Data Analytics and Predictive Maintenance Specialist
Course title: Engineering System Design Modeling Techniques and Simulations
Target group: Expert
Level: Foundations
Engineering System Design Modeling Techniques and Simulations
Provider
Alison
Description
This free online course on Modeling Techniques and Simulations, is the final phase of the design process. Modeling and Simulation involves a process of designing a model of a real-world or anticipated system such as a design concept, then conducting experiments with the model for the purposes of understanding the performance of the system under different operating conditions and evaluating alternative management strategies and decision-making processes. In System Engineering, modeling and simulation techniques are applied to two distinct types of system; physical mechanisms whose performance is governed by the laws of physics and process-based systems whose performance are governed by human, group and organizational behaviors.
Next, you will learn about different Modeling Techniques and Decision making in System Designs. You will learn how to use a model to study the behavior and performance of an actual or theoretical system. It will allow you to develop a feel for what variables are important in this phase of system design. This Course is suitable for learners who are considering working in Engineering or System Design. If you’re a professional seeking to learn about Modeling and Simulations, then this clear and simple course is for you. Understanding the different Modeling and Simulation Techniques is of vital importance for professionals working in this industry.
Target
- Learners interested in Engineering or System Design
- Professionals seeking to learn Modeling and Simulation techniques
Sector
- Engineering
- System Design
Area
- Modeling Techniques
- Simulation in System Engineering
- Decision Making in System Design
Learning outcomes
- Identify commonly used methods for modeling.
- Distinguish between the different Relationship Diagrams.
- List the different methods of Data Modeling.
- Define what Behavioural Modeling is.
- State what a Petri Net is and how it is essential to the modeling dynamics.
- Recall what engineering methods can be used at various stages of the system design.
- Discuss what the Bond Graph Method of System Modeling is.
- Indicate how the Bond Graph Model can be used for simulating dynamic behavior.
- Discuss how the decision-making process is essential to system design.
- Discuss the different methods of using graphical tools based on the probability.
- Explain the difference between a simple and complex decision-making process.
- Distinguish when to have a function in a SAC or OAT.
Learning content
- MODULE 1
Modeling Techniques and Simulation
In this module, you will learn about some of the most commonly used methods for system modeling, namely, Data Modeling, Process Modeling and Behavior Modeling. You will also cover physical system testing through simulations - MODULE 2
Bondgraph Modelling & Decision Making
In this module, you will learn the important elements in Bondgraph, how to make decisions and how to identify and quantify the conflicting criteria in decision making. - MODULE 3
Course assessment
Approach/method
Online
Duration
3-4 Hours on Average
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Machine Learning and Advanced AI Techniques
Target group: Senior Employee
Level: Foundations
Machine Learning and Advanced AI Techniques
Provider
Alison
Description
Have you ever wondered how Amazon or Netflix knows exactly which product or show to recommend to you? The answer lies in the adoption of machine learning (ML) in business. In this course, we’ll explore, through real-world examples, the various applications of machine learning and deep learning across different types of businesses.
We’ll begin with an introduction to machine learning, where you’ll explore the different types of machine learning, the uses of labelled and unlabelled datasets, and the key algorithms that power machine learning models. Using examples from healthcare, finance, marketing, and more, you’ll learn the importance of selecting the right algorithm based on the specific problem and dataset characteristics. Next, we’ll introduce you to deep learning, where you’ll explore the architecture of neural networks and learn how neurons, layers, and activation functions work together to create powerful models capable of solving complex tasks. You’ll learn about the specific uses of Convolutional neural networks (CNN) and Recurrent neural networks (RNN), the challenges they offer and how to address them. You’ll explore methods to train models to prevent overfitting and optimising techniques for enhancing model performance. The prerequisite for taking this course is completing our previous course in this series, ‘ Introduction to AI in Business’. Enroll now and embark on a learning journey to unlock the transformative potential of AI.
Target
- Professionals and students interested in AI
- Machine learning, and deep learning
- Individuals seeking to apply AI in business contexts
Sector
- Business
- Healthcare
- Finance
- Marketing
- Technology
Area
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Data Science
Learning outcomes
- Define the foundational concepts and frameworks of Machine learning (ML)
- Illustrate how ML differs from traditional programming methodologies
- Recognise the different types of machine learning and their applications
- Analyse the applications, advantages and limitations of supervised and unsupervised ML
- Identify the key algorithms in machine learning, their functionalities and implementations
- Discuss the application of deep learning for optimising operations across various business domains
- Explain how neural networks are used to build intricate AI systems
- Create powerful models capable of solving complex tasks
- Describe how convolutional neural networks are used for image processing and computer vision applications
- Distinguish the uses of convolutional neural networks (CNN) and recurrent neural networks (RNN)
- Outline methods for training and optimising various types of AI models
Learning content
- Machine Learning and Use of Neural Networks
Module 1
This module introduces different types of machine learning, their key algorithms and their applications across various industries, including healthcare, finance, marketing, and autonomous systems. You’ll learn the key concepts of deep learning, neural network architectures, and the techniques used to train and optimise AI models - Course assessment
Module2
Approach/method
Online
Duration
1.5-3 Hours on Average
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Maintenance engineering – Predictive maintenance
Target group: Mid Level Employee
Level: Extended Know-How
Maintenance engineering – Predictive maintenance
Provider
Alison
Description
Predictive Maintenance Master is the complete guide to learn everything about this great maintenance engineering tool. The course covers all the management implications and the techniques that relate to predictive maintenance. As maintenance engineers you will be able to start implementing a solid predictive maintenance program defining schedules, costs and designing every aspect of It. As maintenance technicians, you will find a lot of useful notions and practical suggestions on how to perform predictive controlla and analysis. The course starts by defining predictive maintenance and its perimeter, and explaining how this maintenance tool can improve production and product quality.
Then the course goes on giving a general overview of the main predictive maintenance techniques that will be deepened in detail further on. Notions will be given about vibration monitoring, Thermography, Tribology, Ultrasonics, electric motor analysis, process parameters analysis, Visual inspections and other techniques.
At this point details about program costs and benefits are given.
The core part of the course deals with the main predictive techinques in details. Vibration analysis it’s explained starting from the definition of vibration and going through the different aspects that relate to the practice of measuring and monitoring vibrations, including the causes that originate them. Dynamics of different mechanical components will be described.
Target
- Maintenance engineers; Plant managers; Reliability engineers; Maintenance supervisors
- Manufacturing; Process industries; Facilities management
- Maintenance engineers; Condition monitoring technicians; Reliability analysts
- Manufacturing; Energy; Automotive; Chemical; Pharmaceuticals; Food & beverage
- Plant managers; Maintenance executives; Financial analysts; Operations managers
- Vibration analysts; Condition monitoring technicians; Reliability engineers
- Data engineers; Reliability analysts; IT/OT integration specialists; Instrument technicians
- Instrument technicians; Condition monitoring staff; Maintenance engineers
- Thermography technicians; Maintenance technicians; Inspection engineers; Reliability engineers
Sector
- Manufacturing
- Process industries
- Facilities management
- Manufacturing
- Energy
- Automotive
- Chemical
- Pharmaceuticals
- Food & beverage
- Manufacturing
- Utilities
- Automotive
- Aerospace
- Power generation
- Process industries
- Facilities management
- Thermography-related applications (Thermography, Tribology, Ultrasonics)
Area
- Predictive maintenance concepts; Management implications; Program definition; Cost/benefit analysis
- Condition monitoring techniques; Non-destructive testing basics; Data interpretation; Basic instrumentation
- Cost modeling; ROI; Budgeting; Business case development; Justification of PM programs
- Vibration definition; Measurement practices; Fault mechanisms; Dynamics of components; Data collection systems; Alarm/threshold setting; Data analysis
- Data acquisition; Database design; Data analysis workflows; Alert/alarm configuration
- Vibration sensors/instruments; Installation practices; Trending methods; Interpretation of trends
- Thermography fundamentals; Wear and lubrication (tribology); Ultrasonic inspection; Data interpretation; Practical applications
Learning outcomes
- Learn what predictive maintenance is and its implications
- Learn Total plant management and Maintenance management through predictive program implementation
- Learn how predictive maintenance can boost product quality up
- Konwledge about the most important and diffused predictive maintenance technique
- Learn how to calculate costs and benefits of a predictive maintenance program
- Master vibration analysis, causes of vibrations, machines dynamics, mode shapes, resonance, critical speed, dynamics for the main type of mechanical components
- Learn how to develop a database to collect vibration data
- Learn the main instruments available to perform vibration analysis
- Learn Thermography analysis
- Learn Tribology techniques
- Learn Ultrasonics analysis
Learning content
- Predictive Maintenance Definition And Implications
- General Overview Of Predictive Maintenance Techniques
- Program Costs And Benefits
- Vibration Monitoring And Analysis
- Database Development For Vibration Monitoring
- Instrument For Vibration Analysis And Analysis Techniques
- Thermography
- Tribology
Approach/method
Online
Duration
4 hours on-demand video
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Machine Learning for All
Target group: Junior (Fresh Employee)
Level: Awareness
Machine Learning for All
Provider
Coursera
Description
Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self-driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionize almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should. The big AI breakthroughs sound like science fiction, but they come down to a simple idea: the use of data to train statistical algorithms. In this course you will learn to understand the basic idea of machine learning, even if you don’t have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognize images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high-level concepts before the technical details, but it is also great if your role is non-technical. You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding. Or you might just be following the news reports about AI and interested in finding out more about the hottest new technology of the moment. Whoever you are, we are looking forward to guiding you through you first machine learning project. NB this course is designed to introduce you to Machine Learning without needing any programming. That means that we don’t cover the programming-based machine learning tools like python and TensorFlow.
Target
- Non-technical professionals (managers, executives) interested in understanding AI/ML
- Beginners without prior math or programming background
- Anyone interested in AI breakthroughs and technology trends
- Individuals considering a technical career in Machine Learning (as a first step)
Sector
- Business and Corporate Management
- Education and Academia
- Technology and Innovation (non-technical roles)
Area
- Artificial Intelligence / Machine Learning
- Data-driven decision making
- AI applications in image recognition and automation
Learning outcomes
- You will understand the basic of how modern machine learning technologies work
- You will be able to explain and predict how data affects the results of machine learning
- You will be able to use a non-programming based platform train a machine learning module using a dataset
- You will be able to form an informed opinion on the benefits and dangers of machine learning to society
Learning content
- Machine learning:
In this topic you will learn about artificial intelligence and machine learning techniques. You will learn about the problems that these techniques address and will have practical experience of training a learning model. - Data Features:
In this topic you will learn about how data representation affects machine learning and how these representations, called features, can make learning easier. - Machine Learning in Practice:
In this topic you will get ready to do your own machine learning project. You will learn how to test a machine learning project to make sure it works as you want it to. You will also think about some of the opportunities and dangers of machine learning technology. - Your Machine Learning Project:
In this final topic you will do your own machine learning project: collecting a dataset, training a model and testing it.
Approach/method
Online
Duration
Approx. 20 hours
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Mastering Data Visualization: Theory and Foundations
Target group: Junior (Fresh Employee)
Level: Foundations
Mastering Data Visualization: Theory and Foundations
Provider
Udemy
Description
Welcome to Mastering Data Visualization! In this course, you’re going to learn about the Theory and Foundations of Data Visualization so that you can create amazing charts that are informative, true to the data, and communicatively effective.
Have you noticed there are more and more charts generated every day? If you turn on the TV, there’s a bar chart telling you the evolution of COVID, if you go on Twitter, boom! a lot of line charts displaying the evolution of the price of gas. In newspapers, lots and lots of infographics telling you about the most recent discovery… The reason for that is that now we have lots of data, and the most natural way to communicate data is in visual form: that is, through Data Visualization. But, have you noticed all of the mistakes in those visualizations? I have to tell you, many of the charts that I see regularly have one problem or another. Maybe their color choices are confusing, they chose the wrong type of chart, or they are displaying data in a distorted way.
Actually, that happens because more and more professional roles now require to present data visually, but there’s few training on how to do it correctly. This course aims to solve this gap. If there’s one thing I can promise you is that, after completing this course, you’ll be looking at charts at a completely different way. You will be able to distinguish good and bad visualizations, and, more importantly, you will be able to tell when a graph is lying and how to correct it.
If you need to analyze, present or communicate data professionally at some point, this course is a must. Actually, even if you don’t need to actually draw plots for a living, this course is hugely useful. After all, we are all consumers of data visualizations, and we need to identify when charts are lying to us.
Target
- Professionals who need to present data visually
- Data analysts and communicators
- Media consumers and journalists
- Anyone interested in understanding and critiquing data visualizations
- Beginners with no prior knowledge of data visualization
Sector
- Data analysis and visualization
- Media and journalism
- Education and training
- Communication and presentation
Area
- Data visualization theory and foundations
- Data communication and interpretation
- Media literacy regarding charts and infographics
- Data quality and ethical visualization practices
Learning outcomes
- Learn to design effective data communication
- Improve your plots up to a professional level
- Learn to choose and design the appropriate plot for your purpose
- Learn to create compelling graphs that do not lie
- Learn to avoid the traps your data can fall into
- Learn to distinguish between good, bad and wrong visualization
- Learn the golden rules on Graphical Excellence, Integrity and Sophistication
- Learn the most common crimes in plotting to be able to avoid them
Learning content
- Introduction
- Graphical Perception
- The Golden Rules of Data Visualization
- Statistical Traps: How not to fall in them
- Plots: Find the correct plot for your data
- Plot Crimes
- What Next?
Approach/method
Online
Duration
5 hours on-demand video
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Data Analysis Using Python
Target group: Junior (Fresh Employee)
Level: Foundations
Data Analysis Using Python
Provider
Coursera
Description
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
Target
- Beginners in data science, students
- professionals new to Python data analysis
Sector
- Data Science
- Analytics, IT
- and Technology
Area
- Data analysis
- Python programming
- Data visualization
- Data manipulation
Learning outcomes
- Apply basic data science techniques using Python
- Understand and apply core concepts like Data Frames and joining data, and use data analysis libraries like pandas, numpy, and matplotlib
- Demonstrate how to load, inspect, and query real-world data, and answer basic questions about that data
- Analyze data further by applying learned skills in data aggregation and summarization, as well as basic data visualization
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Learning content
- Module 1: Loading, Querying, & Filtering Data Using the csv Module
- Module 2: Loading, Querying, Joining & Filtering Data Using pandas
- Module 3: Summarizing & Visualizing Data
Approach/method
Online
Duration
1 week at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Big Data Analysis with Scala and Spark
Target group: Mid Level Employee
Level: Extended Know-How
Big Data Analysis with Scala and Spark
Provider
Coursera
Description
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we’ll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We’ll cover Spark’s programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we’ll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.
Learning Outcomes. By the end of this course you will be able to:
- read data from persistent storage and load it into Apache Spark
- manipulate data with Spark and Scala
- express algorithms for data analysis in a functional style
recognize how to avoid shuffles and recomputation in Spark
Target
- Data engineers
- data scientists
- software engineers
- IT professionals who work with large-scale data processing and analytics.
Sector
- Information Technology
- Software
- Data Analytics
- Cloud/Big Data services
- Technology consulting
Area
- processing using Apache Spark
- functional programming concepts applied to large-scale data
- Spark programming model
- latency and network considerations
- performance optimization to avoid shuffles and recomputation
Learning outcomes
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Learning content
- Module 1
Getting Started + Spark Basics - Module 2
Reduction Operations & Distributed Key-Value Pairs - Module 3
Partitioning and Shuffling - Module 4
Structured data: SQL, Dataframes, and Datasets
Approach/method
Online
Duration
3 weeks at 10 hours a week
Assessment
No
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Sensors and Sensor Circuit Design
Target group: Mid Level Employee
Level: Extended Know-How
Sensors and Sensor Circuit Design
Provider
Coursera
Description
This course can also be taken for academic credit as ECEA 5340, part of CU Boulder’s Master of Science in Electrical Engineering degree.
After taking this course, you will be able to:
- Understand how to specify the proper thermal, flow, or rotary sensor for taking real-time process data.
- Implement thermal sensors into an embedded system in both hardware and software.
- Add the sensor and sensor interface into a microprocessor based development kit.
- Create hardware and firmware to process sensor signals and feed data to a microprocessor for further evaluation.
Study sensor signal noise and apply proper hardware techniques to reduce it to acceptable levels
Target
- Graduate/advanced undergraduate students in Electrical Engineering or related fields pursuing hands-on hardware-software integration
- Aspiring embedded systems engineers who want practical experience with real-time sensor data acquisition and processing
- Professional engineers/students seeking academic credit (ECEA 5340) as part of a Master of Science in Electrical Engineering
Sector
- Electrical Engineering / Embedded Systems
- Industrial automation and process monitoring
- Sensor interface design and hardware-software co-design
- Academic/education sector (credit-bearing university course)
Area
- Sensor selection and specification for real-time data acquisition (thermal, flow, rotary)
- Embedded hardware integration (sensor interfacing, hardware design, PCB/microcontroller)
- Firmware and software development for sensor processing (signal conditioning, data handling, MCU integration)
- Hardware/software co-design for sensor networks and data evaluation
- Sensor signal integrity and noise reduction techniques
- Hands-on projects using PSOC 5LP prototyping kit and related components (Digikey part numbers provided)
- Laboratory hardware setup and instrumentation (breadboarding, oscilloscope usage, basic test equipment)
Learning outcomes
- Use the core features of the Cypress PSOC development kit.
- Choose the right temperature sensor, rotary sensor and amplifier for an application.
- Interface sensors, LCD, and ADC to the PSOC development kit.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Learning content
- Module 1
Thermal Sensors - Module 2
Sensor Development Kit and Prototyping - Module 3
Rotary and Flow Sensors - Module 4
Amplifiers and Sensor Noise - Module 5.
Course Project
Approach/method
Online
Duration
3 weeks at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Data Analysis Using Python
Target group: Junior (Fresh Employee)
Level: Foundations
Data Analysis Using Python
Provider
Coursera
Description
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
Target
- Beginners in data science, students
- professionals new to Python data analysis
Sector
- Data Science
- Analytics, IT
- and Technology
Area
- Data analysis
- Python programming
- Data visualization
- Data manipulation
Learning outcomes
- Apply basic data science techniques using Python
- Understand and apply core concepts like Data Frames and joining data, and use data analysis libraries like pandas, numpy, and matplotlib
- Demonstrate how to load, inspect, and query real-world data, and answer basic questions about that data
- Analyze data further by applying learned skills in data aggregation and summarization, as well as basic data visualization
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
Learning content
- Module 1: Loading, Querying, & Filtering Data Using the csv Module
- Module 2: Loading, Querying, Joining & Filtering Data Using pandas
- Module 3: Summarizing & Visualizing Data
Approach/method
Online
Duration
1 week at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Communicating Data Insights with Tableau
Target group: Junior (Fresh Employee)
Level: Foundations
Communicating Data Insights with Tableau
Provider
Coursera
Description
The Communicating Data Insights with Tableau Course focuses on reporting best practices using Tableau. You will learn how to create interactive dashboards and craft compelling presentations based on your data analysis. This course will also teach you best practices for data storytelling to convey business insights and provide stakeholders with the data to effectively explore and build their own insights.
This course is for anyone who is curious about entry-level roles that demand fundamental Tableau skills, such as business intelligence analyst or data reporting analyst roles. It is recommended (but not required) that you have some experience with Tableau Public, but even if you’re new to Tableau Public, you can still be successful in this program.
By the end of the course, you will be able to:
- Identify data storytelling best principles to convey business insights.
- Build interactive dashboards and stories in Tableau Public.
Utilize data storytelling design principles to craft a compelling presentation so stakeholders can explore and disaggregate data to build their own insights.
Target
- Entry-level professionals and aspiring analysts (e.g., business intelligence analysts, data reporting analysts) with an interest in Tableau skills
Sector
- Information Technology / Business Intelligence
Area
- Data storytelling and Tableau dashboard/reporting
Learning outcomes
- Identify data storytelling best principles to convey business insights.
- Build interactive dashboards and stories in Tableau.
- Utilize data storytelling design principles to craft a compelling presentation so stakeholders can explore data to build their own insights.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Tableau Learning Partner
Learning content
- Module 1
Developing Insights - Module 2
Creating and Designing Dashboards - Module 3
Crafting Stories From Insights - Module 4
Certificate Wrap Up
Approach/method
Online
Duration
2 weeks at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website
Course title: Teamwork Skills: Communicating Effectively in Groups
Target group: Junior (Fresh Employee)
Level: Awareness
Teamwork Skills: Communicating Effectively in Groups
Provider
Coursera
Description
Effective teamwork and group communication are essential for your professional and personal success. In this course you will learn to: make better decisions, be more creative and innovative, manage conflict and work with difficult group members, negotiate for preferred outcomes, improve group communication in virtual environments, develop a better overall understanding of human interaction, and work more effectively as a team. Our goal is to help you understand these important dynamics of group communication and learn how to put them into practice to improve your overall teamwork.
Target
- professionals and students seeking to improve collaboration and communication in teams
Sector
- all sectors that rely on teamwork (business, education, non-profit, healthcare, tech, etc.)
Area
- group dynamics, decision-making, conflict management, negotiation, virtual teamwork, and overall interpersonal communication skills
Learning outcomes
- Recognize how hidden forces of context, systems, institutions, and interactions design affect group interaction
- Develop decision making practices in order to become more creative and innovative
- Learn how to communicate to resolve or diffuse group conflicts
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
- Make better decisions about using technology for group work based on key practical and conceptual consideration
Learning content
- Module 1: Rethinking Communication
- Module 2: Group Development & Decision Making
- Module 3: Conflict, Difference, & Diversity
- Module 4: Group Communication & Technology
Approach/method
Online
Duration
1 week at 10 hours a week
Assessment
Yes
Certification
Yes
Provider contacts
Date
Always available
Location
Online
Website