Meeting Title: Amber Lin’s Personal Meeting Room Date: 2025-05-21 Meeting participants: Annie Yu, Luke Daque, Amber Lin
WEBVTT
1 00:00:09.200 ⇒ 00:00:12.090 Amber Lin: Hi, so I’m gonna share my screen.
2 00:00:12.290 ⇒ 00:00:20.140 Amber Lin: And alright. So essentially, we want these tables.
3 00:00:28.300 ⇒ 00:00:36.830 Amber Lin: And then, Hi, Annie, can you explain our problem right now of.
4 00:00:37.010 ⇒ 00:00:41.530 Amber Lin: maybe we need some models and how? How that’s gonna work.
5 00:00:42.250 ⇒ 00:00:44.603 Amber Lin: And essentially like 2 options, we have.
6 00:00:50.880 ⇒ 00:01:02.450 Annie Yu: Hi, Luke, yeah, we were trying to figure out if it makes sense for you to make models, or or should I just do like all the wrangling in in python. So in in my
7 00:01:02.620 ⇒ 00:01:06.049 Annie Yu: kind of use case that I share. Last week I only used
8 00:01:06.500 ⇒ 00:01:14.099 Annie Yu: 2 data sets. That’s why, it was like doable. But I think right now they want to see
9 00:01:14.920 ⇒ 00:01:17.079 Annie Yu: bunch of more visuals.
10 00:01:19.560 ⇒ 00:01:23.669 Annie Yu: But I also don’t know how, if we
11 00:01:23.880 ⇒ 00:01:35.420 Annie Yu: like, are to have you build models. I also don’t know how those models will will look like, or is it just one model or multiple models? So we are trying to kind of lay out
12 00:01:35.990 ⇒ 00:01:45.040 Annie Yu: what granularities we need for kind of different visuals, and then go from there and then we can decide if
13 00:01:45.980 ⇒ 00:01:51.859 Annie Yu: if it makes sense to have models or or not, because if it’s gonna take
14 00:01:52.380 ⇒ 00:01:56.160 Annie Yu: a long time to build models, and it’s
15 00:01:56.850 ⇒ 00:02:11.760 Annie Yu: like it’s also gonna take me time to do like all the cleaning and wrangling in in Python. But if it’s like the both of them are taking the same time that I can do it in python. So that’s the idea.
16 00:02:13.180 ⇒ 00:02:21.119 Luke Daque: Yeah, I don’t know. It’s spoke.
17 00:02:21.740 ⇒ 00:02:25.910 Luke Daque: Would you know? Like what data we need?
18 00:02:26.470 ⇒ 00:02:28.810 Luke Daque: So you can continue with your like.
19 00:02:28.920 ⇒ 00:02:30.709 Luke Daque: yeah, no, this is that you need.
20 00:02:32.420 ⇒ 00:02:33.140 Annie Yu: Yeah, that’s.
21 00:02:33.140 ⇒ 00:02:34.250 Luke Daque: Which which model.
22 00:02:34.250 ⇒ 00:02:35.410 Annie Yu: Figure out.
23 00:02:39.670 ⇒ 00:02:42.560 Annie Yu: So I remember I was trying to.
24 00:02:43.820 ⇒ 00:02:46.391 Annie Yu: else I don’t even know where.
25 00:02:47.380 ⇒ 00:02:53.340 Annie Yu: yeah, we are trying to list out kind of different granularities that we need to get, and then
26 00:02:53.580 ⇒ 00:02:55.739 Annie Yu: probably get your opinion on
27 00:02:56.800 ⇒ 00:03:07.060 Annie Yu: if all these will just require one model or multiple. So the level one, like total across is easy, right? But then.
28 00:03:07.220 ⇒ 00:03:08.050 Luke Daque: Right.
29 00:03:08.373 ⇒ 00:03:16.460 Annie Yu: If we want to see like one person, I think. No, let’s not say one person. I think everything’s aggregated by team. So if one.
30 00:03:16.460 ⇒ 00:03:16.930 Luke Daque: And.
31 00:03:16.930 ⇒ 00:03:19.000 Annie Yu: For each department
32 00:03:19.840 ⇒ 00:03:32.940 Annie Yu: like, what’s if we wanna show like one department? And both emails and meetings, how will that look like? But also like by day of week, also by our day.
33 00:03:34.520 ⇒ 00:03:43.399 Luke Daque: Right, and this is coming from the meetings right and chat. I guess
34 00:03:47.190 ⇒ 00:03:48.370 Luke Daque: something like that.
35 00:03:55.910 ⇒ 00:03:57.530 Annie Yu: Let me think through this.
36 00:04:01.610 ⇒ 00:04:08.359 Annie Yu: Yeah, I think the right granularity would mean you will have for each department.
37 00:04:09.537 ⇒ 00:04:14.930 Annie Yu: How many emails, how many chats, how many meetings in
38 00:04:15.130 ⇒ 00:04:21.740 Annie Yu: one day, but also another. Granularity is for each department. How many emails chat meetings
39 00:04:21.980 ⇒ 00:04:24.389 Annie Yu: in each hour of day!
40 00:04:26.620 ⇒ 00:04:31.860 Annie Yu: So I think in my mind that would that would mean 2 models. But I don’t know. I don’t really
41 00:04:32.280 ⇒ 00:04:34.949 Annie Yu: no like you would.
42 00:04:36.180 ⇒ 00:04:43.979 Luke Daque: Yeah, we can. I can try it out like, maybe, yeah, do that like
43 00:04:44.190 ⇒ 00:04:50.260 Luke Daque: create a March model? That’s like, maybe join already joining the
44 00:04:52.480 ⇒ 00:04:57.789 Luke Daque: I guess those 4 tables right? The emails, chats, meetings.
45 00:05:00.070 ⇒ 00:05:09.179 Luke Daque: And then so it’s a model. Let me think we’ve broken out.
46 00:05:09.520 ⇒ 00:05:11.330 Annie Yu: By a team
47 00:05:11.860 ⇒ 00:05:20.150 Annie Yu: by emails. But yeah, like, everything’s aggregated by team. But also, I think eventually they also wanna see
48 00:05:20.520 ⇒ 00:05:25.270 Annie Yu: within each team like, it’s not for company.
49 00:05:25.270 ⇒ 00:05:29.640 Annie Yu: Yeah, I think we can just focus on aggregated by team, right?
50 00:05:29.960 ⇒ 00:05:41.420 Amber Lin: They if we don’t even do that because we can add the team data later, or maybe that will add in the task. But right now. The 1st thing we want to tackle is when we want to do these totals right.
51 00:05:41.420 ⇒ 00:05:48.550 Annie Yu: No, yeah. But total total isn’t easy. Total is easy total. I can do it without models.
52 00:05:48.970 ⇒ 00:05:49.530 Amber Lin: Okay.
53 00:05:49.530 ⇒ 00:06:02.020 Annie Yu: Where where we meet. We mean, like how to show department emails, chats, meetings by
54 00:06:02.910 ⇒ 00:06:05.810 Annie Yu: day of week and by hour of day.
55 00:06:11.980 ⇒ 00:06:17.170 Amber Lin: So if you can see my screen here, I think we’re talking about for
56 00:06:17.410 ⇒ 00:06:28.550 Amber Lin: this will be the same when we aggregate team right. So 1st of all, we want to see the day of the week, and then we want to see the hour of the day, and for each one of them. We kinda wanna
57 00:06:28.820 ⇒ 00:06:33.490 Amber Lin: check it for each of these sources right? And then.
58 00:06:34.090 ⇒ 00:06:40.910 Amber Lin: later, we still do the same graph. But just broken down by team.
59 00:06:41.660 ⇒ 00:06:45.238 Amber Lin: Is is that a correct understanding? Annie.
60 00:06:52.000 ⇒ 00:06:53.379 Annie Yu: I’m I’m thinking.
61 00:07:03.615 ⇒ 00:07:05.040 Annie Yu: I think. Yes.
62 00:07:08.420 ⇒ 00:07:09.150 Amber Lin: So.
63 00:07:09.773 ⇒ 00:07:14.869 Annie Yu: Yeah, no. But also, I think we also want to show.
64 00:07:23.060 ⇒ 00:07:25.479 Annie Yu: Yeah, that’s not. That’s not overcomplicated.
65 00:07:26.150 ⇒ 00:07:29.390 Amber Lin: Okay, sounds good.
66 00:07:30.140 ⇒ 00:07:32.110 Amber Lin: So currently.
67 00:07:45.720 ⇒ 00:07:53.449 Amber Lin: So I put like an example. I guess this is by day of the week bar graph by hour of the day kind of like a line graph
68 00:07:55.110 ⇒ 00:07:59.099 Amber Lin: And then, if we can, we can just add all of these together.
69 00:07:59.520 ⇒ 00:08:03.790 Amber Lin: or we could. I don’t. I I don’t know about these 2 like you can decide
70 00:08:03.960 ⇒ 00:08:07.090 Amber Lin: for these, I would say, like optional.
71 00:08:09.640 ⇒ 00:08:11.100 Amber Lin: And then
72 00:08:33.380 ⇒ 00:08:38.089 Amber Lin: just based on based on this, do you need any models.
73 00:08:41.049 ⇒ 00:08:48.359 Annie Yu: If we’re just showing totals across company by day by hour. No, it’s a department.
74 00:08:49.810 ⇒ 00:08:59.890 Amber Lin: Okay, sounds good. So where it needs models is when we need to define it by teams.
75 00:09:00.020 ⇒ 00:09:06.850 Amber Lin: So when we need to like, compare things right if we want to compare teams, want to compare remote versus in person.
76 00:09:12.980 ⇒ 00:09:13.520 Amber Lin: Okay.
77 00:09:13.520 ⇒ 00:09:23.179 Annie Yu: Yeah. And also like, if we want to flag like Monday to Thursday on site and Friday, remote.
78 00:09:23.770 ⇒ 00:09:24.870 Amber Lin: Oh.
79 00:09:35.940 ⇒ 00:10:02.279 Amber Lin: it sounds like that, because we still don’t have all, all, at least like these 2 graphs for all of these sources. I think you can start doing these, and so Luke will also have some time to explore how to do the modeling while you are while we can ship. Something to the client is that a good organ? Is that? Does that sound good? If we do this.
80 00:10:02.430 ⇒ 00:10:09.390 Amber Lin: you can do this 1st without needing models, and Luke, and figure out the models, and once he’s done you can use it.
81 00:10:09.390 ⇒ 00:10:11.260 Annie Yu: Yeah, I think that makes sense.
82 00:10:11.260 ⇒ 00:10:16.210 Amber Lin: Yeah, yeah, this way, like, we won’t be stuck. And you don’t have to do
83 00:10:16.330 ⇒ 00:10:18.719 Amber Lin: modeling that you’re not familiar with.
84 00:10:20.210 ⇒ 00:10:21.610 Luke Daque: Yeah, that makes sense.
85 00:10:21.910 ⇒ 00:10:27.160 Amber Lin: Okay, then, Annie. Then would you be able to define the specific
86 00:10:27.610 ⇒ 00:10:39.539 Amber Lin: items that you needed to be modeled, or, like either of you can work with chat gpt on that, because I really don’t know that much. You will have to tell me how this goes.
87 00:10:41.430 ⇒ 00:10:46.170 Annie Yu: Yeah, I just don’t have time to do that this afternoon.
88 00:10:48.072 ⇒ 00:10:50.400 Amber Lin: Able to like. Just
89 00:10:50.790 ⇒ 00:11:02.319 Amber Lin: talk to Chatgpt, or just send Luke a voice recording of what you think, and we can pass it through AI, and see what we need from that. So see what we can do from that.
90 00:11:03.170 ⇒ 00:11:03.890 Annie Yu: Yeah.
91 00:11:04.290 ⇒ 00:11:11.160 Amber Lin: Okay, yeah. Some thought, any thoughts from you would be great to get us started, and then we can confirm if our understanding is correct.
92 00:11:12.470 ⇒ 00:11:13.000 Annie Yu: Yeah.
93 00:11:13.000 ⇒ 00:11:20.959 Amber Lin: Yeah, and I’ll book a meeting for us tomorrow. I hope I have time tomorrow.
94 00:11:32.480 ⇒ 00:11:37.519 Amber Lin: Okay, any of you have to grow. I think we’re good good for now.
95 00:11:39.370 ⇒ 00:11:44.020 Annie Yu: Okay, yeah. And don’t think I can get this done by Friday.
96 00:11:44.843 ⇒ 00:11:45.650 Annie Yu: Cause I.
97 00:11:45.650 ⇒ 00:11:47.259 Amber Lin: First, st part, right.
98 00:11:47.860 ⇒ 00:11:48.620 Annie Yu: What’s that?
99 00:11:48.830 ⇒ 00:11:51.430 Amber Lin: You mean the 1st part. We won’t be.
100 00:11:51.430 ⇒ 00:11:54.345 Annie Yu: Be able to get that done this week.
101 00:11:55.120 ⇒ 00:12:00.250 Amber Lin: I mean, let’s just let’s just get this 1st part done by Friday. I think we’ll take a bit of time.
102 00:12:00.250 ⇒ 00:12:04.629 Annie Yu: But I think I’m I’m looking at Friday end of day. I I still have some like
103 00:12:04.730 ⇒ 00:12:11.890 Annie Yu: things I have to get done for Eden and I. I just don’t know if I’ll be able to
104 00:12:12.270 ⇒ 00:12:13.509 Annie Yu: come to this.
105 00:12:13.750 ⇒ 00:12:19.000 Amber Lin: I see. What is something that we can get done by the end of week to show the client
106 00:12:20.100 ⇒ 00:12:21.830 Amber Lin: like maybe just.
107 00:12:22.740 ⇒ 00:12:26.960 Amber Lin: All tools by the where graph one. We have all these tools or.
108 00:12:26.960 ⇒ 00:12:35.759 Annie Yu: Well, if you give me like to the end of day Friday, I think I can do that. But I’m just saying like Friday afternoon would be like Friday evening for clients.
109 00:12:35.960 ⇒ 00:12:43.820 Amber Lin: Oh, that’s okay, like, they’re probably online until one am. And like 12 every single day. So.
110 00:12:44.450 ⇒ 00:12:45.260 Annie Yu: Yeah.
111 00:12:48.740 ⇒ 00:12:49.530 Annie Yu: yeah.
112 00:12:50.540 ⇒ 00:12:51.650 Amber Lin: No problem. Okay.
113 00:12:52.130 ⇒ 00:13:03.729 Amber Lin: okay, sounds good. I can add more details into these optional. But I just really think these 2 graphs with all these sources are the main things we want to get done this week.
114 00:13:04.540 ⇒ 00:13:13.579 Annie Yu: Yeah, sounds good. And honestly, I think the client didn’t communicated that very clearly last time. I think they were like.
115 00:13:14.260 ⇒ 00:13:19.809 Annie Yu: They he like went ahead to like stats. That’s where, like, I focus my time on.
116 00:13:19.810 ⇒ 00:13:21.329 Amber Lin: Yeah, he! He!
117 00:13:21.330 ⇒ 00:13:21.670 Amber Lin: Oh.
118 00:13:21.670 ⇒ 00:13:34.020 Amber Lin: they already see everything. But I don’t think they realize that we’re like when he says everything all at once. We kind of want to do everything all at once, and that’s not how things should be done. So.
119 00:13:34.020 ⇒ 00:13:34.340 Annie Yu: Yeah.
120 00:13:34.340 ⇒ 00:13:38.899 Amber Lin: I think this was really helpful today that we just understood like, okay, this is the
121 00:13:39.670 ⇒ 00:13:41.430 Amber Lin: thing we need to get started with.
122 00:13:42.340 ⇒ 00:13:43.010 Annie Yu: Yeah.
123 00:13:43.010 ⇒ 00:13:43.650 Amber Lin: Okay.
124 00:13:43.650 ⇒ 00:13:44.400 Annie Yu: Okay.
125 00:13:44.720 ⇒ 00:13:47.760 Amber Lin: I’ll try and make some tickets. I think I have some more time today.
126 00:13:48.016 ⇒ 00:13:50.319 Annie Yu: Thanks. I I have to. I have to hop.
127 00:13:50.320 ⇒ 00:13:51.450 Amber Lin: Yeah, go ahead. We’re done.
128 00:13:51.450 ⇒ 00:13:52.240 Annie Yu: Bye.
129 00:13:52.620 ⇒ 00:13:53.940 Amber Lin: Alrighty bye.
130 00:13:53.940 ⇒ 00:13:55.750 Luke Daque: Thanks, thanks everyone. Bye-bye.
131 00:13:55.980 ⇒ 00:13:56.275 Amber Lin: Yeah.