This course equips you with the theoretical knowledge and both practical and technical skills to participate in the flourishing data revolution, helping you to contribute to and benefit from the new data-driven economy. The course emphasizes a hands-on approach to learning data and skills, offering a number of interactive, online exercises that will let you try out many of the techniques and concepts covered in the taught material.  In addition the course introduces theoretical AI concepts.

The course is broken into four weeks.

Week 1: You will get “hands-on” experience of Jupyter the web-based learning environment which you will use for the course exercises and assignments.  This week also contains a Python Primer activity for those of you who are unfamiliar with the programming language or would like a refresher.

Week 2: You will learn about the fundamental terminology and processes in data science, discovering the technology landscape that has helped fuel the data explosion, and the tools that data scientists use to unlock the hidden value in these vast amounts of data. This week also contains an introduction to using Python for data science. You will begin gaining hands-on experience of data science in this week, focusing on collecting, storing and managing data.

Week 3: In this week you will understand how the data is analyzed, covering a range of techniques that any data science team will encounter from statistics and machine learning and you will use Python to analyze some given data.

Week 4:  Introducing use of search, clustering and knowledge graph processes. The case study in week four introduces in further detail the concepts of supervised and unsupervised learning to identify patterns which exist in data without classification labels. Such methods are used extensively by searching algorithms as they enable clustering of similar or closely-related results. By the end of this week you will have gained an understanding of the means by which todays search engines provide results and how the leverage structured information from knowledge bases to enhance both performance and user experience.

Aims and learning outcomes

This module aims to provide you with the knowledge and expertise to become a proficient data scientist.

Having successfully completed this module, you will be able to:

·         Understand the key concepts in data science, including their real-world applications and the toolkit used by data scientists;

·         Explain how data is collected, managed and stored for data science;

·         Implement data collection and management scripts using NodeJS and MongoDB;

·         Demonstrate an understanding of statistics and machine learning concepts that are vital for data science;

·         Produce Python code to statistically analyse a dataset;

·         Critically evaluate data visualisations based on their design and use for communicating stories from data;

·         Plan and generate visualisations from data using Python and Bokeh.

·         Identify potential applications of AI in practice

·         Be familiar with the fundamental concepts of extraction, clustering, prediction, as well as search and planning techniques

·         Learn how software can be used to process, analyze, and extract meaning from natural language, images and numerical data to understand the world the way we do

Key benefits:

1.      Tutor-led – University of Southampton and Chabukaisix Learn academics guide students through the course material 

2.      Continuing Professional Development (CPD) accredited

3.      Hands-on – students learn how to apply concepts and techniques within their workplace.

Education provider: 

Chabukaisix Learn and  Southampton Data Science Academy 

Southampton Data Science Academy forms part of the Web Science Institute at the University of Southampton – ranked among the top 100 of universities globally.

Developed in partnership with leading global education specialists Cambridge Education Group (CEG), the Academy bridges the data skills gap in today’s increasingly data-driven world through world-class training and education from industry-leading academics and thought leaders in the field of data science.

CEG Digital partners with prestigious, high quality UK universities to deliver online or blended courses to a global market on a part-time, flexible basis. Courses are delivered using cutting-edge, tablet friendly technology and sector leading pedagogy.

Southampton Data Science Academy training is CPD accredited, meaning it has reached the Continuing Professional Development (CPD) standards and benchmarks.

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With the growing interest in learning online courses, as the future of learning shifts to digital, tutors are delivering academic resources online and capitalizing on digital educational platforms. We unite with Chabukai6 and SDSA with one goal - to bring closer the opportunity to expand your knowledge and study subjects under the guidance of professionals in their respected field.

Our students will be able to join the online tutoring courses and will get great benefits in the context of providing an outstanding and engaging learning experience in the field of Data science, Machine learning, AI, etc.

Course objective

This course equips you with the theoretical knowledge and both practical and technical skills to participate in the flourishing data revolution, helping you to contribute to and benefit from the new data-driven economy. The course emphasises a hands-on approach to learning data skills, offering a number of interactive, online exercises to allow you to try out many of the techniques and concepts covered in the taught material against real examples.

Course Structure

The course runs over 6 weeks and is broken down into manageable weekly topics: 

Module 1: In Module 1, we discover what data science is, along with key examples of it in action. We learn about the overlap with data journalism and open data, to look at how data science is changing the way we tell stories. We explore the spectrum of data and the importance of understanding your rights to use different types. We use the course forums to collect more great data science examples and look at the applications in your own domain.

Module 2: In Module 2, we learn about the process of data science, from gathering to visualisation, covered in the course. You also begin your hands on experience looking at the critical aspect of data management. The first assignment is based upon a real case study of hospital performance data in Tanzania and focuses on the importance of standards when collecting and organising data. This week also looks at the importance of data cleaning and the techniques to clean dirty data.

Module 3: The major case study of the course is introduced in Module 3. We begin looking at a large piece of data analysis using up to date incident records from the London Fire Brigade. In 2014, 10 fire stations were closed in London amid protests and claims that this would put lives at risk. We look at the evidence to find out what has happened as a result and if more changes need to be made. This week looks at the data processing and analysis that can help reveal the answer. Data visualisation is the focus of Module 3. Choosing the right visualisation to communicate your findings to everyone is of critical importance. This week introduces many of the different types of visualisations available and looks at the use of aspects like colour to represent different dimensions in data. You are challenged to spot when you are being deceived, and to select the most appropriate visualisations.
Module 4:  In a very short period, AI has evolved into an essential part of our daily lives (e.g. personal assistants, news and content recommendation). Nowadays, AI is able to defeat professional gamers in chess, Go and video games. The potential benefits from AI can be tremendous. Topics introduced in this week include how the impact of user expectations and advances in computing technology contributed to a history of AI ‘winters’ and ‘summers’, the core technologies associated with AI and the types of data these technologies use. The contributions of ‘big data’, ‘cloud computing’ and the ‘Internet of Things’ are discussed along with possible legal, moral and ethical implications which may have arisen. The AI present and future are also considered at the end of this week.

After successfully completing the course, you’ll be able to: 

• Explain the key concepts in data science and its real-world application.
• Classify the different types of data available along with rights for usage.
• Implement an effective data collection and management strategy.
• Prepare data ready for analysis
• Create a number of data visualisations.
• Start working with live data.
• Critically evaluate the challenges and opportunities of exploiting data science in your organisation.
• Demonstrate high level understanding what AI is and the role it can play to deliver benefits for your organisation.
• Identify potential applications of AI in practice.
• Understand the main capabilities of AI and the core technologies that help deliver them

Key benefits:

1. Tutor-led – University of Southampton and Chabukaisix Learn academics guide students through the course material 
2. Continuing Professional Development (CPD) accredited
3. Hands-on – students learn how to apply concepts and techniques within their workplace.

Education provider: 

Chabukaisix Learn and  Southampton Data Science Academy 

Southampton Data Science Academy forms part of the Web Science Institute at the University of Southampton – ranked among the top 100 of universities globally.  Developed in partnership with leading global education specialists Cambridge Education Group (CEG), the Academy bridges the data skills gap in today’s increasingly data-driven world through world-class training and education from industry-leading academics and thought leaders in the field of data science.

CEG Digital partners with prestigious, high quality UK universities to deliver online or blended courses to a global market on a part-time, flexible basis. Courses are delivered using cutting-edge, tablet friendly technology and sector leading pedagogy.

Southampton Data Science Academy training is CPD accredited, meaning it has reached the Continuing Professional Development (CPD) standards and benchmarks.

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-Professor Dame Wendy Hall

Wendy is Regius Professor of Computer Science and Pro Vice-Chancellor (International Engagement) at the University of Southampton, as well as Executive Director of the Web Science Institute. She is a member of the World Economic Forum’s Global Futures Council on the Digital Economy, was co-Chair of the UK government’s 2017 AI Review, and is the first Skills Champion for AI in the UK.

-Professor Vancho Chabukovski

Vancho is visitor at the Web Science Institute (WSI) and Web and Internet Science (WAIS) research group of the School of Electronics and Computer Science of the University of Southampton. He is a professor of Computer Science, Software Development and Databases, Programming Languages and Systems, and Information Systems at Faculty of Natural Sciences and Mathematics, Saints Cyril and Methodius University in Skopje, Republic of North Macedonia. He has extensive experience developing e-learning systems and is CEO and co-founder of Chabukai6 Limited and founder of Chabukaisix Learn, partnering with Southampton Data Science Academy (SDSA) at the University of Southampton and helping to extend the reach of Southampton Data Science Academy’s learning propositions in South-East Europe (SEE).

-Professor Les Carr

Les is Professor of Web Science at the University of Southampton’s School of Electronics and Computer Science. He’s also a Director of the Web Science Institute and previously Director of the Web Science Centre for Doctoral Training. His research on Open Access and Open Data led to the establishment of Eprints – offering Open Access publication and data services, training and support to the research industry.

-Dr Gary Willis

Gary is an Associate Professor in Computer Science at the University of Southampton. He graduated from the University of Southampton with an Honors degree in Electromechanical Engineering, followed by a PhD in Industrial Hypermedia Systems. Gary is a Chartered Engineer, a member of the Institute of Engineering Technology, and a Principal Fellow of the Higher Educational Academy. He is also a visiting Associate Professor at University of Cape Town and a research professor at RLabs.

-Dr David Millard

Dave is Associate Professor of Computer and Web Science at the University of Southampton, David is a founding member of the Web and Internet Science research group within the School of Electronics and Computer Science (ECS). He represents ECS on the steering group for the Web Science Centre for Doctoral Training. David is currently Vice-Chair for ACM SIGWEB.

-Dr Bob Blair

Rob is Visiting Fellow at the University of Southampton’s School of Electronics and Computer Science. He holds an MSc Information Systems from University of East Anglia and an MSc Web Science from University of Southampton. Qualified to teach Physics, Mathematics and Computer Science, Rob is a highly-experienced classroom teacher and online tutor specializing in data science.

-Dr. Manuel Leon-Urrutia

Manuel is Senior Teaching Fellow in the Web and Internet Science research group of the Electronics and Computer Science department of the University of Southampton. He holds an MSc & PhD in Web Science, a PGCE in education and an MA in Applied Linguistics. Manuel is specialised in learning technologies, with experience in learning design and research interests in MOOCs and learning analytics. Prior to joining the Computer Science department in 2012, he worked for 6 years as an editor in a publishing company, and 6 more years as a language teacher in the University of Southampton. Manuel currently has an academic, senior tutoring and learning design role in the Southampton Data Science Academy.

-Dr. David Tarrant

David is learning skills lead and a data scientist at the Open Data Institute.

David joined the ODI from the University of Southampton where he was a lecturer in the Web and Internet Science Group. He was responsible for creating the world’s first undergraduate course in open data.

Since joining the ODI David has put in place key educational content that helped transform governments and unlock over $15m for startups. Additionally he has applied his data science skills to building policy making tools for open data leaders, including the Open Data Barometer visualization. This tool has been used to guide policy development and allow leaders to compare and contrast their open data initiatives with other similar ones globally.

As learning skills lead, David is responsible for the direction and quality of the ODI’s learning offering. Products include face to face training, eLearning and the ODI’s learning records system (LRS). Underpinned by the skills framework, all of these products put together ensure that the ODI can offer high quality online and face to face training to as many people as possible.

-Dejan Zlatkovski

Dejan is an eLearning specialist with over 20 years of experience in the field. He has a BSc in Computer Science from the Faculty of Natural Sciences and Mathematics, the Saints Cyril and Methodius University in Skopje, Republic of North Macedonia, and an MSc specialisation in Public Policy Making in Education and Human Development from George Washington University.

Dejan worked for the Ministry for Education and Science where he was responsible for educational policies related to ICT in education, eLearning and R&D projects for academic and educational IT networks, and for the National Agency for European Educational Programmes and Mobility, where he was responsible for educational policies networks and studies, and deployment of the eLearning platforms in the formal and vocational education and training. He is currently a consultant on ICT related projects and has tutoring and eLearning design roles at Chabukaisix Learn.

-Professor Riste Temjanovski

Riste is a Professor at University Goce Delcev – Shtip, North Macedonia and holds Research Chair in Department of Economy at the Faculty of Economics. He has a demonstrated practice of working in the higher education and entrepreneurial community. Skilled in Transport and Logistics Science, E-business, Management Information Systems, E-Marketing, GIS Software and Data Analysis.  He is strong education professional with a PhD in Economic Science, Master of Spatial planning focused on Transport Science.  He is experienced in gathering and analysing economical data, statistical data analysis and graphic data presentation and  interpretation. He has tutoring and course content developing roles at Chabukaisix Learn.

Fundamentals of Data Science - Non Technical

Fundamentals of Data Science and AI Courses

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