Professional Certificate in AI & Data Analytics

Foundation Certificate in AI

Unlock the power of AI in this hands-on course. Learn the latest techniques, technologies, and the ethical and societal implications of AI. Join the AI revolution

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Complete Overview of AI and Data Analytics

1. Introduction
2. The Definition of Human and Artificial Intelligence (AI)
3. Intelligent Agents
4. Human logical levels of thinking using Robert Dilt’s Model
5. Ethics and Trustworthy AI and the general definition of Ethics
6. How human-centric ethical purpose respects fundamental rights, principles and values
7. Using Trustworthy AI to deliver Ethical Purpose AI
8. Continual Assessment and Monitoring of trustworthy AI to ensure a Human Centric Ethical Purpose
9. The 3 Fundamental Areas of Sustainability
10. The UN's Sustainable Development Goals (SDGs) expanded
11. How AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
12. Machine Learning as a contribution to the growth of AI
13. How ‘learning from experience’ relates to Machine Learning with Tom Mitchell’s definition
14. Section Conclusion "Ethical and Sustainable Human and Artificial Intelligence"
1. Artificial Intelligence and Robotics Introduction
2. Understanding AI intelligent agents: The four rational agent dependencies
3. Performance measures, environment, actuators and sensors for Agents
4. Four types of agent: reflex, model-based reflex, goal-based and utility-based
5. Relationship of AI agents with Machine Learning (ML)
6. What is a Robot? Exploring Robotic Paradigms
7. Discussing intelligent robots and comparing them to intelligent agents
8. Conclusion to Artificial Intelligence and Robotics
1. Introduction to Applying the benefits of AI - challenges and risks
2. Human-centric ethical AI in a Sustainable Society
3. Benefits of Artificial Intelligence, machine learning and human and machine systems
4. Describe the challenges of Artificial Intelligence, and give general ethical challenges AI raises general examples of the limitations of AI systems compared to human systems
5. Understanding of the risks of AI project
6. A typical AI project team in particular, describe a domain expert, describe what ‘fit-of-purpose’ is, and describe the difference between waterfall and agile projects
7. Identify typical funding sources for AI projects and relate them to the NASA Technology Readiness Levels (TRLs)
1. Introduction
2. How we learn from data – functionality, software and hardware
3. Common open source machine learning functionality, software and hardware
4. Introductory theory of Machine Learning
5. Typical tasks in the preparation of data
6. Typical types of Machine Learning Algorithms
7. The typical methods of visualising data
8. How narrow AI capability is useful in ML and AI agents’ functionality
9. Conclusion
1. Introduction: "The Management, Roles and Responsibilities of humans and machines"
2. How Artificial Intelligence & Machine Learning will drive humans and machines to work together
3. Future directions of humans and machines working together
4. 5 more examples of how AI and humans can work together
5. Top 3 most disruptive examples of AI and Human collaboration
6. How a ‘learning from experience’ Agile approach to projects works
7. The type of team members needed for an Agile project
8. Conclusion
1. Key components of AI
2. AI and The Digital Ecosystem
3. The relationship between AI and Data
4. The role the IT professional plays in supporting AI for the future
5. Human Vs Artificial Intelligence
6. How are digital technologies and AI transforming organisations?
7. The Internet of Things (IoT) as a digital ecosystem
8. Ecosystem mindset
9. How AI can support effective digital ecosystems
10. How AI and Big Data enables organisations to better understand their business
11. Industries that can grow with AI
12. How has AI already transformed certain industries?
13. Intelligence Augmentation through AI
14. Utopian AI Future?
1. Intro to Data Languages
2. SQL - Introduction
3. Examples of common SQL queries
4. SQL aggregation functions
5. SQL Joins
6. Python - Introduction
7. Python - Numpy
8. Python - Pandas
9. Python - Scikit-learn
10. Python - Plotly
11. Code - Python Project
1. Intro to Data Analysis
2. XLOOKUP
3. Pivot Tables
4. Data Model/Power Pivot
5. Data ToolPak - Regression Model
6. Power Query
7. Logistic Regression
8. Analyse - Data Analysis Project
9. Time Series Analysis
10. Classification of data types and the stages of the data lifecycle
11. Utilising Structured and Unstructured Data
12. Gathering customer requirements for data analysis
13. Ensuring good quality data whilst complying with Data Protection regulation
14. The principles of data architecture, data modelling and database design
15. Data Integration
16. The Data Analysis Lifecycle
17. Communicating key information to stakeholders
18. How to classify data, and its use within a business
19. Common sources of structured data
20. Using structured data for data analysis
21. Importance of Domain Knowledge for effective data analysis
22. Analyzing customer requirements
23. Apply the requirements elicitation process
24. Normalisation
25. De-normalization
26. Data Set Quality Testing Requirements
1. Intro to Visualisation Tools - Video
2. Power BI - Introduction - Video
3. Power BI - Dashboard Creation
4. Power BI - Time Series Analysis
5. Power BI - Time Series Analysis
6. Power BI - Clustering
7. Power BI - DAX
8. Design - Create a Dashboard Project
9. Data-driven decision making
10. The process of formatting data to make decisions
11. Engaging stakeholders and getting buy-in
12. Requirements regarding data protection
13. Tools and techniques used to present data
1. What is Big Data?
2. Different types of data structures
3. The need for a Big Data strategy
4. Benefits of using Big Data frameworks
5. Key aspects of Big Data analytics
6. Text analytics
7. Predictive analytics
8. Data mining
9. Machine learning
10. Linear regression
11. Bayesian classifiers
12. Time series analysis
13. Decision trees
14. The importance of Data Analytics
15. Why Machine Learning needs Big Data
16. The challenges of Big Data
1. What is Machine Learning?
2. The basic principles of machine learning
3. Different applications of Machine Learning
4. The use of coding languages and software used in Machine Learning
5. The types of algorithms used in machine learning
6. Typical algorithms used in Machine Learning
7. The key stages within the Machine Learning process
8. Machine Learning is a subset of Artificial Intelligence
9. Ackowledging Tom Mitchell's 1997 book "Machine Learning"
10. Explore different examples and applications of Machine Learning
11. Prediction in a business context using machine learning
12. Object recognition in a business context using machine learning
13. Classification in a business context using machine learning
14. Clustering in a business context using machine learning
15. Recommendations using machine learning in a business context
16. Learning Agents
17. Deep Learning
18. Neural Networks
19. How Machine Learning compliments Knowledge-Based Systems
20. Complimentary AI technologies
21. How Machine Learning works with Data
22. Analyse the problem
23. Data Selection
24. Data Pre-processing
25. Cleaning
26. Integration
27. Transformation
28. Reduction
29. Wrangling
30. Data Visualisation
31. Select a Machine Learning model (algorithm)
32. Train, Test, Repeat
33. Review
34. Peer Review
35. The benefits of learning from multiple algorithms
36. How can we identify the best Machine Learning model?
37. Python for Machine Learning
38. What are the main python libraries for machine learning?
39. Common open source and proprietary software used for Machine Learning
40. Comparing Scikit-Learn with Tensorflow
41. Mathematics in Machine Learning
42. Probability (Bayes Theorem)
43. Descriptive Statistics
44. Inferential Statistics
45. Linear Algebra
46. Regression algorithms
47. Classification algorithms
48. Clustering algorithms
49. Logistic Regression
50. Boosting in machine learning
51. Decision Forests
52. Ensembles
53. Semi-supervised learning
54. Reinforcement learning
55. Using Machine Learning to solve problems in real-life
56. Preparation of data for application of Machine Learning
57. Training a Machine Learning model
58. Testing a Machine Learning model
59. Ealuating the results of a Machine Learning model
1. Smart Products, Robotics and Automation
2. History of the use of robots within technology
3. What is a Robot?
4. What is an intelligent robot?
5. Robots and automation in the workplace
6. The role robots will play within the future
7. Uses and Design of Robots and Smart Products
8. Identifying, creating, and maintaining automation
9. The 5 levels of Autonomous vehicles
10. Nanobots
11. SWARM
12. Augmentation via Smart products
13. Robotic paradigms (Sensing, Planning, Acting)
14. Agent-based modelling
15. How robots and smart products are used in different industries
16. Understanding intelligent robotics & intelligent agents via deep learning
17. How robots “think” and act, making decisions based on the input data and certain “degrees of truth” (fuzzy logic)
18. The next phase in the development of robotic and smart technology
19. Transforming from the Third industrial revolution to Industry 4.0
20. The transition from the 4th Industrial revolution to the 5th exploring space
21. Intelligent RPA
22. Digital twins
23. Market needs driving new innovations in robotics and smart technologies
24. Ethical principles surrounding the creation of smart products and robotics
25. Ethical AI use – what is the impact on humans?
26. What challenges are created when introducing greater levels of autonomy?
27. How a smart product or robot can be supported
28. Types of testing such as performance, reliability, acceptance, and operational testing
29. Fostering an environment of continuous innovation
30. Current limitations in robotic/machine intelligence
31. Examples of where robots, smart products and humans coexist.
32. Possibilities and future innovations that have not yet been designed
33. Implications of a Human/machine interface – (organoid)
34. Implications of Exoskeleton technology
35. Implications of Social care innovation
36. Health care innovation
37. Industrial production innovation
38. Travel innovation
1. What is data? An introduction
2. Types of Data
3. Importance of Statistics
4. Samples and Populations
5. Descriptive & Inferential Statistics and Variables & Parameters
6. Statistical Measures Mean, Median, Mode
7. The key components of AI
8. The relationship between AI and The Digital Ecosystem
9. The relationship between AI and Data
10. The role the IT professional plays in supporting AI for the future
11. The general definition of human intelligence (as per Encyclopaedia Britannica)
12. The general definition of artificial intelligence and how it relates to human intelligence
13. Various subfields are used within AI
14. Digital Ecosystems
15. How are digital technologies and AI transforming organisations?
16. Digital Transformation
17. Digitization
18. AI as personal assistants
19. The Fundamental enabler (human plus machine systems)
20. The Internet of Things
21. The Internet of Things (IoT) as a digital ecosystem
22. Big Data
23. Smart Offices
24. What is an ecosystem mindset?
25. How AI can facilitate a digital ecosystem
26. The benefits of an effective digital ecosystem
27. Aspects of an effective digital ecosystem
28. The use of AI to create a digital ecosystem as a driver or enabler.
29. How embedded AI has become within human society
30. Data infrastructure with specific relevance to Big Data
31. How can we take advantage of Big Data?
32. Key concepts within data architecture
33. Machine Learning and Neural Networks
34. How AI can support the utilisation of Big Data
35. How AI is enabling organisations to better understand their business
36. Approaches used within the testing of AI systems
37. How using a combination of different methods of testing can improve a business
38. Industries that are set to heavily invest in AI
39. How has AI already transformed certain industries? What potential does it have for further digital transformation?
40. Intelligence Augmentation, the role of AI in raising human intelligence
41. Digital singularity
42. Singularity Theory
43. Maintaining your skills and knowledge in current and emerging technologies
44. Logistic Regression
45. The general definition of human and Artificial Intelligence
46. ‘Learning from experience’ and how it relates to Machine Learning (Tom Mitchell’s explicit definition)
47. How ML is a significant contribution to the growth of AI
48. How AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’
49. The benefits of AI and list advantages of machine and human systems
50. The challenges of Artificial Intelligence and general ethical challenges AI raises
51. The risks of Artificial Intelligence
52. Typical funding sources for AI projects
53. Opportunities for AI
54. AI intelligent agents
55. The four rational agent dependencies
56. Machine Learning in various contexts: business, socal media, entertainment, science
57. Narrow AI's use in ML and AI agents’ functionality
58. Types of Machine Learning: Supervised, unsupervised, reinforcement
59. The basic schematic of a neural network
60. How Artificial Intelligence can drive humans and machines to work together
1. Interview Skills
2. LinkedIn and Social Media
3. Build Your CV/Resume
1. Ada Lovelace - algorithm and computer coding
2. Alan Turing - breaking enigma
3. What does it mean to be Turing complete?
4. Advent of machine learning and neural networks
5. Smartphones
6. Features of organisational structures
7. Features of for profit organisations
8. Features of Not for profit organisations
9. Features of Public organisations
10. Features of Charities
11. Features of a Private Limited company
12. Features of a Public Limited company
13. Features of a Sole trader (in the UK)
14. Flat Organisational structure
15. Hierarchical Organisational structure
16. Functional organisational structure
17. Divisional organisational structure
18. Matrix organisational structure
19. Mission, vision, values, and other organisational strategies
20. Mission and Vision in focus
21. Factors that influence organisational culture
22. The value and impact of a diverse workforce
23. Top down culture
24. Bottom up culture
25. What is an Agile organisation
26. Organisational Governance
27. The role of AI in an organisation
28. How AI can improve a customer's entire interaction with a business
29. Humans and AI woking together to deliver optimal results
30. Embracing change as the workforce and AI evolves
31. Understanding external forces which influence your business activities
32. PESTLE
33. Porters 5
34. Using Competitor Analysis
35. Using Stakeholder mapping including Power Interest Matrix
36. Enabling factors for AI
37. Tools and resources required to implement a successful AI solution
38. SWOT Analysis
39. Balanced Scorecard
40. GAP analysis
41. Capability assessment
42. NASA Technology Readiness Levels
43. Methods to understand the actual performance of the organisation, against the established objectives
44. Create and present a business case
45. Methods to propose new ideas and concepts to the decision makers in an organisation
46. The importance of Funding to decision makers in an organisation
47. The importance of Business Case to the decision makers in an organisation
48. Understanding a Business Sponsor within the context of an organisation
49. AI champions within the context of an organisation
50. Timescales
51. Identify components of a creative thinking environment
52. Fail fast
53. Safety culture within an organisation
54. Early adopters
55. Change management
1. Risk and Risk Management
2. The principles of the ISO31000 Risk Management Framework
3. Roles in risk management
4. Introducing Project sponsor, Project owner, Risk manager, Action owner
5. Project sponsor
6. Project owner
7. Risk manager
8. Action owner
9. Risk appetite and Risk tolerance
10. Risk tolerance, Risk averse, Risk tolerant, Risk neutral, Risk seeking
11. Suitable responses to risk; Avoid, accept, reduce, exploit
12. Risks associated with implementing a digital technology based on assessment
13. Risk categories
14. Strategic risk; Compliance risk; Operational risk; Financial risk; Reputational risk
15. Risk assessment techniques for qualitative data
16. How to risk assess qualitative data
17. Probability impact - accurately measuring and documenting risk
18. Cause Risk Effect
19. Risk assessment techniques for quantitative data
20. Decision trees as a risk assessment technique
21. Sensitivity analysis as a risk assessment technique
22. How to document risk within a risk register
23. Risk map
24. Stakeholder risk within the context of Communication, Change, and Cost
25. Establish stakeholder rights and interests

1 Year Full Access
Accredited Exam Included
995 + VAT

Featured Tools and Technology

What direction will your career take after the course?

You’ll get all the hard skills to accelerate your career with an accredited qualification

AI Specialist

Develop and implement AI solutions to improve company operations by working closely with other teams, designing and implementing AI models, and ensuring ethical and responsible use of AI within the organization.
Business Intelligence Analyst

A hybrid between the data and the business analyst. Uses data analysis, modelling and visualisation to help the company to achieve its objectives. They are responsible for reporting and using tools like Excel, SQL and Power BI.
Data Scientist

Builds models for the collection, manipulation and analysis of data, which can be based on elements of machine learning. Their tools are Python, SQL and advanced models such as Regression, Time Series, and Clustering.

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What you will learn

Your DACertify course goes beyond just a certificate in AI and Data Analytics. This platform is designed so you can learn all the hard skills needed to analyse data to make decisions, implement machine learning models and to build a practical data application ready for real-world deployment.

Data Analysis

You will learn to master analytics platforms to study user behaviour, to analyse the effectiveness of marketing campaigns and to identify ways to optimise processes.
Data Visualisation

Using Power BI you will learn how to structure and present complex reports with simplicity and speed. You will be able to create graphs and dashboards that “talk to each other” and allow you to analyse data in depth, answering increasingly specific and complex questions.
Statistics & Machine Learning

You will learn key concepts of machine learning and its relation with data science and artificial intelligence. Then, we will unpack important technical jargon that is critical for understanding the typical data mining workflow for analysing and preparing data, as well as building your first machine learning models and evaluating their performance.

Programming Languages

Database Management with SQL

Information is everywhere. You can put it to use by learning SQL. Used in data science, analytics, and engineering, SQL makes it easy to work with data and make more informed strategy, operations, and business decisions.
Data Analysis Expressions (DAX)

You can unlock the advanced power of reporting in Power BI. These functions will help you get better data insights. DAX or Data Analysis Expressions is a library of functions and operators, using which you can create formulas and expressions, helping you create powerful formulas to ease your data analysis and reporting process.
Unlock the Power of Data with Python

Python is a very powerful programming language, which offers enormous possibilities of analysis. Thanks to functions such as web scraping, for example, you can automatically collect textual data from multiple websites for monitoring or comparison purposes to benefit your company.

Exam material

Completion of your course opens up access to the accredited the Exam

Building a Machine Learning Toolbox

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Management, Roles and Responsibilities of AI

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Artificial Intelligence and Robotics

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Testimonials

“This time last year I was a stay-at-home mum struggling to access even basic data courses. Now, I work with innovative training providers and create my own training & awareness content, and I support both data and governance teams with their projects. I get to tell people I am a consultant for a global firm, which makes me super proud. Thanks to DACertify for all your support, could not have done it without your help!”

Sheryl J (Oct, 2022)

“After 16 years working for the government, I am happy to share I have been offered a new position as an Analyst. 6 short months ago I decided to make the leap with DACertify to re-skill in Data and AI and I have not regretted a minute of it. DACertify stood out from the start: they understand data analysis is all about people, and that the industry needs all kinds. Thank you for landing me my dream role.”

Andreas T (Sep, 2022)

“A year since I finished my last temp contract, I received and signed my first professional perm job in data as a marketing analyst. I couldn’t have done it without the incredible team at DACertify. The training and company vision is unrivaled, and I would suggest anyone looking to transition into data check them out.”

Jonathan P (Sep, 2022)

“This is a GREAT course, hands-on, practical, with relevant examples that pretty much anyone can do.”

Leah J (Jan, 2023)

“Thanks to this course I actually managed to change career paths and to become a BI analyst. I'm currently not even working with Python in my job but mostly using what I learned in the Power BI section! I got all the necessary fundamentals from this course to easily dive into other programming languages as well. The lessons and course itself is a very inspiring and motivating instructor and did an amazing job with structuring the materials in an increasingly challenging order. I can highly recommend this course for anyone who wants to start learning about programming languages in general and writing good code for data analytics purposes.”

Thalia V (Sep, 2022)

“This took my skills to the next level! This is by far the best resource I've found for learning AI and data science. The examples and concepts together make this course exceptional. I am looking forward to a second course with even more advanced examples and projects. It would be much appreciated!”

Maria N (Aug, 2022)

“Fantastic course, with great teachers and lessons. Broke data science and machine learning basics down into bite-sized pieces. Any beginner can easily understand and learn a lot from this course. It's given me a keen interest to learn more. I really hope DACertify teaches more courses in the future!”

Natalie M (Oct, 2022)

“The course is well designed to become a data practitioner with a deep understanding of AI. This course helped to think about real-world things in data science and statistics perspective and also help to analyse real-world problems and covers different aspects such as statistics and probability, Regression, classification, cluster analysis, unsupervised ML and Deep Learning and Neural networks.”

Kamala R (Dec, 2022)

High Quality Training

FAQ

Who can take the Foundation AI course?

Anyone who wants to become a data analyst or AI specialist with an accelerated learning journey can take our course. You can start completely from scratch and just need to be over 18 to sign up. If you’ve never analysed data before, or if you’ve already taken some courses but feel that you haven’t done enough to boost your career in tech, DACertify is the course for you.
Can I follow the course from anywhere in the world?

Yes. The course is taught in English so as long as you have good English skills and are able to follow the timetable of study and complete it within a year you are able to sign up.
How can I pay for the AI course?

We accept all major credit and debit cards. Mastercard, Visa, and AMEX via trusted payment processing platform provider Stripe.
How long do I have access for?

You have full access to all course materials and the Exam for one year.

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A life-changing experience

Gain an internationally recognised AI certificate, proving your skills and knowledge in this fast-growing field

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With our online self-paced classes and world-class curriculum, you will gain the skills top tech employers are looking for and be ready to start your first role in the tech industry.

Ethical and Sustainable Human and Artificial Intelligence

Artificial Intelligence and Robotics

Applying the Benefits of AI - Challenges and Risks

Starting AI how to build a Machine Learning Toolbox - Theory and Practice

The Management, Roles and Responsibilities of humans and machines

AI and the Digital ecosystem

Data Languages - Under the Hood

Data Analysis - Get, Clean, Analyize, Present

Data Visualisation - Building Effective Reports and Powerful Dashboards

Big Data - Powering Exponential Change

Machine Learning - Leveraging Algorithms for Predictions

Smart Products, Robotics and Automation

Various content (rearrange)

Soft Skills and Interview Prep - Get Hired

How AI Can Support Your Organisation

How to Manage Risk

AI Specialist

Business Intelligence Analyst

Data Scientist

Data Analysis

Data Visualisation

Statistics & Machine Learning

Database Management with SQL

Data Analysis Expressions (DAX)

Unlock the Power of Data with Python

Building a Machine Learning Toolbox

Management, Roles and Responsibilities of AI

Artificial Intelligence and Robotics