1 00:00:00,000 --> 00:00:06,000 Hey everyone, this is Odette here. Welcome to Queen's Citizens. 2 00:00:06,000 --> 00:00:15,000 So today I just started the Irish talk about why I wanted to grab school and my experience 3 00:00:15,000 --> 00:00:24,000 with it as a first person in the family to pursue masters of science in Canada. 4 00:00:24,000 --> 00:00:30,000 So I am a second generation Canadian of less African descent. 5 00:00:30,000 --> 00:00:42,000 And yeah, university itself is really hard and pursuing further education was not exactly what I expected. 6 00:00:42,000 --> 00:00:49,000 And I just want to be very open about my experience on my own podcast. 7 00:00:49,000 --> 00:00:54,000 So if you're new here, I love for you to stay. I welcome you. 8 00:00:54,000 --> 00:01:00,000 And yeah, if you love this episode, give it a thumbs up and share it with a friend. 9 00:01:00,000 --> 00:01:12,000 So I'm just going to get into it. Before grad school, I had a bachelor's of science and math at the University of Saskatchewan. 10 00:01:12,000 --> 00:01:21,000 So if you don't know what that is, that's located in the province of Saskatchewan, and specifically in the city of Saskatoon. 11 00:01:21,000 --> 00:01:29,000 So with the math and stats program there, I felt like it wasn't really good. 12 00:01:29,000 --> 00:01:32,000 And I know that I'm not the only one saying this. 13 00:01:32,000 --> 00:01:37,000 So I knew for sure that I didn't want to do a masters in math. 14 00:01:37,000 --> 00:01:43,000 Math itself is it's a universal subject, but it's really hard itself. 15 00:01:43,000 --> 00:01:46,000 I always wanted to do something health-care-related. 16 00:01:46,000 --> 00:01:52,000 So that's why I did the BIOS statistics program through the School of Public Health. 17 00:01:52,000 --> 00:01:59,000 So when I tell people that I didn't math degree, they're not sure how that fits in with public health. 18 00:01:59,000 --> 00:02:09,000 But to pursue a BIOS statistics degree, the masters level, you can have either a math degree or a stats degree. 19 00:02:09,000 --> 00:02:16,000 And even when you pursue a math degree at the bachelor's level, you can take a lot of stats courses. 20 00:02:16,000 --> 00:02:24,000 So I pursued BIOS statistics, which is a collaborative program at the School of Public Health. 21 00:02:24,000 --> 00:02:30,000 And yes, it was during COVID-19. So I felt like there would have been a lot of opportunities. 22 00:02:30,000 --> 00:02:39,000 During my last year of studies at the undergraduate level, I just heard a lot of things about data science, data analysis. 23 00:02:39,000 --> 00:02:49,000 And I thought maybe I could put my interest together, and I felt like I'll be meeting some type of demand. 24 00:02:49,000 --> 00:02:55,000 So in my program, I told people it's basically like health statistics. 25 00:02:55,000 --> 00:03:09,000 So when you're reading a number, let's say, 5% of patients with pancreatic cancer deal with a food security, that's just a random fact. 26 00:03:09,000 --> 00:03:16,000 I always wondered, like, where those numbers come from, so you need to have some type of BIOS statistics knowledge 27 00:03:16,000 --> 00:03:21,000 to be able to understand where these numbers are from. 28 00:03:21,000 --> 00:03:27,000 So basically, in grad school, I also have to take an epidemiology course. 29 00:03:27,000 --> 00:03:33,000 Lots of people don't know what that is. Even though we just went through a pandemic. 30 00:03:33,000 --> 00:03:39,000 So epidemiology is basically the study of epidemics. 31 00:03:39,000 --> 00:03:47,000 I can further explain that by saying that epidemiology, you're looking at the distribution of disease. 32 00:03:47,000 --> 00:03:52,000 So for example, sometimes more females are affected by a certain disease. 33 00:03:52,000 --> 00:04:03,000 They're male sometimes when you look at certain age groups. Like, okay, people who are very young tend to have this sort of disease. 34 00:04:03,000 --> 00:04:10,000 So like, where I currently live, tuberculosis, what we call TB, is it's epidemic. 35 00:04:10,000 --> 00:04:18,000 And it's mostly prevalent in those who are younger than compared to those who are older. 36 00:04:18,000 --> 00:04:23,000 And we also talk about different risk factors in epidemiology. 37 00:04:23,000 --> 00:04:29,000 So there are certain factors that make you more likely or more susceptible to certain diseases. 38 00:04:29,000 --> 00:04:35,000 With tuberculosis, it is the overcrowding. 39 00:04:35,000 --> 00:04:46,000 If you're with a lot of people in a household, especially if one person is infected, you can likely be infected yourself. 40 00:04:46,000 --> 00:04:53,000 So we talk about housing, security or housing insecurity as a risk factor. 41 00:04:53,000 --> 00:05:05,000 Moving on, during Glasgow, I also had to take this really, really challenging course called mathematical statistics and inference. 42 00:05:05,000 --> 00:05:10,000 And that course was not only hard. 43 00:05:10,000 --> 00:05:16,000 It's just like it was hard for me to understand how to apply what I was learning. 44 00:05:16,000 --> 00:05:20,000 It was all theory. 45 00:05:20,000 --> 00:05:33,000 And for my thesis, I mostly just explained into the tea about one statistical method that I employed. 46 00:05:33,000 --> 00:05:39,000 So for my thesis, the writing itself was also hard because I was never born to be a writer. 47 00:05:39,000 --> 00:05:42,000 I just wanted to be a writer. 48 00:05:42,000 --> 00:05:51,000 But yeah, it was just a, it was also a long process. I thought I would have been done sooner. 49 00:05:51,000 --> 00:05:58,000 And I also felt like certain students got priority over others. 50 00:05:58,000 --> 00:06:11,000 I had two supervisors and I could tell that one of my supervisors wasn't as like invested in my thesis compared to some of his other students. 51 00:06:11,000 --> 00:06:21,000 So I no longer a grad student. I don't see myself doing a PhD just because I wouldn't say that I absolutely love research. 52 00:06:21,000 --> 00:06:28,000 I think that with research, it provides self-aq good learning opportunity. 53 00:06:28,000 --> 00:06:31,000 However, I don't think it's for everyone. 54 00:06:31,000 --> 00:06:38,000 And to work in industry, specifically in public how you don't necessarily need to have a PhD. 55 00:06:38,000 --> 00:06:45,000 If you ever do want to go for a PhD though, it takes many, many years. 56 00:06:45,000 --> 00:06:49,000 It could take like six years to do it. 57 00:06:49,000 --> 00:06:55,000 So based on where I'm right now, I am employed. 58 00:06:55,000 --> 00:07:07,000 However, even I just started, so as someone who's just entered the workforce, I think there are things that you might expect for your students. 59 00:07:07,000 --> 00:07:11,000 And that's why I'm trying to get back for your shirt and put job. 60 00:07:11,000 --> 00:07:18,000 You have your dream job in mind, but I'd say you still have to let climb a ladder to get there. 61 00:07:18,000 --> 00:07:23,000 So with my job right now, it includes administrative tasks. 62 00:07:23,000 --> 00:07:28,000 And I didn't know that's what I was basically sending up for. 63 00:07:28,000 --> 00:07:35,000 So I'm hoping that I'll be able to actually do like data analysis with my job. 64 00:07:35,000 --> 00:07:37,000 I actually have not done that. 65 00:07:37,000 --> 00:07:42,000 I can do so many things like something like data entry. 66 00:07:42,000 --> 00:07:49,000 Because right now, it's just a lot of, once again, administrative tasks. 67 00:07:49,000 --> 00:07:53,000 Now I talk to a recent graduate. 68 00:07:53,000 --> 00:07:58,000 One hours in my first year of masters and she was from Nigeria. 69 00:07:58,000 --> 00:08:04,000 And she finished her masters in bio stats in the year 2016. 70 00:08:04,000 --> 00:08:12,000 And she was working for SHA, so that's the Saskatchewan Health Authority. 71 00:08:12,000 --> 00:08:16,000 And it's funny because she said that that's what happened to her. 72 00:08:16,000 --> 00:08:24,000 She, during the four years of working for that organization, she mostly did administrative tasks. 73 00:08:24,000 --> 00:08:27,000 She only did one bio statistics project. 74 00:08:27,000 --> 00:08:30,000 Which I thought was very interesting. 75 00:08:30,000 --> 00:08:36,000 So for me, I kind of felt like working for SHA wouldn't be for me. 76 00:08:36,000 --> 00:08:48,000 But even though I kind of tried to avoid and this type of job, I kind of still ended up in that position, 77 00:08:48,000 --> 00:08:51,000 but just with the different organization. 78 00:08:51,000 --> 00:08:56,000 So this is like my honest opinion of grad school. 79 00:08:56,000 --> 00:09:01,000 I want to go more into detail about my experience. 80 00:09:01,000 --> 00:09:09,000 I want to tell you guys about how to get student jobs, how to communicate with professors. 81 00:09:09,000 --> 00:09:12,000 Because all of these things nobody really tells you. 82 00:09:12,000 --> 00:09:16,000 I also want to be able to talk about funding. 83 00:09:16,000 --> 00:09:24,000 For example, for me, when I first enrolled in my program, I did not get funding right away. 84 00:09:24,000 --> 00:09:30,000 And some masters programs, depending on who your supervisor is, 85 00:09:30,000 --> 00:09:35,000 do you may get funding or if you do, it's still not that much. 86 00:09:35,000 --> 00:09:38,000 And the same thing goes for PhD. 87 00:09:38,000 --> 00:09:43,000 Like you could be paid like maybe 30k a year. 88 00:09:43,000 --> 00:09:46,000 And nowadays that's just not a lot. 89 00:09:46,000 --> 00:09:50,000 Students are trying to advocate for change within the departments, 90 00:09:50,000 --> 00:09:55,000 but academia itself is so slow to change. 91 00:09:55,000 --> 00:10:00,000 Right now for me, my focus is at high pay not my student loans. 92 00:10:00,000 --> 00:10:12,000 So I also want to be very open and discuss whether it's essential to pursue a master's degree. 93 00:10:12,000 --> 00:10:21,000 And then I also want to talk more about career aspirations because, well, I was in school. 94 00:10:21,000 --> 00:10:23,000 I wanted to do so many things. 95 00:10:23,000 --> 00:10:27,000 I still want to do a lot of things, but ideally, for some time, 96 00:10:27,000 --> 00:10:32,000 I wanted to work as an epidemiologist, especially after taking that epic course. 97 00:10:32,000 --> 00:10:36,000 And I wanted to work for like, organization psych WH. 98 00:10:36,000 --> 00:10:41,000 Oh, I thought about working for doctors without borders. 99 00:10:41,000 --> 00:10:48,000 And of course, I can go into detail about the different career opportunities within public health. 100 00:10:48,000 --> 00:10:55,000 Because there's just so many, it's not just working as an epidemiologist or a researcher. 101 00:10:55,000 --> 00:10:59,000 And there's so many avenues where you can work. 102 00:10:59,000 --> 00:11:05,000 You don't have to work at a university, although some students do get hired by their supervisors. 103 00:11:05,000 --> 00:11:06,000 I didn't. 104 00:11:06,000 --> 00:11:09,000 I was able to find a job elsewhere. 105 00:11:09,000 --> 00:11:17,000 And I guess that leads me with also talking about job hunting because I thought with public health, 106 00:11:17,000 --> 00:11:19,000 there would be a lot of jobs. 107 00:11:19,000 --> 00:11:20,000 And there are. 108 00:11:20,000 --> 00:11:31,000 But the key thing is I didn't know that you had to have like three to five years of experience in order to get some your dream role. 109 00:11:31,000 --> 00:11:38,000 So I think I want to talk about navigating the job market because I was very discouraged for some time. 110 00:11:38,000 --> 00:11:47,000 And I also I didn't really do a lot of whole ton of interviews or not from the lack of trying. 111 00:11:47,000 --> 00:11:53,000 So I want to thank you so much for stopping by to my podcast. 112 00:11:53,000 --> 00:12:00,000 If you want me to discuss anything specific due to public health or even something like self improvement, 113 00:12:00,000 --> 00:12:07,000 go to Google citizenship, feel free to leave your comments and suggestions and suggestions. 114 00:12:07,000 --> 00:12:10,000 And I hope you tune in next time. 115 00:12:10,000 --> 00:12:11,000 See ya. 116 00:12:11,000 --> 00:12:27,000 This is Odette once again and I'm signing off.