Data Analytics and Predictive Maintenance Specialist
Course title: 5G O-RAN (Open RAN): Architecture, Procedures And Use Cases
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
Cost
Free
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
Cost
Free
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
Cost
€79.99
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
Cost
Free
Provider contacts
Date
Always available
Location
Online
Website