Certificate in Data Science
(Level 7 – 20 Credits)
 

This Level 7 Certificate is a 20-credit qualification. The aims of this qualification are  to provide an in-depth investigation on the field of Artificial Intelligence (AI) and Data Science with a focus on the practical application of these technologies in high impact real world problems. This qualification is specifically designed for professionals that want to explore Data science and AI and their latest applications; familiarize themselves with available tools and software packages; benefit from case studies, demonstrations and practical exercises providing solutions for real world problems; develop simple machine learning models; consider ethical and legal implications of AI deployment; acknowledge limitations with regards to policy and data protection; and discuss future directions of AI .

Learners are able to take this qualification as continuing professional development, or as entry onto larger qualifications at the same or higher levels.

To obtain the Certificate in AI for Real World Applications learners must achieve six Mandatory Units.

Total Qualification Time: 150 Hours

Total Guided Learning Hours: 60

Total Credit Value: 20

Unit Codes	Unit Title	Level	Credit	GLA
Introduction to AI and data science		7	2	6
Applications of AI and data science		7	2	6
Data science software and tools		7	2	6
Data collection and preparation		7	2	6
Forecasting, classification and optimization		7	2	6
Mini AI Project		7	10	30
Total		7	20	60

Introduction to AI and data science

Learners will develop a deep understanding of the most recent developments in AI and data science. The learners will explore the core mechanisms and processes that fuel these technologies and investigate their impact and limitations. The learners will:

• Demonstrate a solid understanding of the fundamental concepts of data, big data, AI and data science.
• Being able to critically compare different approaches and algorithms.
• Understand the process of implementing AI models for real world applications.
• Understand legal and ethical issues for AI and data science.
• Identify limitations and future directions for AI technologies.

Indicative Content

• Introduction to Data, Big Data and Data science
• AI Fundamentals and core algorithms
• Classification, Regression and optimization
• Regression, SVM, Neural Networks, Genetic algorithms, Fuzzy Logic
• AI ethics and GDPR issues
• AI limitations and future directions

Applications of AI and Data Science

During day 2, the learners will be presented with examples from the application of AI and Data science technologies in different areas. The learners will:

• Explore the benefits of AI and data science for business.
• Familiarize with intelligent digital health applications.
• Understand the impact of these AI technologies in future transport and cities.
• Recognize the benefits of AI in education.

Indicative Content

• AI for business: sentiment analysis, sales forecast, pricing optimization etc.
• AI for education: smart classrooms, personalised learning, student emotion detection etc.
• AI for transport: autonomous vehicles, driver fatigue detection, passenger profiling etc.
• AI for smart cities: traffic congestion forecasting, flood monitoring, energy grid management etc.
• AI for health: intelligent wearables, personalised treatment plans, remote diagnosis etc.

Data science software and tools

Different options for utilizing AI and Data Science technologies, such as software tools (such as SPSS, MATLAB) and programming languages (Python, R) will be presented. The learners will:

• Explore available software packages and identify their advantages and limitations.
• Learn how to effectively interpret and present the results generated from AI models.

Indicative Content

• Introduction to Python
• MATLAB NN and Fuzzy Logic Toolbox tutorial
• SPSS NN tutorial
• JAVA WEKA tutorial

Data collection and preparation

By exploiting real datasets, this day will introduce to learners practical examples of collecting and preparing data for developing AI solutions. The learners will:

• Be able to identify databases and online sources that include data that can inform the development of intelligent applications.
• Explore ways that data can be collected and pre-processed.
• Investigate the means for organising, combining, managing and visualising data.
• Study clustering and dimension reduction techniques.

Indicative Content

• Finding data online
• Extract data from databases and online sources
• Outlier detection
• Hierarchical clustering. Fuzzy clustering. Density-based clustering. Model-based clustering
• High Correlation Filter, Random Forests/Ensemble Trees, Principal Component Analysis (PCA), Backward Feature Elimination

Forecasting, classification and optimization

This day includes demonstration of real AI applications for all basic AI categories. The course will present to learners practical examples of building and applying data analytics and AI. By using real datasets and techniques utilized by professionals in the field, the learners will develop their own intelligent solutions. The learners will:

• Focus on the fundamental steps for researching and creating a complete solution that can be used to solve a real-world problem.
• Explore a range of models based on different machine learning approaches to tackle forecasting, classification and optimization problems.
• Gain an in-depth understanding on concepts such as training and tests sets, overfitting, classification and forecasting accuracy, error measurements and others.

Indicative Content

• Training and testing, N-fold validation, Leave one out method
• Mean Error, Mean Square error, Confusion Matrix
• Linear regression for forecasting and classification
• Neural Networks for forecasting and classification
• Genetic algorithm optimization

Mini Project

The learners will be involved and supported in the development of an intelligent application focused on addressing a real-world challenge. The learners will be able to:

• Develop a basic AI solution to address a real-world problem.
• Familiarize with working on multidisciplinary AI based projects.
• Critically evaluate the development process and results of an AI application.