- I'm John Tricker. I recently completed the fundamentals of data science course. And actually, just today, Brandon, I received my certificate, actually, from the course that I started in August as well. For the past 12 years, I suppose professionally, I've mainly been focused on learning and development training role, which means that I have been working in different organizations on performance, on inputs, on training interventions and performance outputs really within different organizations.
And in terms of what I've studied in the past, it certainly has not been related to data science, really, at all. So this was an entirely new field for me. I've studied a lot of training and education. I started a master's in education and learning design.
I also did a master's in international management as well. I also studied business and in coaching in talent management certifications. But this was my first experience within data science, actually.
Like everybody at the moment, I think in terms of anybody who has a job at the moment or anybody who is working at all, we've became saturated in data in many ways. There's so much information that I need to use every day. And I've always been in meetings and had communication with people within my organizations that seem to be wizards with the data that I look at and get lost in a little bit.
And I wanted to do a course that would allow me to start interpreting that. I suppose to understand, knowing what to do with data to analyze it and knowing what to do with data to help me inform better decisions, really, within my role and also help others to do that too. And it began in the middle of the year, maybe around four or five months from where I started googling a data science course, these data analytics courses.
And I found a Southampton course. And I chose that because, to be honest, the communication I had with the team, with your team Brandon, and then also because of [INAUDIBLE] before I started with both the tutors and the support I was going to get from the tutors, that seemed a little bit different from their-- I think they call them MOOCs, like the courses you can do in other platforms.
I felt as if the tutoring was going to be far more available at the time when I chose to do so, which ended up being true, by the way. But it was more of how do I start to understand a little bit of the technical skills of analyzing data and then how do I help, how do I develop my ability to make better decisions and present those to others. That was really the reason why I chose the course and why I chose the Southampton Data Science Academy was because of the tutoring and the one-to-one support that was promised as well.
Actually, my tutor who is the person who is a tutor in the course, Gefion Thuermer, she's actually invited me to loads of other events and webinars that he's doing at the moment too. The actual support has continued after I feel as if I've actually got a nice little network of the tutors that I know and have connected with on LinkedIn, which I didn't expect.
But I done a master's through test in this learning before. I think I started that in 2014. That was the one in adult learning and learning design education that I've done. And that was a master's program with a University in London.
And I was so surprised by the amount of support that I received in this short six-week course. And I support the work Gefion, the tutor that I had, I think the responses to my questions came within maybe-- some of them were within a few hours, certainly within 24 hours. But every question I sent, and as I said I was second only enthusiastic, so I sent a lot. And she answered every one of those.
The best experience, actually, in terms of the support from academics, there was two live sessions a week. There was the group tutorial, which was led by the tutor and then also with all my classmates as well. So I got through the six weeks actually became a little bit of-- it was a wonderful group to work with. Actually share what we're learning and support each other. So that happened.
I think the tutor asked in the beginning to the group what they would be prepared and which time for the group tutorial so that most people could attend. Two hours was on a Friday afternoon. But then I had the opportunity to book every week.
I went to one tutorial as well, which was great because I felt that I could talk about the things that I was most curious about in that one-to-one tutorial. The tutor sent me loads of other resources and blogs and quite a few coding sites as well, like platforms I could use to learn how to code with Python because my skills at that are rudimentary at best at the moment.
But that all came from the one-to-one tutorials actually. So there was a group every Friday. But then at a time that suited me, I could actually pick one-to-one which was actually really nice. And I really look forward to those every week.
And then the feedback that I got on my assignments was also really, really helpful as well. So I suppose I definitely surpassed my expectations, really, in terms of the tutor support. And it still does because I'm still in contact with the tutor as well, which is great.
I didn't know what data storytelling-- I read it in the learning outcomes for the course before I started. But I didn't really know what data storytelling was. And the way the literature explained it in the beginning, if you've ever been on The Guardian website and you see these big exposé articles about how data has been-- open data has been-- an investigation has been done into data, or data has been leaked, and then people have done these investigations and are able to do data journalism, tell stories with data. And that was the kind of-- that was the moment where I started to realize what data storytelling was or what it could be used for.
But for me, for me within my role, actually, in terms of consulting decision-makers and learning and development objectives, data storytelling means that-- one thing I did learn, actually, quite early on in the course was that you should spend-- in any project where you're working with data, you should spend 80% of your time cleaning, organizing, sorting, and making the data ready to be analyzed. I think at some point in the course, you refer to big data, or data that you have is an unrefined resource that requires a lot of time to refine it and clean it, make sure that it's in the right data formats and the values are workable, before you then start to analyze, whether using code, whether using a platform that allows you to analyze that way, or even using Excel and the formulas.
And the course actually gave a huge amount of tutorials and how to do that, whether it be technical or the non-coding methods. And after the analysis is done, then the data visualization aspect was fascinating for me. So in the course, I learned a little bit about the neuroscience behind why data is very confusing a lot of the time for people. It doesn't allow us to actually make decisions.
For the visualization, we used Tableau and we used a few other platforms to create really nice visualizations, which then allowed the storytelling to be done. So the last assignment was building on the assignments that came before. But the final one was the data storytelling.
So it was taking all the cleaning of the data, taking all the analysis that was done, and actually telling a story at the end, an impactful story, an emotive story, around what the data really meant. And that was probably the most enjoyable part for me. So it's definitely transformed my ability to do that within my profession and work, which I didn't expect actually from the six weeks of the course.
The best thing about the assignments I found was-- there was three of them. And the course was six weeks in duration. And we got access to the assignments, the instructions for the assignments.
We did the first one in week 2, then in week 3, and then in week 4. So in the beginning, I thought I had to submit the assignments within that week. But I actually discovered that you actually get two weeks to submit every assignment, which was nice, because, as I said before, it meant that the week that I had struggled with time I was able to catch up the week after.
The first assignment was focused on Tanzanian health care facility information. And it was this really messy data that was like different tabs. All of the format was all off.
Some data was mixed in the beginning. I was confused, and I had no idea where to start. But the resources in the tutorials helped me to work out how to create the schema for the data, how to sort it, how to clean it, and then how to organize it in a way that would be ready for analysis. And I had to-- the assignment was to present the data in a file, the cleaned data, but then also provide a report of the process of doing that.
So the creating-- another good thing was that the grading rubric was really clear from the beginning. So I knew what I was being graded and going to get feedback on. So it meant that I could actually create my assignment around what I knew the feedback was going to be focused on, which was really useful. It gave different levels of grading from fail, pass, good, and excellent, I think.
The first one was about cleaning data, reorganize and explaining how you would do that. We also used an open source platform called OpenRefine made by Google. And it actually is a bit of a power tool for cleaning data as well.
The second assignment was using London Fire Brigade, real London Fire Brigade data on attendance times and the performance of that. I don't know if anybody knows. But in London, they closed 10 fire stations in the beginning of 2014 to a lot of protests. And the government said that they-- it wouldn't affect performance times and people's lives wouldn't be at risk.
So I was using real data from that. And I had to use the skills of data analysis and formulate this analysis of whether it actually had an impact on performance times, which I became fascinated with. And I submitted the assignment, again, submitted the data but then also a lot of pivot tables and a lot of outcomes and analysis in statistics to show the impact as well.
That was a really enjoyable assignment. And you have two weeks using all the tutorials to do that. And then the thought was to use that analysis and provide a [? fill ?] what I would describe as a data journalism study of what-- the stories that could be told whether that data after analyzed. And I had to provide the visualizations to use Tableau-- there was a few other platforms-- to create these beautiful visualizations for that.
So there were three assignments. There was separate grids for all three you had to pass. And I got really good, really quite specific feedback on the things that I maybe could have done better, as well, for each assignment, which was really useful.
One thing that I was really surprised about was the big picture thinking from the course, as well as the technical, as well as the technical things about machine learning, the differences between artificial intelligence and machine learning that I learned, even within the data science course. But the main thing was around data ethics and around the moral questions of open data as well. I also learned that the Office of National Statistics is an incredible resource.
So the UK'S office for National Statistics is an absolute treasure trove of data that is just at our fingertips. It sent me down a rabbit hole, actually, of-- as I say, my daughter was born recently. And I actually was wondering about when children were born, what age the parents were when children were born and how that's changed through the years. And I found data from about birth rates from the 1850s.
And my understanding of open data and how we can access it and the skills that I learned has actually allowed me to analyze a huge amount of government data. And the moral questions around open data mean-- are who should be able to access it. And are citizens-- do citizens have the data literacy skills to be able to actually understand their data, to know who is using it, when, when it's been captured?
And do-- and should organizations and schools and universities make aspects of data literacy something that they really teach? Because everybody's-- the future of professions means that everybody is going to be using data to a certain extent. So the big moral questions around open data and data literacy and whether governments and organizations and schools are doing what they need to do to prepare people for the future of data was fascinating, actually.
And that's something that I never really thought would be so thought-provoking during the course. But it's a thing that's now motivating me to learn how to code. Because I think, at one point, my daughter is going to be asking me, Daddy, how do I do this? And I can help with simple mathematics at the moment, but I can't help with coding. So I think that's the bigger questions, bigger picture questions that I never anticipated that I learned.
The one aspect that impressed me the most, actually-- and I have studied aspects of adult learning and designing pedagogy, like digital pedagogy, actually, in my master's degree. And the case studies that I'd done during my master's about how to design assignments for online learning meant that when I saw the assignments, I could really understand the rationale between why we were being asked to do the things in the way that we did, what we had been asked to produce.
And the way the assignments-- the way the resources and the timing of those was designed with the discussion activities because it all points to asynchronously posting the discussion forums through the week, and ask questions, that's like a constructivist approach to getting the group to learn together, to create new knowledge and support each other, which was great.
So the thing that actually impressed me the most was the way that the assignments were designed. It meant they were really clear what you had to do. Go and find this. These are the parameters in which you should create your answers. And the submission of those was really clear.
So everything felt-- in hindsight, actually, now, everything actually felt really well designed, which-- if there was any points where things weren't clear or I feel I wanted to clarify something, then I got my answers to my questions really quickly, too, which helps, especially if it's online. I think the assignments, the fact that they were real, it was real data and the fact that it was quite emotive topics, actually, was really well-designed, really well-selected assignments, I would say, in hindsight, probably why I was so motivated during it.
What I mentioned a little bit earlier, to make sure you log on every Monday morning and make a plan for the week around when you're going to engage with the resources, when you're going to read. Everything that's available, go through the tutorials, the video lectures that are there so that you can really engage with all the resources and learn everything. I felt as if the more I read, it benefited me later in the week when I had to do the assignment or when I had to take part in the discussion activity.
So I'd say reviewing everything that's required for the week on a Monday and then making a little plan, that really helped me. And then attending the group tutorials was great, as well, not because it helped so much with the assignments or what I had to produce. Well, it did actually, because there was a lot of questions I could ask during that. But it really made the-- it really ensured that the course felt alive.
I've done quite a few courses online in the past. And in hindsight, those felt a little bit lonely in comparison to this experience in the six weeks because those group tutorials and the one-to-ones were so warm and engaging, actually. So I would say my other tip would be to attend those, and definitely a few if you can.
I'm so glad I'd done the course when I did. And at points, it was quite difficult. Like I said, there was that week that I struggled. And I did very little in one week. But I was able to catch up the week after. So I'm so glad that I started the course when I did. Because there are courses that I've been thinking about doing in the past, and they haven't happened.
And I think there's a lot to be said for-- because the course is so flexible and because the skills that I've taken away from it are so instantly applicable into my profession and into the work that I've been doing in the last few weeks alone, I would say I'm really glad that I started when I did, actually, because time goes on. I always feel that it's a risk to start something. And then when you do it, I'm glad I did as well.
So that would be my only-- maybe if I was going to give some advice, it would be, time is the only scarce resource, and it never feels like we have ample time. But you can always manage it, really. And this course is certainly set up for that, thankfully.