Meeting Title: Awaish <> Abigail Tech Screen Date: 2025-06-12 Meeting participants: Awaish Kumar, Abigail Zhao
WEBVTT
1 00:00:31.260 ⇒ 00:00:32.270 Abigail Zhao: Hi!
2 00:00:32.630 ⇒ 00:00:33.540 Abigail Zhao: Hello!
3 00:00:34.834 ⇒ 00:00:35.800 Awaish Kumar: Hello!
4 00:00:35.980 ⇒ 00:00:37.010 Awaish Kumar: How are you?
5 00:00:37.420 ⇒ 00:00:38.610 Abigail Zhao: I’m good. How are you?
6 00:00:39.340 ⇒ 00:00:43.680 Awaish Kumar: I’m good as well, so how do you pronounce your name?
7 00:00:44.720 ⇒ 00:00:46.310 Abigail Zhao: Abigail Abigail Zao.
8 00:00:46.930 ⇒ 00:00:48.170 Awaish Kumar: I’ve been okay.
9 00:00:49.168 ⇒ 00:00:56.069 Awaish Kumar: Okay, so I’ll start off with the introducing like little bit about
10 00:00:56.240 ⇒ 00:00:59.510 Awaish Kumar: brain force and what we are going to discuss
11 00:01:01.090 ⇒ 00:01:09.269 Awaish Kumar: in this meeting. And yeah, and then, yeah, we can go ahead with your introduction, and we can start from there.
12 00:01:10.156 ⇒ 00:01:16.619 Awaish Kumar: So my my name is Avesh Kumar. And I’ve been working as a data engineer for
13 00:01:17.230 ⇒ 00:01:18.500 Awaish Kumar: for some time.
14 00:01:19.125 ⇒ 00:01:27.840 Awaish Kumar: In brain forge. And brain forge is is basically data and AI consulting company.
15 00:01:28.400 ⇒ 00:01:30.810 Awaish Kumar: So we were, we provide services.
16 00:01:31.565 ⇒ 00:01:38.659 Awaish Kumar: data data services like including data, engineering analytics and analyst work
17 00:01:38.950 ⇒ 00:01:42.650 Awaish Kumar: and the AI services like billing agents for
18 00:01:43.727 ⇒ 00:01:51.250 Awaish Kumar: for the clients, or building even complete AI platform for them. And then
19 00:01:52.580 ⇒ 00:01:58.249 Awaish Kumar: we have some internal project as well where we utilize AI and data.
20 00:01:59.026 ⇒ 00:02:05.723 Awaish Kumar: So that’s what basically Brentforge do. And Brentforge is basically kind of a company with
21 00:02:06.784 ⇒ 00:02:17.969 Awaish Kumar: with a lot of utilization of AI. So we have kind of use AI in every aspect of our work to speed up the processes
22 00:02:18.250 ⇒ 00:02:27.290 Awaish Kumar: and optimize our time and and efficiently perform some of the actions we do. So, even if
23 00:02:27.830 ⇒ 00:02:37.289 Awaish Kumar: by using AI, or even without using AI like, there are a lot of things which can be optimized in a company. And so we we try to
24 00:02:38.217 ⇒ 00:02:44.559 Awaish Kumar: to be to to be be a AI driven and data driven company.
25 00:02:45.515 ⇒ 00:02:51.610 Awaish Kumar: So after, like, yeah, that’s it for for the brain force.
26 00:02:52.309 ⇒ 00:03:04.490 Awaish Kumar: What we are going to discuss about this in this meeting is, we are like, 1st of all, we I get to like. We get to know you about your background experiences and
27 00:03:05.555 ⇒ 00:03:16.580 Awaish Kumar: you what you have been working, what kind of skill set you puzzes, and then we maybe deep, drive in in in in one of the projects to explore more on
28 00:03:16.690 ⇒ 00:03:20.500 Awaish Kumar: like how you worked on on a project like
29 00:03:21.090 ⇒ 00:03:25.779 Awaish Kumar: and in in like, we, we discuss that in in detail.
30 00:03:26.812 ⇒ 00:03:30.350 Awaish Kumar: So to to get the overview of what
31 00:03:30.891 ⇒ 00:03:34.270 Awaish Kumar: like understanding of what kind of things we have been working on.
32 00:03:36.650 ⇒ 00:03:41.189 Awaish Kumar: So yeah, like, now, you can go ahead with introducing yourself and.
33 00:03:42.310 ⇒ 00:04:04.870 Abigail Zhao: Yeah, I’m Abigail. I’m a rising senior majoring and applied in computational mathematics at Usc. Over the past few years. I guess I’ve developed a pretty strong interest in data science. I plan on doing a progressive degree program at Usc, as well to obtain a master’s in applied data science. Later on
34 00:04:05.183 ⇒ 00:04:23.359 Abigail Zhao: I’ve gained a bunch of experience through coursework and like statistics and programming, and just like my math classes as well. And I’ve also worked on like a few data driven academic projects that we can go into a little. I interned at Brainforge around the end of summer last year for around like 2 months or so.
35 00:04:23.360 ⇒ 00:04:40.840 Abigail Zhao: and work mainly just like on the sales side. But I’m really intrigued as to how like the company has grown since then, cause I haven’t like kept up with it since then, and would definitely be interested in taking more of a look into like the data engineering and like the data, analysis, side, so yeah.
36 00:04:42.588 ⇒ 00:04:47.360 Awaish Kumar: So you do. You have done your like undergrad or master’s recently.
37 00:04:47.693 ⇒ 00:04:51.029 Abigail Zhao: I’m about to go into my senior year of undergrad.
38 00:04:52.460 ⇒ 00:04:53.180 Awaish Kumar: Okay.
39 00:04:53.430 ⇒ 00:04:54.000 Abigail Zhao: Yep.
40 00:04:55.883 ⇒ 00:05:02.460 Awaish Kumar: Let’s see, okay? And so like.
41 00:05:03.550 ⇒ 00:05:10.240 Awaish Kumar: okay, so like, what kind of in terms of if we talk about data
42 00:05:10.734 ⇒ 00:05:14.100 Awaish Kumar: like, what kind of projects you have worked on.
43 00:05:15.690 ⇒ 00:05:25.479 Abigail Zhao: I can say, the most recent one was for one of my classes where it was basically just forecasting, like movie.
44 00:05:26.139 ⇒ 00:05:34.929 Abigail Zhao: like movie like insights in the entertainment landscape. So we combined a bunch of information from movies from like 2,000 to
45 00:05:35.060 ⇒ 00:05:47.400 Abigail Zhao: 2020 and conducted like revenue analysis did some machine learning prediction, and then also did like subtitles, sentiment analysis, which basically was just a forecast movie performances
46 00:05:47.610 ⇒ 00:05:55.929 Abigail Zhao: in like the entertainment landscape. So for that one we used like the sentiment analysis model, and also machine learning models
47 00:05:56.080 ⇒ 00:05:58.230 Abigail Zhao: to like analyze linear regression on that.
48 00:06:00.020 ⇒ 00:06:03.689 Awaish Kumar: Okay. But like, I would like to understand what kind of
49 00:06:04.788 ⇒ 00:06:07.379 Awaish Kumar: tools or programming language, or
50 00:06:08.358 ⇒ 00:06:12.929 Awaish Kumar: to gather the data and then transform or whatever.
51 00:06:13.780 ⇒ 00:06:32.500 Abigail Zhao: So for this one, we got the data sets on. We found the data sets online, and then for the machine learning models that we used. It was, I believe, xgboost. We did random forest and shap analysis and as well, just like basic linear regression. And then we use that to
52 00:06:32.630 ⇒ 00:07:01.080 Abigail Zhao: basically just conduct a revenue analysis to examine, like the success of the movie, comparing it to like the gained revenue. And then for the subtitle sentiment analysis. We used the built in word tokenization, I believe, and use that to analyze the sentiment of movie subtitles like across the length of the movie. And we also found, like the subtitle data online as well.
53 00:07:02.290 ⇒ 00:07:08.749 Awaish Kumar: And so like, let’s get back to the get like gathering the data, you have found it
54 00:07:09.340 ⇒ 00:07:13.070 Awaish Kumar: online, like on Google, like Kaggle or.
55 00:07:13.340 ⇒ 00:07:19.569 Abigail Zhao: Yeah, it was found on Kaggle. And then we yeah. And then we also like cleaned up the data, too. With that.
56 00:07:19.690 ⇒ 00:07:21.729 Awaish Kumar: Okay, so how did you clean up like.
57 00:07:22.390 ⇒ 00:07:26.979 Awaish Kumar: what kind of programming language did you choose? Or platforms, or id.
58 00:07:29.505 ⇒ 00:07:31.080 Abigail Zhao: for the.
59 00:07:31.590 ⇒ 00:07:49.049 Abigail Zhao: I worked I worked primarily a lot on the sub like the sentiment, analysis, part so for that one, it was like cleaning out the subtitles. For that one. It was just removing a lot of unnecessary like information. So for that it was honestly just a lot of like
60 00:07:49.320 ⇒ 00:07:59.800 Abigail Zhao: coding through it. We didn’t really use any models to clean through it, but more just like like removed anywhere, like unnecessary words or symbols that would have came out throughout the.
61 00:07:59.800 ⇒ 00:08:03.729 Awaish Kumar: Yeah. But like, what? How? Why, what languages did you choose like.
62 00:08:03.730 ⇒ 00:08:05.039 Abigail Zhao: Oh! Oh! And python.
63 00:08:06.140 ⇒ 00:08:09.379 Awaish Kumar: And like you, drew some Id or something.
64 00:08:10.430 ⇒ 00:08:14.810 Awaish Kumar: Development platform is Vs code or something.
65 00:08:15.130 ⇒ 00:08:21.139 Abigail Zhao: Oh, oh, oh, this was all done through like Google Collab, because that’s what the professor wanted from the
66 00:08:21.440 ⇒ 00:08:24.039 Awaish Kumar: Okay, Google Collab, like notebooks.
67 00:08:24.865 ⇒ 00:08:25.190 Abigail Zhao: Yeah.
68 00:08:26.460 ⇒ 00:08:32.660 Awaish Kumar: Okay, so it was kind of you, you, okay? So it’s just kind of
69 00:08:33.080 ⇒ 00:08:40.729 Awaish Kumar: it’s a project where you we were using notebooks to read the data and use some python libraries.
70 00:08:41.090 ⇒ 00:08:47.780 Awaish Kumar: To train some sentiment, analyst analyst models and then save the output
71 00:08:48.691 ⇒ 00:08:50.599 Awaish Kumar: in some files, or like
72 00:08:50.950 ⇒ 00:08:55.489 Awaish Kumar: sentiment of the movie like, how do you just show it some somehow? Right?
73 00:08:55.620 ⇒ 00:08:59.309 Awaish Kumar: Did you had any build, some charts or something?
74 00:08:59.590 ⇒ 00:09:02.032 Abigail Zhao: Yeah, yeah, we we yeah, we made
75 00:09:02.830 ⇒ 00:09:08.299 Abigail Zhao: we like graphed out charts to like graph, the like sentiment analysis, like over the span of the movie.
76 00:09:10.410 ⇒ 00:09:11.080 Awaish Kumar: Okay.
77 00:09:11.966 ⇒ 00:09:16.670 Awaish Kumar: So how did you like? What kind of libraries did you utilized for that.
78 00:09:17.015 ⇒ 00:09:27.394 Abigail Zhao: This one we use the nltk toolkit, and then also like tokenize the words into chunks of like 500. And then, like that library was able to analyze.
79 00:09:28.150 ⇒ 00:09:29.620 Awaish Kumar: No, but I mean, like, about
80 00:09:29.840 ⇒ 00:09:34.909 Awaish Kumar: what did you for libraries? Did you choose to build the charts.
81 00:09:35.980 ⇒ 00:09:37.300 Abigail Zhao: Oh, that was
82 00:09:38.390 ⇒ 00:09:42.439 Abigail Zhao: Oh, that was just like pandas, and like numpy like along the.
83 00:09:43.470 ⇒ 00:09:50.320 Awaish Kumar: Yeah. But pandas don’t help with them building the charts like
84 00:09:50.650 ⇒ 00:09:53.649 Awaish Kumar: in the pandas. You can read the data, transform it.
85 00:09:53.650 ⇒ 00:09:54.210 Abigail Zhao: Hmm.
86 00:09:54.350 ⇒ 00:09:58.159 Awaish Kumar: But but visualize it like it’s some shirt.
87 00:09:58.340 ⇒ 00:10:03.889 Awaish Kumar: some line chart or bar chart, or something you have to use some other libraries.
88 00:10:06.630 ⇒ 00:10:11.609 Awaish Kumar: Okay? So okay, so from
89 00:10:14.250 ⇒ 00:10:17.929 Awaish Kumar: How like, how do you rate yourself like in python?
90 00:10:18.310 ⇒ 00:10:19.849 Abigail Zhao: And inescaled.
91 00:10:20.690 ⇒ 00:10:23.770 Abigail Zhao: Yeah, so, since most
92 00:10:24.080 ⇒ 00:10:31.300 Abigail Zhao: since my degree right now is more like focused in math, most of my programming classes, have been
93 00:10:31.450 ⇒ 00:10:37.595 Abigail Zhao: more centered around python, so I would say, my python is probably better than my sequel.
94 00:10:38.640 ⇒ 00:10:40.590 Abigail Zhao: if you were to have me like
95 00:10:40.710 ⇒ 00:10:43.939 Abigail Zhao: rate it on a scale of like 0 to 10.
96 00:10:44.280 ⇒ 00:10:46.710 Abigail Zhao: Oh, sorry. Yeah.
97 00:10:46.960 ⇒ 00:10:49.949 Awaish Kumar: On a scale of 10, how to reiterate yourself.
98 00:10:50.530 ⇒ 00:11:01.439 Abigail Zhao: Yeah, I would say, in terms of sequel, I would definitely am interested in gaining more experience on that side. I would say like, I only know the basics of it currently. So I’d give myself like
99 00:11:01.940 ⇒ 00:11:14.540 Abigail Zhao: like a 3 out of like 10 right now and then. Python, I would say, I know a lot better. Definitely something. I feel like I could explore more as well. But I think I’d give myself like a 5 or 6 on that, because I think.
100 00:11:15.070 ⇒ 00:11:19.729 Awaish Kumar: Okay, so like your experience, your like bachelors, is it more
101 00:11:20.320 ⇒ 00:11:24.310 Awaish Kumar: like, relate, like, what was the subject? I just forgot.
102 00:11:24.693 ⇒ 00:11:28.529 Abigail Zhao: Oh, applied in computational mathematics is the title of it.
103 00:11:28.530 ⇒ 00:11:34.050 Awaish Kumar: Okay, but complex mathematics. And what like do you study programming?
104 00:11:34.854 ⇒ 00:11:41.620 Abigail Zhao: Yeah, yeah, there are like, there are definitely like programming courses, that are like part of my like degree program as, well.
105 00:11:42.300 ⇒ 00:11:43.789 Awaish Kumar: Have you studied any.
106 00:11:46.130 ⇒ 00:11:46.600 Abigail Zhao: Oh, yeah.
107 00:11:46.600 ⇒ 00:11:49.949 Awaish Kumar: Need to be close. Have you taken already taken some courses.
108 00:11:50.150 ⇒ 00:11:51.210 Abigail Zhao: Yes, yes.
109 00:11:52.260 ⇒ 00:11:55.869 Awaish Kumar: Okay, like, can you just share the title.
110 00:11:56.340 ⇒ 00:11:57.440 Abigail Zhao: Oh, yeah, yeah.
111 00:11:58.217 ⇒ 00:11:59.860 Abigail Zhao: I took like
112 00:12:00.230 ⇒ 00:12:04.167 Abigail Zhao: I took like the basic classes like introduction to python as well. I took
113 00:12:05.371 ⇒ 00:12:24.709 Abigail Zhao: Oh, object oriented programming was another one. I did, but that class was mainly in Java, and then the class I took last semester was also like a business data, data science class. It’s like a really long title. But in that class was where I learned a lot of like python oriented.
114 00:12:24.710 ⇒ 00:12:39.640 Awaish Kumar: So if I ask few things regarding object oriented programming, like, what is the the polymorphism in object-oriented programming?
115 00:12:42.680 ⇒ 00:12:44.460 Abigail Zhao: Sorry! Oh, sorry! And I don’t think.
116 00:12:44.460 ⇒ 00:12:47.390 Awaish Kumar: Like? Do you know what is polymorphism?
117 00:12:48.290 ⇒ 00:12:51.199 Abigail Zhao: Yeah. Oh, yeah, I learned about that in my class.
118 00:12:52.820 ⇒ 00:12:53.490 Abigail Zhao: But yeah.
119 00:12:53.490 ⇒ 00:12:59.670 Awaish Kumar: Okay, so like, do you like, what are classes? Do you remember that.
120 00:13:06.060 ⇒ 00:13:09.060 Abigail Zhao: like in the object oriented programming.
121 00:13:09.200 ⇒ 00:13:15.080 Awaish Kumar: Class is a basic building block.
122 00:13:16.660 ⇒ 00:13:21.829 Awaish Kumar: So do do you know anything about classes?
123 00:13:22.510 ⇒ 00:13:26.259 Abigail Zhao: Oh, yeah, if we like.
124 00:13:26.630 ⇒ 00:13:29.360 Abigail Zhao: Oh, did you want me to like go.
125 00:13:29.360 ⇒ 00:13:32.390 Awaish Kumar: Yeah, explain, like, what what a class is, why we use it.
126 00:13:33.170 ⇒ 00:13:42.327 Abigail Zhao: Oh, from what I’ve learned, this was also like, yeah, this is also like a year ago. So from what I’ve learned, classes are basically
127 00:13:42.980 ⇒ 00:13:44.269 Abigail Zhao: like the different.
128 00:13:45.430 ⇒ 00:13:59.969 Abigail Zhao: How like, how I think of it is like you can have like a bit like a parent class. And then anything like below. It shares like subclasses, and then all the subclasses like, inherit the properties of the parent class, and like, so on and so forth, and then.
129 00:13:59.970 ⇒ 00:14:06.509 Awaish Kumar: But what are classes you’re correct with? We have classes, we have best classes, we have enhance.
130 00:14:06.680 ⇒ 00:14:08.199 Awaish Kumar: But what are classes.
131 00:14:10.770 ⇒ 00:14:18.400 Abigail Zhao: Oh, classes. I just like, I think of them as just like the found like
132 00:14:20.030 ⇒ 00:14:25.829 Abigail Zhao: I i’m, like, I don’t know how to describe. I like am blanking on how to describe it right now, but it’s just like the.
133 00:14:26.440 ⇒ 00:14:27.710 Awaish Kumar: I’ll give an example.
134 00:14:27.940 ⇒ 00:14:29.770 Abigail Zhao: Block. If that makes sense.
135 00:14:30.950 ⇒ 00:14:36.928 Awaish Kumar: Okay, you can can give an example. Like class is something like which we we say like,
136 00:14:37.440 ⇒ 00:14:41.490 Awaish Kumar: an object. For example, right? Allow me.
137 00:14:42.547 ⇒ 00:14:45.569 Awaish Kumar: A room can be a class right
138 00:14:46.442 ⇒ 00:14:55.719 Awaish Kumar: and and size like length, width. Height can be dismember variables like, and these are functions like
139 00:14:56.480 ⇒ 00:15:05.820 Awaish Kumar: like you can get the height or set the height of the room room right. This can be the the methods of the class, so.
140 00:15:07.070 ⇒ 00:15:12.979 Awaish Kumar: So basically class and encapsulates the properties of a single object
141 00:15:13.560 ⇒ 00:15:17.120 Awaish Kumar: in one at one place. So
142 00:15:17.520 ⇒ 00:15:22.289 Awaish Kumar: that’s what what a class is, and what? What a class is used for?
143 00:15:22.410 ⇒ 00:15:29.380 Awaish Kumar: Okay? So I mean, okay, like, I.
144 00:15:30.240 ⇒ 00:15:38.270 Awaish Kumar: No, no like. Ha! Have you worked on SQL. Like you already mentioned that you have worked very little with with SQL.
145 00:15:38.270 ⇒ 00:15:38.800 Abigail Zhao: Hmm.
146 00:15:40.520 ⇒ 00:15:45.010 Awaish Kumar: So like, okay,
147 00:15:46.460 ⇒ 00:15:55.759 Awaish Kumar: like with with basics. What do you mean like? Have you used it for some building some projects? Or do you learn it in a subject or.
148 00:15:56.090 ⇒ 00:15:59.369 Abigail Zhao: Yeah, mainly just like learned it through my coursework.
149 00:16:00.190 ⇒ 00:16:00.850 Awaish Kumar: Alright.
150 00:16:02.670 ⇒ 00:16:06.430 Awaish Kumar: Okay. And can you write a meeting
151 00:16:07.850 ⇒ 00:16:11.140 Awaish Kumar: like, do you know how, if I want to like if we have a table.
152 00:16:12.410 ⇒ 00:16:14.379 Awaish Kumar: And then I want to read something from that.
153 00:16:15.220 ⇒ 00:16:17.920 Awaish Kumar: How would you like that, Ury?
154 00:16:18.450 ⇒ 00:16:22.239 Awaish Kumar: I I just want to read all the columns from some table, that’s all.
155 00:16:23.545 ⇒ 00:16:24.210 Abigail Zhao: Oh!
156 00:16:24.210 ⇒ 00:16:25.190 Awaish Kumar: I would just be like.
157 00:16:25.330 ⇒ 00:16:28.590 Abigail Zhao: Let oh, sorry!
158 00:16:29.470 ⇒ 00:16:32.369 Awaish Kumar: Very simple. It will be very simple, Kelly.
159 00:16:32.770 ⇒ 00:16:37.560 Awaish Kumar: where you would just want to read something from a table like, Where are we
160 00:16:39.280 ⇒ 00:16:46.550 Awaish Kumar: like? Table can be some. Anything like table name is a just like you want to read all the columns.
161 00:16:46.820 ⇒ 00:16:49.179 Awaish Kumar: So how how will you write it?
162 00:16:50.430 ⇒ 00:16:54.520 Abigail Zhao: It would just be you just like select like Star, and then, like all of it, would.
163 00:16:55.510 ⇒ 00:16:58.540 Awaish Kumar: So let’s start from some teams right?
164 00:16:58.540 ⇒ 00:16:59.550 Abigail Zhao: On the table, yet.
165 00:17:00.170 ⇒ 00:17:00.860 Awaish Kumar: Try it.
166 00:17:01.070 ⇒ 00:17:08.200 Awaish Kumar: Oh, man, okay. And like, let’s talk about your interests like means.
167 00:17:08.380 ⇒ 00:17:14.170 Awaish Kumar: like, like majority of our work, is either with SQL.
168 00:17:15.432 ⇒ 00:17:24.399 Awaish Kumar: Or with icon or like, you know, like, if if you
169 00:17:24.550 ⇒ 00:17:29.840 Awaish Kumar: like. How? Where do you want to work like in this internship. What are your expectation?
170 00:17:30.640 ⇒ 00:17:32.949 Awaish Kumar: So what what we are doing right now.
171 00:17:33.270 ⇒ 00:17:37.509 Awaish Kumar: like we we don’t have like any ml, ml, work right now.
172 00:17:37.620 ⇒ 00:17:46.040 Awaish Kumar: So machine learning stuff. But we do have some AI work which is like buildings or agents or things like that.
173 00:17:47.368 ⇒ 00:17:56.189 Awaish Kumar: Where you have to like. There are some platforms which which are drag and drop, basically like
174 00:17:56.820 ⇒ 00:18:02.830 Awaish Kumar: to build those agents. But then there is, some python coding required for that.
175 00:18:05.507 ⇒ 00:18:15.139 Awaish Kumar: And second part is data, engineering analytics and data analyst work which we basically
176 00:18:17.090 ⇒ 00:18:22.780 Awaish Kumar: like the mailing we do here, which is like, we are going
177 00:18:22.930 ⇒ 00:18:30.070 Awaish Kumar: to read data from some, some, some database data warehouse, some data source, whatever it can be like.
178 00:18:30.370 ⇒ 00:18:41.779 Awaish Kumar: And we move that data somewhere else, like from place A to B and then run some transformations writing SQL, and basically we build the dashboards
179 00:18:42.020 ⇒ 00:18:49.860 Awaish Kumar: and the for the building. The dashboards. We I don’t use like pipe, vital libraries or anything. We
180 00:18:50.050 ⇒ 00:18:51.332 Awaish Kumar: now we use.
181 00:18:52.891 ⇒ 00:19:01.650 Awaish Kumar: some tools like tableau power bi super set like these are open source tools to build the dashboards
182 00:19:01.840 ⇒ 00:19:06.500 Awaish Kumar: so like with the dashboarding, is like also drag and drop.
183 00:19:06.740 ⇒ 00:19:10.110 Awaish Kumar: But but like we’ve understood like
184 00:19:10.470 ⇒ 00:19:18.039 Awaish Kumar: you have to write some, maybe sometimes some SQL, so it’s is a mainly SQL. If if you are interested more into data work
185 00:19:19.160 ⇒ 00:19:23.010 Awaish Kumar: like you have to learn a skill and work with it a lot
186 00:19:23.512 ⇒ 00:19:32.087 Awaish Kumar: and then data engineering work. There could be some work on that. But that that could be like that can be mixed right sometimes it’s
187 00:19:32.640 ⇒ 00:19:43.050 Awaish Kumar: drag and drop like in industry. Normally, we don’t always write everything from scratch, right? We we have lots of tools available for us to. We can utilize them.
188 00:19:43.560 ⇒ 00:19:52.279 Awaish Kumar: So normally, we can. Just maybe we are. We will be using using some tool to move the data. Sometimes it’s not possible. Then we write our own scripts.
189 00:19:52.460 ⇒ 00:20:01.569 Awaish Kumar: how we build our own pipelines, then it’s like writing some python. We have some orchestration tool where we can deploy our model, our pipeline.
190 00:20:02.000 ⇒ 00:20:03.520 Awaish Kumar: and then there is.
191 00:20:04.050 ⇒ 00:20:13.380 Awaish Kumar: yeah, it works. I’m I’m just trying to explain what areas here we we are working on when where you think you can be
192 00:20:13.490 ⇒ 00:20:16.069 Awaish Kumar: better suited? Or what are your interests.
193 00:20:19.610 ⇒ 00:20:20.800 Abigail Zhao: I think
194 00:20:21.160 ⇒ 00:20:33.549 Abigail Zhao: currently, I’m leaning more towards like a data analysis. More like building dashboards and visualizations. And I’m like learning uncovering like insights through that.
195 00:20:34.990 ⇒ 00:20:42.239 Abigail Zhao: I’m like I think I am very interested in, like the like infrastructure side, too, of like how it’s like
196 00:20:42.400 ⇒ 00:20:59.090 Abigail Zhao: like collected, cleaned and stored, and like how you use it to like visualize everything. And I think I would also really like to learn more about like the data pipelines as well. So like using these tools like, I do want to improve in like my sequel as well, and like learning what you guys use personally like.
197 00:20:59.190 ⇒ 00:21:03.640 Abigail Zhao: For, like the data pipelines and the tools that you guys use in that sense.
198 00:21:06.650 ⇒ 00:21:11.230 Awaish Kumar: Okay? And in terms of
199 00:21:15.090 ⇒ 00:21:19.030 Awaish Kumar: like, okay. And apart from the
200 00:21:19.460 ⇒ 00:21:23.200 Awaish Kumar: apart from all of what we have discussed like, do you have any
201 00:21:23.877 ⇒ 00:21:28.570 Awaish Kumar: projects you have worked on right, which which
202 00:21:29.290 ⇒ 00:21:32.789 Awaish Kumar: which you think you’re you’re proud of, or like something
203 00:21:33.070 ⇒ 00:21:36.199 Awaish Kumar: something you have built, and you you would like to share.
204 00:21:37.932 ⇒ 00:21:45.379 Abigail Zhao: In terms of personal projects, I would. I have focused more on like
205 00:21:46.330 ⇒ 00:21:54.890 Abigail Zhao: the math side of my degree right now, so I don’t have like programming personal projects that I like could share right now, if that’s.
206 00:21:55.810 ⇒ 00:21:59.221 Awaish Kumar: Okay, yeah. Like, I understand.
207 00:22:00.700 ⇒ 00:22:03.130 Awaish Kumar: That, and the thing is like I
208 00:22:03.430 ⇒ 00:22:06.860 Awaish Kumar: I would have. I would have had a lot of questions regarding
209 00:22:07.360 ⇒ 00:22:15.370 Awaish Kumar: different technical things, but I don’t know like how like. That’s why I wanted to understand where
210 00:22:16.140 ⇒ 00:22:22.500 Awaish Kumar: your strength lies, because right now I I don’t.
211 00:22:23.120 ⇒ 00:22:25.829 Awaish Kumar: I don’t know like like from.
212 00:22:26.220 ⇒ 00:22:33.260 Awaish Kumar: If if you could share like, even if it’s not programming related like, if you could share what we have worked on, I I could find
213 00:22:33.540 ⇒ 00:22:34.560 Awaish Kumar: and like.
214 00:22:35.030 ⇒ 00:22:43.630 Awaish Kumar: and that like I can I I can still get some insights in in your into your profile, like what? How like, how
215 00:22:44.430 ⇒ 00:22:50.673 Awaish Kumar: you can be better suited in in some of the work we are doing, and then also, like
216 00:22:51.320 ⇒ 00:23:02.389 Awaish Kumar: how quickly you can learn different things like as you mentioned as well, like we use quite a lot if you want to go in into like transformations. And all of these things like
217 00:23:02.770 ⇒ 00:23:06.669 Awaish Kumar: we have like, we have to learn a lot in in terms of
218 00:23:08.640 ⇒ 00:23:16.770 Awaish Kumar: and how it like adoptable you are, how willing you are to learn and how quickly you can learn. And that’s
219 00:23:17.140 ⇒ 00:23:19.309 Awaish Kumar: that’s all we we want to like.
220 00:23:19.940 ⇒ 00:23:24.240 Awaish Kumar: And I want to understand from this conversation.
221 00:23:25.079 ⇒ 00:23:28.351 Abigail Zhao: Yeah, no. I mean, I’m definitely willing to
222 00:23:29.070 ⇒ 00:23:46.799 Abigail Zhao: like, put in more effort into the like areas that i’m lacking in so like any sort of like training, or like any sort of like new like topics that you want me to learn. I’m definitely open to like devoting more time to learning that if my like
223 00:23:46.960 ⇒ 00:23:52.259 Abigail Zhao: current experiences right now don’t necessarily like
224 00:23:53.400 ⇒ 00:23:59.220 Abigail Zhao: demonstrate the like similar level of like the work you guys are doing. So I’m definitely willing to like, put in the work for that.
225 00:23:59.360 ⇒ 00:24:02.030 Abigail Zhao: If that’s like the concern.
226 00:24:05.790 ⇒ 00:24:07.196 Awaish Kumar: Okay, yeah.
227 00:24:08.030 ⇒ 00:24:14.560 Awaish Kumar: So that’s so like, what do you do like apart from
228 00:24:14.940 ⇒ 00:24:18.510 Awaish Kumar: when you have free time, like, apart from your classes.
229 00:24:18.780 ⇒ 00:24:24.620 Awaish Kumar: What kind of activities you do like we in general.
230 00:24:25.070 ⇒ 00:24:49.729 Abigail Zhao: Yeah, I live in like a very. I’m part of like a very musical family. My parents are both piano teachers, so it might like. I also play piano, and I also know how to play cello as well. So I do that a lot in my free time I like playing tennis. And I also really, just sometimes I just also really just like crocheting and like doing like crafts along those lines. That’s what I do in my free time.
231 00:24:50.240 ⇒ 00:24:50.950 Abigail Zhao: Yeah.
232 00:24:52.380 ⇒ 00:24:53.390 Awaish Kumar: 2 graphs.
233 00:24:54.130 ⇒ 00:24:58.480 Abigail Zhao: Oh, crafts! Sorry like that’s like what my hobbies are like outside of class.
234 00:24:59.040 ⇒ 00:25:04.390 Awaish Kumar: Okay, and like, when I is your
235 00:25:05.440 ⇒ 00:25:08.350 Awaish Kumar: summer break, like is going to end.
236 00:25:09.412 ⇒ 00:25:14.040 Abigail Zhao: I start classes back. August 24, th I believe. Yes.
237 00:25:14.510 ⇒ 00:25:19.270 Awaish Kumar: Okay, until 24th of August, you are probably labeled, Okay.
238 00:25:19.270 ⇒ 00:25:19.870 Abigail Zhao: Hmm.
239 00:25:20.530 ⇒ 00:25:26.370 Awaish Kumar: And what else? I
240 00:25:30.220 ⇒ 00:25:36.710 Awaish Kumar: okay? And like, I I don’t know. Like I I’m I’m not in.
241 00:25:37.570 ⇒ 00:25:44.559 Awaish Kumar: I’ve not studied in us, so I don’t know like what you mean. Senior year like you’re in 3rd year or second year.
242 00:25:45.204 ⇒ 00:25:50.720 Abigail Zhao: Yeah, I’m going into my 4th year. But since I’m also going to
243 00:25:51.110 ⇒ 00:25:58.340 Abigail Zhao: do a like progressive degree program, that just means I’m staying an extra year so like I can get my like. Master’s there as well.
244 00:25:59.930 ⇒ 00:26:01.810 Awaish Kumar: You know, like good.
245 00:26:02.250 ⇒ 00:26:07.850 Awaish Kumar: That’s okay. But you like the the bachelor is for 4 years, and then you go in.
246 00:26:07.850 ⇒ 00:26:12.030 Awaish Kumar: Yeah, yes, you’re entering your 4th year right now.
247 00:26:12.030 ⇒ 00:26:12.710 Abigail Zhao: Yes.
248 00:26:13.120 ⇒ 00:26:14.880 Awaish Kumar: After the summer break.
249 00:26:15.180 ⇒ 00:26:17.340 Abigail Zhao: Yeah, after this, break.
250 00:26:17.940 ⇒ 00:26:22.940 Awaish Kumar: Okay. So like you, you have some final year projects or something.
251 00:26:25.022 ⇒ 00:26:28.460 Abigail Zhao: Yeah, I think we’ll do. I’ll like definitely explore
252 00:26:28.944 ⇒ 00:26:33.060 Abigail Zhao: more into that. And I think also, I think once I
253 00:26:33.260 ⇒ 00:26:41.810 Abigail Zhao: start my master’s program. That’s where I will have more like data oriented projects. Because, like, currently, my.
254 00:26:43.300 ⇒ 00:26:45.820 Awaish Kumar: What I meant is like inconvenience.
255 00:26:46.230 ⇒ 00:26:48.310 Awaish Kumar: Where I have studied like we have, like.
256 00:26:48.310 ⇒ 00:26:48.820 Abigail Zhao: Off.
257 00:26:49.336 ⇒ 00:26:59.443 Awaish Kumar: In in our final year we have kind of a project called Final Year Project. So normally, every semester. You have some projects, but this final year project is kind of
258 00:27:00.230 ⇒ 00:27:02.839 Abigail Zhao: Full kind of full course full.
259 00:27:02.840 ⇒ 00:27:03.370 Abigail Zhao: I see.
260 00:27:03.370 ⇒ 00:27:06.330 Awaish Kumar: Here, and when you build kind of a real
261 00:27:06.670 ⇒ 00:27:11.439 Awaish Kumar: real world project, like kind of on a larger scale than we normally do in
262 00:27:11.560 ⇒ 00:27:13.599 Awaish Kumar: in the semester, wise projects.
263 00:27:14.440 ⇒ 00:27:25.280 Abigail Zhao: I see for my oh, sorry for my degree program. I don’t think they like for my school, at least I don’t. They do not have us do that like in our final year.
264 00:27:28.150 ⇒ 00:27:29.425 Awaish Kumar: Okay, so like,
265 00:27:30.615 ⇒ 00:27:40.624 Awaish Kumar: okay, I don’t. I don’t think so. I have any more questions here. And we will definitely let you know
266 00:27:42.450 ⇒ 00:27:49.400 Awaish Kumar: like after the call, like we are going to plan out some
267 00:27:50.395 ⇒ 00:27:57.534 Awaish Kumar: like a plan like internship plan, for how is is going to look like and
268 00:27:59.745 ⇒ 00:28:06.689 Awaish Kumar: what kind of like, how we are going to conduct it, and how we are going to review the
269 00:28:07.557 ⇒ 00:28:11.109 Awaish Kumar: give you the feedback reviews and how
270 00:28:12.035 ⇒ 00:28:21.170 Awaish Kumar: things is going to go. So and yeah, we’ll let you know, like, maybe next week, about like
271 00:28:21.360 ⇒ 00:28:24.343 Awaish Kumar: when we expect you to join, and
272 00:28:24.770 ⇒ 00:28:25.190 Abigail Zhao: Gotcha.
273 00:28:25.190 ⇒ 00:28:30.280 Awaish Kumar: What whatever the decision is, and so we will. I get to know next next week.
274 00:28:30.840 ⇒ 00:28:32.260 Abigail Zhao: Okay. Alright. Thank you.
275 00:28:32.870 ⇒ 00:28:34.100 Awaish Kumar: Okay. Thank you.
276 00:28:35.540 ⇒ 00:28:36.200 Awaish Kumar: Okay.
277 00:28:36.430 ⇒ 00:28:37.310 Awaish Kumar: One.