Duration: 4 weeks
Number of hours: 20 (includes tutorial support + self-study, assignments and peer discussions)
Number of Assignments: 3
Number of Engagement Discussions: 5
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.
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:
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.
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