate Provost for Digital Learning & Innovation at Boston University. He received his Ph.D. in Computer Science from MIT in 1996 and his Diploma in Electrical Engineering from the National Technical University of Athens, Greece in 1989.

Andrei Lapets

Director of Research Development and Research Scientist, Hariri Institute for Computing at Boston University

Andrei is a Research Scientist and Director of Research Development within the Hariri Institute for Computing, Director of the Software & Application Innovation Lab, and Adjunct Assistant Professor within the Department of Computer Science, all at Boston University. He earned his A.B. and S.M. degrees from Harvard University and his Ph.D. in Computer Science from Boston University.

About This Course:

While big data infiltrates all walks of life, most firms have not changed sufficiently to meet the challenges that come with it. In this course, you will learn how to develop a big data strategy, transform your business model and your organization.

This course will enable professionals to take their organization and their own career to the next level, regardless of their background and position.

Professionals will learn how to be in charge of big data instead of being subject to it. In particular, they will become familiar with tools to:

assess their current situation regarding potential big data-induced changes of a disruptive nature,
identify their options for successfully integrating big data in their strategy, business model and organization, or if not possible, how to exit quickly with as little loss as possible, and
strengthen their own position and that of their organization in our digitalized knowledge economy

The course will build on the concepts of product life cycles, the business model canvas, organizational theory and digitalized management jobs (such as Chief Digital Officer or Chief Informatics Officer) to help you find the best way to deal with and benefit from big data induced changes.

During the course, your most pressing questions will be answered in our feedback videos with the lecturer. In the assignments of the course, you will choose a sector and a stakeholder. For this, you will develop your own strategy and business model. This will help you identify the appropriate organizational structure and potential contributions and positions for yourself.

What You’ll Learn:

Identify the stakeholders and characteristics of your sector in the era of big data
Identify potential big data induced changes in strategy, business model, organization and job descriptions
Substantially change existing strategy, business model, organization or adopt new ones as required
Find and develop strategically important tasks for yourself in your organization

Syllabus:

Week 1:

1. Challenges and Opportunities of big data

1.1 Big data in the digitalized knowledge economy

1.2 Are firms ready for big data?

1.3 The machine learning revolution

Week 2:

2. Stakeholders and Sectors

2.1 Stakeholders: Private or public goals

2.2 Sectors: Disruptive or incremental changes induced by big data

2.3 Assignment Part A

Week 3:

3. Big data upending strategy and innovation

3.1 Conventional wisdom on strategy and innovation

3.2 Wisdom considering the disruptive power of big data

3.3 Assignment Part B

Week 4:

4. Transforming building blocks of the business model canvas

4.1 What does big data mean for the different building blocks of the business model canvas?

4.2 Examples

4.3 Assignment Part C

Week 5:

5. Transforming the organization: CIO, CDO or CEO in the lead?

5.1 What does big data mean for the organization of your company?

5.2 Who leads the way?

5.3 Examples

5.4 Assignment Part D

Week 6:

6. Wrapping up

6.1 Ready for big data

6.2 Feedback

Meet Your Instructors:

Marijn Janssen

Marijn Janssen is full professor in ICT & governance and chair of the Information and Communication Technology section of the Technology, Policy and Management Faculty of TU Delft. His research interests are in the field of orchestration, governance, shared services, intermediaries, open data and infrastructures within constellations of public and private organizations. Marijn was recently designated as the top researcher in the area of eGovernance, and has been recognized as such several times in the last decade.

Claudia Werker

Claudia Werker is an associate professor in Economics of Technology and Innovation at the Department Technology, Policy and Management at Delft University of Technology. She is also visiting professor at the Research Area Technology, Innovation, Marketing and Entrepreneurship (TIME) at RWTH Aachen University. She has been teaching Bachelor, Master and PhD students in the field of Economics of Innovation and Technology. Claudia Werker received her PhD-degree in Economics (Dr. rer. pol.) from Freiberg University of Technology, Germany (summa cum laude). Starting with her PhD thesis she has been studying the creation and dissemination of innovation and knowledge in innovation systems. Currently, her recent research focuses on management of technology and innovation, the economic effects of big data and design for values.

Scott Cunningham

Scott Cunningham joined the faculty in 2004. Prior to joining TU Delft, he worked in the computer and software industry, creating analytical models for commercial clients. His work on national innovation indicators helps inform policy for the governments of the U.S., the U.K. and Malaysia. Scott Cunningham is interested in operations research and decision sciences approaches for policy making. In particular, he is interested in probabilistic models of social exchange. Other interests include building multi-actor systems theory through the economic sociology and innovation policy literatures. A recent publication is Tech Mining (with Alan Porter), a book on assessing new technology developments.

About This Course:

Learn about Lean Management, a customer-centric methodology that improves processes by eliminating waste and focusing on value-added tasks.

This course will introduce the main tenets of the Toyota Production System, which includes Just-in-Time manufacturing, quality management tools, and the critical concept of Kaizen, the Japanese practice of continuous improvement. You will also learn about the key organization and managerial approaches that are used in Lean.

You will learn how to analyze process flows in order to establish process capacity and identify the process bottleneck. You will then calculate resource utilization and cycle time to evaluate the impact of set up times, batching, defects and reworks on key process performance measures, including inventory, flow rate and flow time.

We will also discuss the impact of key concepts of Lean, including Heijunka, Kanban, Jidoka, Andon, Poka Yoke, and 5S, which help achieve increased productivity and quality.

Upon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.

What You’ll Learn:

The history and background of Lean production and the complementing elements of quantity and quality control.
To measure production performance and how defects and waste degrade performance.
To understand the importance and role in Lean Production of the customer “Takt.”
To improve process performance through the application of Lean principles, including setup time reduction, batch optimization, and defect elimination.
To explain the importance of Total Productive Maintenance and the widely-used metric Overall Equipment Effectiveness.
To understand the difference between push- and pull-systems and how the implementation of pull-systems reduces waste.
To apply elements of Lean production including Heijunka, Kanban, Jidoka, and Poka Yoke.
To apply the 5S methodology for establishing and sustaining a productive work environment.

Professional Certificate in Lean Six Sigma Yellow Belt: Quantitative Tools for Quality and Productivity

Earn a TUM Lean Six Sigma Yellow Belt certificate

Six Sigma: Define and Measure

Six Sigma: Analyze, Improve, Control

Lean Production

3 Courses 6 Months 3 - 4 Hours per week

Complete Program $264.03

Program Details

Frequently Asked Questions:

Who offers this program?
The Professional Certificate ProgramSix Sigma and Lean: Quantitative Tools for Quality and Productivity is offered by edX and the Technical University of Munich (TUM).

How many courses are in the program?
There are three courses in this program:
Course1 QPLS1x – Six Sigma: Define and Measure
Course2 QPLS2x – Six Sigma: Analyze, Improve, Control
Course3 QPLS3x – Lean Production

Do I need to take the courses in order?
The courses should be completed sequentially, but it is not required that they are completed in any particular order. However, as Course2 QPLS2x – Six Sigma: Analyze, Improve, Control builds on the material in Course1 QPLS1x – Six Sigma: Define and Measure, we strongly recommend that you take QPLS1x before you take QPLS2x.

Which certification do I earn for this course?
You will earn the Verified Certificate forCourse3 QPLS3x – Lean Production.

What certificates do I earn with this program?
Upon successful completion of this program, learners will earn both theedX Professional Certificate for the program and the TUM Lean Six Sigma Yellow Belt certification. In order to achieve the TUM Lean Six Sigma Yellow Belt Certification it is mandatory to complete all 3 courses in the program and achieve a Verified Certificate in each. Then, automatically, you will earn the edX Professional Certificate and the TUM Lean Six Sigma Yellow Belt certification.
The TUM LSSYB is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.

What is the passing grade for the course?
An overall average for all homework sets and peer-review assignments of 70% is required to pass the course.

Do I need to achieve 70% on each homework?
No, you need an average grade for all homework sets and peer-review assignments of 70%. This means you can do poorly or miss a homework set as long as you do well enough on other homework sets to achieve 70% overall.

How do I receive the edX Professional Certificate for this program?
The Professional Certificate will be available from your dashboard after the end of the course.

How do I receive the TUM Lean SixSigma Yellow Belt Certification for this program?
If you have earned the Verified Certificate for all three courses in the program, then you will receive the edX Professional Certificate and the TUM Lean Six Sigma Yellow Belt. These will be sent to you by email.
— For those finishing all courses by October 31, you will receive your certificate before November 31.
— For those finishing all courses by February 28, you will receive your certificate before March 31.
— For those finishing all courses by June 30, you will receive your certificate before July 31.

When you have passed all three courses with a verified certificate and received the edX Professional Certificate, you can also include in your CV that you have achieved the TUM Lean Six Sigma Yellow Belt, TUMLSSYB.

Meet Your Instructors:

Martin Grunow

Martin Grunow is a professor of production and supply chain management at Technische Universität München and an adjunct professor at Technical University Denmark, where he previously held a professorship in Operations Management. Earlier, he worked at Technical University Berlin and in the R&D department of Degussa, a multinational company producing special chemicals. His research interests are in production and logistics management with a focus on the process, electronics and automotive industries. He has coauthored more than 100 publications amongst others in International Journal of Production Economics, International Journal of Production Research, European Journal of Operational Research, CIRP Annals, Flexible Services and Manufacturing Journal, and OR Spectrum. For the latter two journals, he also acts as an editor. Martin Grunow has been on the program committee and track organizer of more than 30 international conferences and is an associate member of The International Academy of Production Engineering.

Holly Ott

Holly Ott is a professor of Production Management and IT Systems at the University of Rosenheim Applied Sciences and Management, a senior lecturer at the Technical University of Munich and an adjunct professor at Singapore Management University (Singapore), IE Business School (Madrid) and Syracuse University (New York). She holds a Ph.D. in electrical engineering from the University of Virginia and has worked for twelve years in quality and supply chain management in the USA, Europe and Asia for Siemens, Motorola, IBM and Infineon. Her work has been in the areas of device simulation, electrical and reliability testing, and quality and supply chain management. She holds a International Association for Six Sigma Certification (IASSC) Certified Lean Six Sigma Green Belt (ICGB) and is a member of the American Society for Quality (asq.org). Holly is the program chair for the Singapore Semiconductor Industry Association's Supply Chain Forum and academic director of the TUM Case Centre.

Learner Testimonials

“One of the best MOOC on Lean production that you can find online. Whether you are a novice or an expert, this course gives you the concise content that is required to understand lean. The course is well structured, perfectly presented with case studies, examples, math problems, discussion forum, guest lectures given by prominent faculty members from reputed universities around the world. The best things about the course are the faculty members & the participants, who bring in rich diverse experience either educationally or professionally or both. I would recommend this course as well as other courses that are part of TUM’s Professional Certificate Program to those who want to learn or brush up their skills in six sigma and lean. Thank You Mr.Martin, Mrs. Holly, Mr. Reiner, Mr.Zubair and the entire support team. Take a bow!”

-- Murali Dhepalli

“At the beginning I took this course just to show current or future employers my experience in this topic and didn’t expect anything so new for me as it’s my filed of work as a manager in continuous improvement. But as the course commenced, I have learnt more and more details that I could already implement in day to day work. I really enjoyed this course and it’s sure that i will enroll in the additional ones in this TUMx professional program. Prof. Holly Ott and her team managed to develop a great MOOC that is fun and horizon expanding. Prof. Ott is a frequent follower (and responder) of forum discussions which encourages everyone to exchange experiences. Not to forget the emails you get during the course with great pointers to real world problems. All in all, I can recommend this MOOC a lot to anyone who is even remote interested in lean and production management.”

-- Christian Kahl

“This course is the last one in Six Sigma – Lean production offered by TUM on EdX. First thing : while it’s been said that this course can be taken as stand alone, I really think it is best to take first the 2 other courses on Six Sigma, at least if you are a novice in quality management. The course itself is really great. It covers a lot of subject and it is a good help for any manager having to deal with productivity issues, not only in manufacturing but also in service industry. The instructors are very good, they know their subject and can easily pass their knowledge to the student through the lectures. One of the best thing for me was to see the theory applied to real life examples (you’ll see that Professor Holly is really into skateboarding!). Bottom line, I recommend this course!”

-- Etienne Pavajot

“Dear TUM, I am grateful to be one of you MOOC’S student. Really, it helps me a lot specially when I took my ASQ CSSYB and CQIA. It’s almost a year now since I started this professional certificate program and I am happy to inform you that I completed all of 3 verified certificates. Although, I am working as Nurse but I know that some of those concept’s are applicable or can be applied in healthcare setting. Thank Professor Holy and Other’s TUM faculty for your great effort that boosted my passion in Quality field. Regards, Manuel”

-- Manuel

“One of the best MOOC on Lean production that you can find online. Whether you are a novice or an expert, this course gives you the concise content that is required to understand lean. The course is well structured, perfectly presented with case studies, examples, math problems, discussion forum, guest lectures given by prominent faculty members from reputed universities around the world. The best things about the course are the faculty members & the participants, who bring in rich diverse experience either educationally or professionally or both. I would recommend this course as well as other courses that are part of TUM’s Professional Certificate Program to those who want to learn or brush up their skills in six sigma and lean. Thank You Mr.Martin, Mrs. Holly, Mr. Reiner, Mr.Zubair and the entire support team. Take a bow!”

-- Murali Dhepalli

“At the beginning I took this course just to show current or future employers my experience in this topic and didn’t expect anything so new for me as it’s my filed of work as a manager in continuous improvement. But as the course commenced, I have learnt more and more details that I could already implement in day to day work. I really enjoyed this course and it’s sure that i will enroll in the additional ones in this TUMx professional program. Prof. Holly Ott and her team managed to develop a great MOOC that is fun and horizon expanding. Prof. Ott is a frequent follower (and responder) of forum discussions which encourages everyone to exchange experiences. Not to forget the emails you get during the course with great pointers to real world problems. All in all, I can recommend this MOOC a lot to anyone who is even remote interested in lean and production management.”

-- Christian Kahl

“This course is the last one in Six Sigma – Lean production offered by TUM on EdX. First thing : while it’s been said that this course can be taken as stand alone, I really think it is best to take first the 2 other courses on Six Sigma, at least if you are a novice in quality management. The course itself is really great. It covers a lot of subject and it is a good help for any manager having to deal with productivity issues, not only in manufacturing but also in service industry. The instructors are very good, they know their subject and can easily pass their knowledge to the student through the lectures. One of the best thing for me was to see the theory applied to real life examples (you’ll see that Professor Holly is really into skateboarding!). Bottom line, I recommend this course!”

-- Etienne Pavajot

“Dear TUM, I am grateful to be one of you MOOC’S student. Really, it helps me a lot specially when I took my ASQ CSSYB and CQIA. It’s almost a year now since I started this professional certificate program and I am happy to inform you that I completed all of 3 verified certificates. Although, I am working as Nurse but I know that some of those concept’s are applicable or can be applied in healthcare setting. Thank Professor Holy and Other’s TUM faculty for your great effort that boosted my passion in Quality field. Regards, Manuel”

-- Manuel

About This Course:

Learn how to statistically analyze data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.

You will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.

You will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and the 5 Whys to reduce risk and manage process deviations.

To complement the lectures, learners are provided with interactive exercises, which allow learners to see the statistics “in action.” Learners then master statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.

Upon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.

What You’ll Learn:

To identify process problems and perform a root cause analysis using cause and effect diagrams and regression analysis.
To analyze data using inferential statistical techniques, including confidence intervals and hypothesis testing.
To test and quantitatively assess the impact of different improvement options using the design of an experiment.
To test for the significance of effects using an Analysis of Variance.
To implement control mechanisms for long-term monitoring using control charts for both quantitative and qualitative measurements.
To apply the Six Sigma methodology for the Analyze, Improve and Control phases in your work or research.

Professional Certificate in Lean Six Sigma Yellow Belt: Quantitative Tools for Quality and Productivity

Earn a TUM Lean Six Sigma Yellow Belt certificate

Six Sigma: Define and Measure

Six Sigma: Analyze, Improve, Control

Lean Production

3 Courses 6 Months 3 - 4 Hours per week

Complete Program $264.03

Program Details

Frequently Asked Questions:

Who offers this program?
The Professional Certificate Program Six Sigma and Lean: Quantitative Tools for Quality and Productivity is offered by edX and the Technical University of Munich (TUM).

How many courses are in the program?
There are three courses in this program:
Course1 QPLS1x – Six Sigma: Define and Measure
Course2 QPLS2x – Six Sigma: Analyze, Improve, Control
Course3 QPLS3x – Lean Production

Do I need to take the courses in order?
The courses should be completed sequen tially, but it is not required that they are completed in any particular order. However, as Course2 QPLS2x – Six Sigma: Analyze, Improve, Control builds on the material in Course1 QPLS1x – Six Sigma: Define and Measure, we strongly recommend that you take QPLS1x before you take QPLS2x.

Which certification do I earn for this course?
You will earn the Verified Certificate forCourse2 QPLS2x – Six Sigma: Analyze, Improve, Control.

What certificates do I earn with this program?
Upon successful completion of this program, learners will earn both the edX Professional Certificate for the program and the TUM Lean Six Sigma Yellow Belt certification. In order to achieve the TUM Lean Six Sigma Yellow Belt Certification it is mandatory to complete all 3 courses in the program and achieve a Verified Certificate in each. Then, automatically, you will earn the edX Professional Certificate and the TUM Lean Six Sigma Yellow Belt certification.
The TUM LSSYB is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.

What is the passing grade for the course?
An overall average for all homework sets and peer-review assignments of 70% is required to pass the course.

Do I need to achieve 70% on each homework?
No, you need an average grade for all homework sets and peer-review assignments of 70%. This means you can do poorly or miss a homework set as long as you do well enough on other homework sets to achieve 70% overall.

How do I receive the edX Professional Certificate for this program?
The Professional Certificate will be available from your dashboard after the end of the course.

How do I receive the TUM Lean SixSigma Yellow Belt Certification for this program?
If you have earned the Verified Certificate for all three courses in the program, then you will receive the edX Professional Certificate and the TUM Lean Six Sigma Yellow Belt. These will be sent to you by email.
— For those finishing all courses by October 31, you will receive your certificate before November 31.
— For those finishing all courses by February 28, you will receive your certificate before March 31.
— For those finishing all courses by June 30, you will receive your certificate before July 31.

When you have passed all three courses with a verified certificate and received the edX Professional Certificate, you can also include in your CV that you have achieved the TUM Lean Six Sigma Yellow Belt, TUMLSSYB .

Meet Your Instructors:

Martin Grunow

Martin Grunow is a professor of production and supply chain management at Technische Universität München and an adjunct professor at Technical University Denmark, where he previously held a professorship in Operations Management. Earlier, he worked at Technical University Berlin and in the R&D department of Degussa, a multinational company producing special chemicals. His research interests are in production and logistics management with a focus on the process, electronics and automotive industries. He has coauthored more than 100 publications amongst others in International Journal of Production Economics, International Journal of Production Research, European Journal of Operational Research, CIRP Annals, Flexible Services and Manufacturing Journal, and OR Spectrum. For the latter two journals, he also acts as an editor. Martin Grunow has been on the program committee and track organizer of more than 30 international conferences and is an associate member of The International Academy of Production Engineering.

Holly Ott

Holly Ott is a professor of Production Management and IT Systems at the University of Rosenheim Applied Sciences and Management, a senior lecturer at the Technical University of Munich and an adjunct professor at Singapore Management University (Singapore), IE Business School (Madrid) and Syracuse University (New York). She holds a Ph.D. in electrical engineering from the University of Virginia and has worked for twelve years in quality and supply chain management in the USA, Europe and Asia for Siemens, Motorola, IBM and Infineon. Her work has been in the areas of device simulation, electrical and reliability testing, and quality and supply chain management. She holds a International Association for Six Sigma Certification (IASSC) Certified Lean Six Sigma Green Belt (ICGB) and is a member of the American Society for Quality (asq.org). Holly is the program chair for the Singapore Semiconductor Industry Association's Supply Chain Forum and academic director of the TUM Case Centre.

Learner Testimonials

“I did not have any prior knowledge of Quality Management but have always known the value, importance, and job demand related to this topic. I am very glad that i enrolled for this course, although i have to put little extra efforts and time to understand the material but i am very thankful to Dr. Holly Ott for her special teaching skills to explain every topic in a simple way and with practical examples. I also appreciate the guest speakers and their expertise in the subject. I plan to complete all the three courses of this certificate program as soon as i can. I am into my very first course and within couple of weeks i was motivated to look into CSSGB (Certified Six Sigma Green Belt), as I know CSSGB is going to be very useful for professional advancement. I believe if i understand these three Quality Management courses my foundation to study for CSSGB with be solid. My Humble Gratitude to the entire MOOC team and EdX team and many more who are behind the scene to provide us best education from the top most educational institutes.”

--Asha Grover

“This is my second time around using statistics. In Define and Measure I was able to learn how to apply statistics to define problems in in factories and industries. And the ability to measure the results to start an improvement process. I love the instructors who lectured the course. They all have such a breadth of knowledge. My goal after completing this series is to get certified through ASQ to get my Green Belt. I feel confident that this series will lead to that goal.”

--Previous student

“If you are new to Six Sigma like me, this course will greatly expand your skills and knowledge. The course includes many examples, practical exercises, real life case studies, and guest lecturers. These help to reinforce concepts, apply the math, and master the topics.”

--David Rozene

“One of the best online course for 6 sigma. This course has helped me professionally as well as personally, whether taking a decision or solving a problem raised. Very well-articulated, elaborately explained with proper usage of tools and most importantly usage of formulae & examples for numerical problem solving. Discussion forum to share and learn from your peers and getting your doubts clearly by the staff, who promptly reply to every query posted. Thank you everyone who structured this course as is and my best wishes for your future endeavors.”

--Murali Dhepalli

“I did not have any prior knowledge of Quality Management but have always known the value, importance, and job demand related to this topic. I am very glad that i enrolled for this course, although i have to put little extra efforts and time to understand the material but i am very thankful to Dr. Holly Ott for her special teaching skills to explain every topic in a simple way and with practical examples. I also appreciate the guest speakers and their expertise in the subject. I plan to complete all the three courses of this certificate program as soon as i can. I am into my very first course and within couple of weeks i was motivated to look into CSSGB (Certified Six Sigma Green Belt), as I know CSSGB is going to be very useful for professional advancement. I believe if i understand these three Quality Management courses my foundation to study for CSSGB with be solid. My Humble Gratitude to the entire MOOC team and EdX team and many more who are behind the scene to provide us best education from the top most educational institutes.”

--Asha Grover

“This is my second time around using statistics. In Define and Measure I was able to learn how to apply statistics to define problems in in factories and industries. And the ability to measure the results to start an improvement process. I love the instructors who lectured the course. They all have such a breadth of knowledge. My goal after completing this series is to get certified through ASQ to get my Green Belt. I feel confident that this series will lead to that goal.”

--Previous student

“If you are new to Six Sigma like me, this course will greatly expand your skills and knowledge. The course includes many examples, practical exercises, real life case studies, and guest lecturers. These help to reinforce concepts, apply the math, and master the topics.”

--David Rozene

“One of the best online course for 6 sigma. This course has helped me professionally as well as personally, whether taking a decision or solving a problem raised. Very well-articulated, elaborately explained with proper usage of tools and most importantly usage of formulae & examples for numerical problem solving. Discussion forum to share and learn from your peers and getting your doubts clearly by the staff, who promptly reply to every query posted. Thank you everyone who structured this course as is and my best wishes for your future endeavors.”

--Murali Dhepalli