WEBVTT

1
00:00:00.225 --> 00:00:01.165
My name is drt.

2
00:00:01.485 --> 00:00:03.925
I studied diagnostics, data and digital health,

3
00:00:04.145 --> 00:00:06.005
and I graduated in 2024.

4
00:00:06.565 --> 00:00:09.645
I work at Zopa Bank in London Bridge

5
00:00:09.905 --> 00:00:11.765
and my role is a data scientist.

6
00:00:12.505 --> 00:00:17.405
My role involves making models related to credit risk

7
00:00:17.465 --> 00:00:19.525
or fraud modeling, things like that.

8
00:00:19.985 --> 00:00:23.085
And it usually involves making models from beginning,

9
00:00:23.185 --> 00:00:25.805
so from conception all the way up to monitoring.

10
00:00:26.115 --> 00:00:29.045
There's lots of other things involved as well, like coding,

11
00:00:29.505 --> 00:00:32.605
uh, working in a team, managing different people.

12
00:00:32.905 --> 00:00:34.925
And so I get to experience a lot of different things.

13
00:00:35.645 --> 00:00:37.765
I decided to study this course

14
00:00:37.795 --> 00:00:41.485
because I wanted to do something related to maths

15
00:00:41.485 --> 00:00:43.845
and computer science, like my undergraduate degree,

16
00:00:44.185 --> 00:00:47.125
but I wanted to do it in a field that really mattered to me

17
00:00:47.225 --> 00:00:49.445
and something that could make a difference in the world.

18
00:00:49.845 --> 00:00:51.885
I was able to learn a lot about the NHS

19
00:00:51.885 --> 00:00:54.165
and do something that I thought was really important.

20
00:00:54.975 --> 00:00:57.285
There were lots of things that I learned from my course

21
00:00:57.285 --> 00:00:59.845
that I apply now, especially modeling.

22
00:01:00.185 --> 00:01:01.245
So before my course,

23
00:01:01.405 --> 00:01:04.365
I hadn't really experienced any modeling before.

24
00:01:04.965 --> 00:01:08.165
I did different things like modeling and Python

25
00:01:08.345 --> 00:01:12.165
and matlab, um, using feature engineering,

26
00:01:12.535 --> 00:01:15.565
model selection, model design, all of these different things

27
00:01:16.225 --> 00:01:19.245
to create something that would be able to predict

28
00:01:19.315 --> 00:01:20.405
what I wanted to predict.

29
00:01:20.825 --> 00:01:23.125
And that is something that I use every day. Now.

30
00:01:23.125 --> 00:01:25.445
Right now I'm in the process of designing a model,

31
00:01:25.785 --> 00:01:27.605
and the reason I know how to do

32
00:01:27.605 --> 00:01:28.805
that is because of my course.

33
00:01:29.225 --> 00:01:32.165
One of the modules that I did was operational management,

34
00:01:32.215 --> 00:01:34.885
which involves studying the NHS workflow

35
00:01:34.945 --> 00:01:36.325
for a few different hospitals,

36
00:01:36.385 --> 00:01:37.725
for a few different processes.

37
00:01:38.305 --> 00:01:41.205
And that really helped us identify all

38
00:01:41.205 --> 00:01:43.645
of the different challenges that healthcare is facing today.

39
00:01:43.915 --> 00:01:45.965
That includes things from budgeting

40
00:01:46.185 --> 00:01:50.365
to organizing the entire workflow, deciding who does what

41
00:01:50.425 --> 00:01:52.565
and what is needed at any given moment.

42
00:01:52.945 --> 00:01:56.325
And I just think that that really helped me want

43
00:01:56.345 --> 00:01:58.565
to improve the NHS in the future.

44
00:01:59.405 --> 00:02:02.405
I had a lot of career support when I was at work.

45
00:02:02.885 --> 00:02:05.045
I went to all of the career fairs

46
00:02:05.045 --> 00:02:06.605
that I could within the last year.

47
00:02:07.065 --> 00:02:09.605
Um, I had CV support with,

48
00:02:10.085 --> 00:02:12.285
I think it's called My Advantage, Warwick.

49
00:02:12.605 --> 00:02:14.645
I also went to a lot of conferences

50
00:02:15.305 --> 00:02:18.085
and that helped me get a bit more exposure to the field,

51
00:02:18.105 --> 00:02:19.405
to the different kinds of things

52
00:02:19.405 --> 00:02:20.685
that we could do in data science.

53
00:02:20.905 --> 00:02:23.885
So it was very inspiring and very useful for me.

54
00:02:24.065 --> 00:02:25.965
Before starting at Warwick, I

55
00:02:26.825 --> 00:02:28.405
was a little bit all over the place.

56
00:02:28.565 --> 00:02:30.725
I didn't know what I wanted to do with my life.

57
00:02:31.085 --> 00:02:32.445
I didn't know what I was good at.

58
00:02:32.745 --> 00:02:35.565
And when I studied maths and computer science and health

59
00:02:35.785 --> 00:02:38.085
and all of these different things at work, I realized

60
00:02:38.085 --> 00:02:39.445
that it's okay to have a passion

61
00:02:39.465 --> 00:02:40.565
for so many different things.

62
00:02:41.065 --> 00:02:43.845
And it is actually really good for finding things

63
00:02:43.845 --> 00:02:46.085
that combine different disciplines, bring them together

64
00:02:46.105 --> 00:02:47.525
and actually create something new.

65
00:02:48.345 --> 00:02:51.845
If I could give a prospective student for my course advice,

66
00:02:52.385 --> 00:02:53.805
it would definitely be

67
00:02:53.865 --> 00:02:56.725
to not worry if you don't know what to do yet.

68
00:02:57.035 --> 00:03:00.245
When I started this course, I did not know what I wanted

69
00:03:00.245 --> 00:03:02.805
to do, and I didn't know I wanted to be a data scientist,

70
00:03:03.145 --> 00:03:05.805
and I know for a fact that my other course mates have gone

71
00:03:05.805 --> 00:03:08.405
on to do so many different things like clinical sciences,

72
00:03:08.475 --> 00:03:11.205
life sciences, data science, computer science.

73
00:03:11.505 --> 00:03:13.325
So many different things are available to you

74
00:03:13.425 --> 00:03:16.645
and you can access the things that you need to access

75
00:03:16.865 --> 00:03:18.245
to get where you want to be.

76
00:03:18.545 --> 00:03:20.125
And you have plenty of time to decide.
