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Peter Baumgartner
and
Akshay Sivadas
By the end of this course, learners are empowered to implement data-driven process improvement objectives at their organization. The course covers: the business case for IoT (Internet of Things), the strategic importance of aligning operations and performance goals, best practices for collecting data, and facilitating a process mapping activity to visualize and analyze a process’s flow of materials and information. Learners are prepared to focus efforts around business needs, evaluate what the organization should measure, discern between different types of IoT data and collect key performance indicators (KPIs) using IoT technology. Learners have the opportunity to implement process improvement objectives in a mock scenario and consider how the knowledge can be transferred to their own organizational contexts.
Read more
By the end of this course, learners are empowered to implement data-driven process improvement objectives at their organization. The course covers: the business case for IoT (Internet of Things), the strategic importance of aligning operations and performance goals, best practices for collecting data, and facilitating a process mapping activity to visualize and analyze a process’s flow of materials and information. Learners are prepared to focus efforts around business needs, evaluate what the organization should measure, discern between different types of IoT data and collect key performance indicators (KPIs) using IoT technology. Learners have the opportunity to implement process improvement objectives in a mock scenario and consider how the knowledge can be transferred to their own organizational contexts.
Read more
By the end of this course, learners are empowered to implement data-driven process improvement objectives at their organization. The course covers: the business case for IoT (Internet of Things), the strategic importance of aligning operations and performance goals, best practices for collecting data, and facilitating a process mapping activity to visualize and analyze a process’s flow of materials and information. Learners are prepared to focus efforts around business needs, evaluate what the organization should measure, discern between different types of IoT data and collect key performance indicators (KPIs) using IoT technology. Learners have the opportunity to implement process improvement objectives in a mock scenario and consider how the knowledge can be transferred to their own organizational contexts.
Material includes online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the first course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11.
Register for this course and see more details by visiting:
OpenCourser.com/course/hf297q/data
What's inside
Syllabus
Operations and Performance Goals
This module covers the business case for establishing a data strategy, including why it is important, from a strategic level, to align your operations and performance goals before you undertake implementation.
Read more
Syllabus
Operations and Performance Goals
This module covers the business case for establishing a data strategy, including why it is important, from a strategic level, to align your operations and performance goals before you undertake implementation.
Read more
Data Collection
This module covers best practices for collecting data.
Process Mapping
This module will prepare you to develop current state process maps, prioritize data gaps, and formulate future state process maps.
Project: Data-Driven Process Improvement
This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a solid foundation in aligning operations and performance goals
Provides knowledge and skills to collect reliable data for process improvement initiatives
Emphasizes practical application through mock scenarios, ensuring transferability of knowledge to learners' own organizational contexts
Facilitates a data-driven mindset, empowering learners to derive insights for optimizing organizational performance
Taught by industry experts, Akshay Sivadas and Peter Baumgartner, known for their contributions to data-driven process improvement
Part of the Data-Driven Decision Making (DDDM) specialization, offering a comprehensive pathway for developing data-driven decision-making skills
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Save Data-Driven Process Improvement to your list so you can find it easily later:
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Reviews summary
Data-driven process improvement: positive course
Learners say this course on data-driven process improvement is well-structured and provides useful instruments. Some of its most notable features include an instructor peer assignment where students receive feedback. However, it should be noted that this feedback may sometimes experience unexpected delays.
The course is well-organized and easy to follow.
"It is a very well structured course."
"Course is very good but if the resource material can be converted to a video it will add more value."
"Well structured, easy to follow and compatible with ones' tight schedule."
Learners highly recommend this course's concepts.
"It's an insightful course on fundamental concepts of processes to collect meaningful data to pursue efficiently certain objectives."
"The course provides some useful instruments and very good guidance."
"The best part about the course is that in addition to the theory, there is a peer reviewed assignment that really helps you think about and put into practice the theory and then get feedback from likeminded people."
Peer review feedback may take longer than expected to receive.
"There are some delay with peer review, other than that everything about the course was great."
"All courses in this specialization are great. However, final activities are vaguely described and it's very difficult not only for the student but also for the graders. This makes very difficult to get a passing grade in the final projects even if students use correctly concepts in the course."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data-Driven Process Improvement with these
activities:
Attend an IoT Industry Meetup
Show steps
Connect with professionals in the IoT field to learn about current trends and best practices.
Browse courses on
Networking
Show steps
-
Research industry meetups in your area.
-
Attend a meetup and introduce yourself to others.
-
Participate in discussions and ask questions.
Review and Organize Course Content
Show steps
Regularly reviewing and organizing your notes and materials will improve your retention and understanding of the course concepts.
Show steps
-
Review lecture notes, readings, and assignments.
-
Organize materials into a logical structure using folders or note-taking software.
Review Basic Statistics
Show steps
Review basic statistical concepts such as mean, median, mode, and standard deviation to strengthen your foundation for understanding data analysis and interpretation.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on basic statistics concepts.
-
Solve practice problems involving calculating measures of central tendency and dispersion.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Volunteer at a Local Tech Event
Show steps
Get involved in the IoT community and apply your skills to help others.
Browse courses on
Community Involvement
Show steps
-
Find a local tech event or organization that needs volunteers.
-
Offer your assistance with tasks related to IoT or data analysis.
Review Data Collection Best Practices
Show steps
You will expand your knowledge of data collection best practices, which will be crucial to your success.
Browse courses on
Data Collection
Show steps
-
Review the course syllabus.
-
Read online resources about data collection.
-
Discuss data collection techniques with a classmate or colleague.
IoT Resource Compilation
Show steps
Keep your knowledge organized by creating a repository of useful resources on IoT.
Browse courses on
Knowledge Base
Show steps
-
Create a document or spreadsheet to store your resources.
-
Include links to articles, videos, websites, and other materials.
-
Organize your resources by topic or category.
Attend a Data Analytics Workshop
Show steps
Attending a workshop will provide you with hands-on experience and insights from industry experts.
Show steps
-
Research and identify relevant data analytics workshops.
-
Attend the workshop and actively participate in discussions and exercises.
Practice Process Mapping
Show steps
This will help you develop the ability to identify and analyze processes, which is essential for improving operations and performance.
Browse courses on
Process Mapping
Show steps
-
Use a whiteboard or online tool to create a process map.
-
Identify the steps and activities involved in a process.
-
Analyze the process for inefficiencies and bottlenecks.
-
Suggest improvements to the process.
Analyze Data Visualization Techniques
Show steps
Practicing data visualization techniques will enhance your ability to interpret and communicate data effectively.
Browse courses on
Data Visualization
Show steps
-
Identify and describe different types of data visualizations, such as bar charts, line graphs, and scatter plots.
-
Create visualizations using real-world data sets.
-
Analyze and interpret the results of your visualizations.
Build a Data-Driven Dashboard
Show steps
Building a data-driven dashboard will provide you with practical experience in applying the concepts learned in the course.
Browse courses on
Data Visualization
Show steps
-
Identify a specific business problem that can be addressed with a data-driven dashboard.
-
Design and develop the dashboard using appropriate visualization techniques.
-
Deploy the dashboard and monitor its effectiveness.
Develop a Data-Driven Process Improvement Plan
Show steps
Creating a data-driven process improvement plan will allow you to apply the concepts learned in the course to a real-world business scenario.
Browse courses on
Process Improvement
Show steps
-
Identify a specific process within your organization that needs improvement.
-
Collect and analyze relevant data to understand the current state of the process.
-
Develop a plan to improve the process using data-driven insights.
-
Implement the plan and monitor its effectiveness.
IoT Data Process Improvement Project
Show steps
This comprehensive project will put your skills to the test in analyzing IoT data and using it to drive real-world improvements.
Show steps
-
Select a process to improve.
-
Collect data on the process.
-
Analyze the data to identify areas for improvement.
-
Implement the improvements and track their effectiveness.
-
Refine the improvements based on the results.
Mentor a Junior Data Analyst
Show steps
Mentoring others will reinforce your understanding of the concepts and help you develop your communication and leadership skills.
Show steps
-
Identify a junior data analyst who could benefit from your guidance.
-
Provide regular support and guidance on data analysis techniques and career development.
Attend an IoT Industry Meetup
Show steps
Connect with professionals in the IoT field to learn about current trends and best practices.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research industry meetups in your area.
- Attend a meetup and introduce yourself to others.
- Participate in discussions and ask questions.
Review and Organize Course Content
Show steps
Regularly reviewing and organizing your notes and materials will improve your retention and understanding of the course concepts.
Show steps
Show steps
Show steps
- Review lecture notes, readings, and assignments.
- Organize materials into a logical structure using folders or note-taking software.
Review Basic Statistics
Show steps
Review basic statistical concepts such as mean, median, mode, and standard deviation to strengthen your foundation for understanding data analysis and interpretation.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on basic statistics concepts.
- Solve practice problems involving calculating measures of central tendency and dispersion.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Volunteer at a Local Tech Event
Show steps
Get involved in the IoT community and apply your skills to help others.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Find a local tech event or organization that needs volunteers.
- Offer your assistance with tasks related to IoT or data analysis.
Review Data Collection Best Practices
Show steps
You will expand your knowledge of data collection best practices, which will be crucial to your success.
Browse courses on
Data Collection
Show steps
Show steps
Show steps
- Review the course syllabus.
- Read online resources about data collection.
- Discuss data collection techniques with a classmate or colleague.
IoT Resource Compilation
Show steps
Keep your knowledge organized by creating a repository of useful resources on IoT.
Browse courses on
Knowledge Base
Show steps
Show steps
Show steps
- Create a document or spreadsheet to store your resources.
- Include links to articles, videos, websites, and other materials.
- Organize your resources by topic or category.
Attend a Data Analytics Workshop
Show steps
Attending a workshop will provide you with hands-on experience and insights from industry experts.
Show steps
Show steps
Show steps
- Research and identify relevant data analytics workshops.
- Attend the workshop and actively participate in discussions and exercises.
Practice Process Mapping
Show steps
This will help you develop the ability to identify and analyze processes, which is essential for improving operations and performance.
Browse courses on
Process Mapping
Show steps
Show steps
Show steps
- Use a whiteboard or online tool to create a process map.
- Identify the steps and activities involved in a process.
- Analyze the process for inefficiencies and bottlenecks.
- Suggest improvements to the process.
Analyze Data Visualization Techniques
Show steps
Practicing data visualization techniques will enhance your ability to interpret and communicate data effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify and describe different types of data visualizations, such as bar charts, line graphs, and scatter plots.
- Create visualizations using real-world data sets.
- Analyze and interpret the results of your visualizations.
Build a Data-Driven Dashboard
Show steps
Building a data-driven dashboard will provide you with practical experience in applying the concepts learned in the course.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a specific business problem that can be addressed with a data-driven dashboard.
- Design and develop the dashboard using appropriate visualization techniques.
- Deploy the dashboard and monitor its effectiveness.
Develop a Data-Driven Process Improvement Plan
Show steps
Creating a data-driven process improvement plan will allow you to apply the concepts learned in the course to a real-world business scenario.
Browse courses on
Process Improvement
Show steps
Show steps
Show steps
- Identify a specific process within your organization that needs improvement.
- Collect and analyze relevant data to understand the current state of the process.
- Develop a plan to improve the process using data-driven insights.
- Implement the plan and monitor its effectiveness.
IoT Data Process Improvement Project
Show steps
This comprehensive project will put your skills to the test in analyzing IoT data and using it to drive real-world improvements.
Show steps
Show steps
Show steps
- Select a process to improve.
- Collect data on the process.
- Analyze the data to identify areas for improvement.
- Implement the improvements and track their effectiveness.
- Refine the improvements based on the results.
Mentor a Junior Data Analyst
Show steps
Mentoring others will reinforce your understanding of the concepts and help you develop your communication and leadership skills.
Show steps
Show steps
Show steps
- Identify a junior data analyst who could benefit from your guidance.
- Provide regular support and guidance on data analysis techniques and career development.
Career center
Learners who complete Data-Driven Process Improvement will develop knowledge and skills
that may be useful to these careers:
Lean Six Sigma Specialist
Lean Six Sigma Specialist
A Lean Six Sigma Specialist uses Lean and Six Sigma methodologies to improve processes and reduce waste. This course would provide a Lean Six Sigma Specialist with the skills to implement data-driven process improvement, allowing them to better identify and reduce inefficiencies.
Continuous Improvement Manager
Continuous Improvement Manager
A Continuous Improvement Manager leads and manages continuous improvement initiatives within an organization, working to enhance processes and increase efficiency. This course would provide a Continuous Improvement Manager with the skills to implement data-driven process improvement, leading to sustained performance improvements.
Industrial Engineer
Industrial Engineer
An Industrial Engineer designs and optimizes integrated systems of people, materials, and equipment. This course would provide an Industrial Engineer with the skills to implement data-driven process improvement, leading to increased productivity and efficiency in manufacturing and other industrial settings.
Operational Excellence Manager
Operational Excellence Manager
An Operational Excellence Manager develops and implements strategies to improve the operational performance of an organization. This course may be helpful for an Operational Excellence Manager who wants to gain a deeper understanding of data-driven process improvement and its impact on overall business performance.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets large amounts of data, communicating insights and driving business decisions. This course may be helpful for a Data Analyst who wants to specialize in data-driven process improvement, allowing them to better optimize existing processes and identify areas for improvement.
Performance Improvement Consultant
Performance Improvement Consultant
A Performance Improvement Consultant helps organizations improve their performance and achieve their goals. This course may be helpful for a Performance Improvement Consultant who wants to gain a deeper understanding of data-driven process improvement and its application in various industries.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods and algorithms to extract insights from data. This course may be useful for a Data Scientist who wants to specialize in data-driven process improvement, allowing them to develop advanced analytical models and solutions.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course may be helpful for a Project Manager who wants to learn about data-driven process improvement to enhance project planning, execution, and evaluation.
Business Analyst
Business Analyst
A Business Analyst analyzes an organization's business processes and systems, helping to improve efficiency and productivity. This course may be useful for a Business Analyst who wants to gain a deeper understanding of data-driven process improvement and its impact on business outcomes.
Process Engineer
Process Engineer
A Process Engineer designs and improves processes within organizations, especially in manufacturing, pharmaceuticals, food processing, or other scientific industries. This course would provide a Process Engineer with the skills to implement data-driven process improvement initiatives, leading to increased efficiency and productivity.
Quality Control Manager
Quality Control Manager
A Quality Control Manager develops and implements quality control systems within an organization. This course may be useful for a Quality Control Manager who wants to gain a deeper understanding of data-driven process improvement and its impact on product or service quality.
Manufacturing Engineer
Manufacturing Engineer
A Manufacturing Engineer designs, develops, and implements manufacturing processes. This course may be useful for a Manufacturing Engineer who wants to optimize manufacturing processes using data-driven process improvement techniques, leading to increased production efficiency and reduced costs.
Quality Assurance Manager
Quality Assurance Manager
A Quality Assurance Manager oversees the development and implementation of quality assurance systems and processes within an organization. This course may be helpful for a Quality Assurance Manager who wants to learn about data-driven process improvement to enhance product or service quality and meet regulatory requirements.
Process Safety Engineer
Process Safety Engineer
A Process Safety Engineer identifies and mitigates hazards associated with chemical processes. This course may be helpful for a Process Safety Engineer who wants to use data-driven process improvement to enhance safety and reduce risks within their processes.
Operations Manager
Operations Manager
An Operations Manager typically leads the Operations department, ensuring the smooth running of the business, often in a manufacturing or production setting. This course may be helpful for an Operations Manager who wants to learn about data-driven process improvement to better optimize equipment and processes.
For more career information including salaries, visit:
OpenCourser.com/course/hf297q/data
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data-Driven Process Improvement.
Save
Provides a comprehensive overview of predictive analytics, including how to use data to predict future events. It covers the basics of predictive analytics, as well as more advanced techniques for building predictive models.
Provides a comprehensive overview of machine learning, including the basics of machine learning, as well as more advanced techniques for building machine learning models. It good resource for learners who are new to machine learning.
Save
Provides a comprehensive overview of deep learning, including the basics of deep learning, as well as more advanced techniques for building deep learning models. It good resource for learners who are new to deep learning.
Provides a comprehensive overview of cloud computing, including the basics of cloud computing, as well as more advanced techniques for working with cloud computing.
Provides a comprehensive overview of data science for business, including the basics of data science, as well as more advanced techniques for working with data.
Save
Provides a comprehensive overview of data mining, including the basics of data mining, as well as more advanced techniques for working with data.
Provides a comprehensive overview of reinforcement learning, including the basics of reinforcement learning, as well as more advanced techniques for building reinforcement learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hf297q/data
Share
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Introduction to Internet of Things
Level
Introductory
Via
Coursera
Institutions
The State University of New York
University at Buffalo
Instructors
Peter Baumgartner
Akshay Sivadas
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
tiếng Việt
This course belongs to a
collection
called
Data-Driven Decision Making (DDDM). It's comprised of three courses:
- Data Analysis and Visualization
- Data-Driven Process Improvement (viewing)
- Applied Analytics and Data for Decision Making
Good to know
Builds a solid foundation in aligning operations and performance goals
Provides knowledge and skills to collect reliable data for process improvement initiatives
Emphasizes practical application through mock scenarios, ensuring transferability of knowledge to learners' own organizational contexts
Facilitates a data-driven mindset, empowering learners to derive insights for optimizing organizational performance
Taught by industry experts, Akshay Sivadas and Peter Baumgartner, known for their contributions to data-driven process improvement
Part of the Data-Driven Decision Making (DDDM) specialization, offering a comprehensive pathway for developing data-driven decision-making skills
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