Manuel [00:00:03] I'm Manuel. I'm a lecturer in the University of Southampton in the Electronics and Computer Science Department, and I am also the head of learning of Southampton Data Science Academy. What does it mean? Well, I run a team of some 11 instructors who teach in the courses that we deliver and also participate in the design of those courses, and they lead the design and the design of those courses, and the tutoring approach, I designed the tutoring approach that we are delivering in those courses. We offer at the moment three main public courses, one of them is called Data Science Nontechnical. Yes, and Fundamentals of Data Science, Non-technical, that's the whole title of the course. And in this course we cover the big picture of data science and we cover it in what I would say practical way, but without getting our hands dirty with gold. And there is another course Fundamentals of Data Science Technical, and in this course, we indeed get our hands dirty with gold and it requires the use of our programming language called Python. And we have to use tools to analyse, to visualise and to manage data. And then we have an artificial intelligence force in which we focus the use of artificial intelligence in the current business industry. Ok, so we mainly cover how to choose the best artificial intelligence application for specific tasks within an organisation. In that course, in the artificial intelligence course, we don't use programming. We don't code, but we cover the fundamental technical aspects of the most common artificial intelligence methods. So those courses are fully online. That's the most important feature of these courses, we don't do any face to face. Those courses are also bite size, it means that they are short courses. Our longest course would amount for a total of 100 hours of study. Our shortest course would amount for some twenty five hours of study and we move within this range. The length of those courses, well our average then is six weeks. Although we have other courses that are eight weeks long and some others that are 10 weeks long. How they are structured, they are structured in modules. Yes, and each module can be one week long or two weeks long. Those modules cover one topic of data science. Usually, they cover one part of the data science pipeline. What kind of data science pipeline, that will depend on the focus of the course. We have different courses, some of them are non-technical and they are the big picture of data science. Some of them, they are more technical, in which the use of our programming language called Python is expected. And other kind of courses where we cover artificial intelligence. We also have courses on Digital Marketing and even Data Security. And the common structure of all those courses is that they are divided in modules and each of the modules have got a beginning and an end, and kind of assessment days. All courses have also one or two big assessments, as well as some smaller assessments along the course. So that will be more or less the main and most common feature of all the courses along all our portfolios. These courses are indeed tutor led, and this means that they offer many hours of contact with tutors, both direct contact, face to face, online. I mean, via asynchronous communication, in calls or in teleconference. And also via discussion forums and also via messages. All these ways in which the delegates can interact with the tutors are all designed for the tutoring team to support the progression of the delegates along the course, and they are also meant to have conversational learning experience by which you learn in those courses by talking about it. Who do you talk to during the course? You talk to your tutors, but you also talk to your peers. So the role of the tutors is also organise and foster these communities of interest and to make sure that along the learning experience of all the weeks of this course, the delegates engage in interesting conversations with like minded people and with people who share the same interests. Most of them graduated doctors, so if not graduated, they are on the way to finish their doctoral studies in computer science related topics and in data science related topics.
Manuel [00:07:05] So we have quite a variety of tutors coming from quite a variety of disciplines. All of them with a common interest in data science, I will give you an example. We have tutors who come from the health science discipline, and they have been for many years analysing diverse quantities of data related to health science. So these tutors are very versed in data science in general and data science for health science in particular. We have other tutors, we have tutors who have been working, doing research on blockchain or others who have been doing research on explainability of artificial intelligence. So we have quite a pool of experts in data science from a variety of disciplines, and all of those tutors bring all the expertise accrued during their professional lives and during their academic lives and they bring them to the course so they are experts they work with data on a day to day basis, and they have a wide expertise in training other people on the use, management, analysis, visualisation of data. So the tutors support you during the whole learning journey of this course. They know your name, they know who you are and they support you even on a personal level.
Manuel [00:09:10] The reason is that there are not high ratios of students to tutors. I mean, we have a cap of delegates, of students, so that the tutor knows every single student. They know their name, they know who they are, so there is dedication. There are one-to-one tutorials that happened during the course. And there are group tutorials and there are many, many opportunities for the delegates to talk to the tutors so that so by the end of the course, they have developed a kind of a relationship, from tutor and tutee. So those tutors support the delegates in such a way that they have the necessary conditions for completing the course and completing the course means that that delegate has worked on a series of assignments and assessments. And they are not just quizzes and it's not enough by just attending the tutorials. The delegates, to finish the course, they have to demonstrate that they have assimilated the knowledge within the course and that they can produce something and the tutors support this way of learning along the whole course via discussion forums, messages, feedback in the assignments, one to one tutorials, group tutorials and announcements. So there are like five or six different weight ways in which the tutors communicate. These courses are for people, usually professionals who have already finished their studies and they are already in the job market. For those professionals to either progress within their organisation or for changing careers or for giving a slight turn to their duties and responsibilities within the organisation. And these courses are also suitable for those who are looking for a job. So they are job seekers who want to explore the data science related industry, or the data-driven industry. So I would say three main profiles. You are working in a
Manuel [00:12:21] company and you want to progress within the company by learning data science techniques. You are working in a company and you want to change departments and you want to go to a more data driven department or you are looking for a job and you want to enhance your data science skills and obtain a certificate that might help you land in a data-driven role. Yeah, so I would say that some of the leading current challenges are that we are sitting on vast amounts of data and we are still not harnessing the value of this data. So we are still very reliant exclusively on our expertise within a specific area, our own experience, our intuition. And I would say that if we complement this with the insights that data can bring us, we can enhance the value of all of our operations. That doesn't mean that data exclusively is going to give us the answers and is going to help us make better decisions. It is the combination of our expertise and the insights that we can extract from data that can help us add value to all the activities that we do in all sorts of different areas in the data-driven industry. So these courses, what they address is that they help you understand, how can you maximise the value of the data that you have available and how you can complement these insights that data brings you, into your day to day decisions that you do at work. So they apply to your day to day work and they are based on real life case studies. So they are oriented to professionals
Manuel [00:15:11] and we use real life scenarios, I would say. The main difference that sets this course apart from other similar courses on offer, is that the tutoring team is dedicated for you to progress in the course. So you as a delegate, you will have the chance to interact with a tutor much more than in any other course, so this is why we have so many tutors and those tutors dedicate many hours to the interactions with the delegates. So that makes these courses a slightly different learning experience in which there is much more interaction, much more conversation, and which we believe makes the learning experience much more meaningful than other kinds of learning experience, which might be a little bit more isolated. These courses can bring all sorts of career opportunities, and it depends on the circumstances and on the aims of the delegates in the course. These courses will expect the delegates to develop a critical understanding of the latest developments, both in artificial intelligence and in data science and the latest techniques. So by completing these courses, you as a delegate will have much more wider view and broader view and deeper view of what the data science industry is bringing along. And also there will be many other career opportunities from other angles, for example, the conversational nature of those courses that leads to many networking opportunities.
Manuel [00:17:44] So by joining those courses, you are also joining a kind of community of people who have a much closer relationship to the data science industry. This means that you will meet many people, you will meet the tutors as well. And in the materials of the courses, you will have a much broader view of the current data science industry. So this is a career opportunity in itself, so knowing what others are doing basically. My final and central piece of advice when enrolling to this course, is make use of the tutoring support available, so make use of the opportunities that we put together for talking to, not only the tutors, but also your peers. So talk about data science with other people during the course. This is the main advice that I can give you, and I think this is what will make you make the most of those courses. You will also have the opportunity to go through the materials of the courses, which are developed by by leading academics in the area. And you will also have the opportunity to develop an understanding of data science and artificial intelligence by completing the assignments and the assessments. But the main value of this course is that you will be doing it along with other people, not only the tutors, but your peers.