Meeting Title: MatterMore x Brainforge | Standup Date: 2025-05-15 Meeting participants: Annie Yu, Luke Daque, Amber Lin
WEBVTT
1 00:01:55.000 ⇒ 00:01:56.440 Amber Lin: Hi.
2 00:01:58.290 ⇒ 00:01:59.260 Annie Yu: Hello!
3 00:01:59.730 ⇒ 00:02:01.539 Amber Lin: I don’t know if they’re joining.
4 00:02:02.890 ⇒ 00:02:03.740 Annie Yu: Hmm.
5 00:02:04.090 ⇒ 00:02:04.800 Amber Lin: Hmm!
6 00:02:05.090 ⇒ 00:02:11.950 Amber Lin: You know. What? Do you have any idea since last yesterday’s meeting?
7 00:02:14.721 ⇒ 00:02:18.299 Annie Yu: Yes, but I also kind of.
8 00:02:18.460 ⇒ 00:02:25.239 Annie Yu: I was looking at the models that look built.
9 00:02:25.890 ⇒ 00:02:29.120 Annie Yu: Think I was thinking from there how to
10 00:02:29.240 ⇒ 00:02:34.880 Annie Yu: make sure. We have the data to show kind of what we want to explore.
11 00:02:34.880 ⇒ 00:02:35.530 Amber Lin: Hmm.
12 00:02:38.090 ⇒ 00:02:41.959 Amber Lin: And I think I remember you said we don’t have granular enough data.
13 00:02:42.170 ⇒ 00:02:47.280 Annie Yu: Yeah. And I, I’m thinking.
14 00:02:50.510 ⇒ 00:02:58.819 Amber Lin: Hi! We’re all here. Not the clients, but we can. We can think about some stuff, and then we can send them updates.
15 00:03:01.690 ⇒ 00:03:11.030 Annie Yu: Yeah, yeah. So I’m just looking at the the comment that I shared with Luke yesterday.
16 00:03:14.620 ⇒ 00:03:19.040 Annie Yu: Yeah, I reviewed the models. Look that you build, and I think
17 00:03:19.380 ⇒ 00:03:26.160 Annie Yu: they are good for like more like higher level summary but I think what they lack
18 00:03:26.680 ⇒ 00:03:33.489 Annie Yu: are the kind of the times of each a message sent
19 00:03:33.600 ⇒ 00:03:37.510 Annie Yu: call as well as like call duration.
20 00:03:37.980 ⇒ 00:03:45.450 Annie Yu: because we want to explore questions like, are people working on Fridays?
21 00:03:45.800 ⇒ 00:03:50.470 Annie Yu: Also, like how many meetings people have per day.
22 00:03:50.580 ⇒ 00:03:56.969 Annie Yu: And how does that trend by hour? So we would need that kind of time. Granularity.
23 00:03:57.280 ⇒ 00:03:58.920 Annie Yu: if that makes sense.
24 00:04:00.320 ⇒ 00:04:06.660 Luke Daque: Yeah, we can definitely do that. I just made that for like, just so we can
25 00:04:07.000 ⇒ 00:04:11.969 Luke Daque: use it as an example. Where, like the joins are working and stuff like that, but
26 00:04:12.160 ⇒ 00:04:23.789 Luke Daque: depending on like what the actual need it is for the models like you mentioned, like, maybe we need the time stamps for the call duration and stuff like that. Then we can definitely
27 00:04:24.040 ⇒ 00:04:26.392 Luke Daque: create that. So yeah,
28 00:04:27.330 ⇒ 00:04:34.679 Luke Daque: do you want us to create that for the synthetic data, though, though or like, would you rather like?
29 00:04:35.250 ⇒ 00:04:37.669 Luke Daque: Wait until we have the actual data.
30 00:04:39.230 ⇒ 00:04:40.330 Annie Yu: Hmm.
31 00:04:41.820 ⇒ 00:04:44.179 Luke Daque: I’m just like asking cause that might.
32 00:04:44.890 ⇒ 00:04:46.239 Luke Daque: I don’t know if we.
33 00:04:46.240 ⇒ 00:04:58.480 Luke Daque: it will take time to create those models. And then it’s just the synthetic data, and it might not even be exactly the same query when we have the actual data and stuff like that. So maybe
34 00:04:59.050 ⇒ 00:05:02.950 Luke Daque: I I’m not sure like, if it’s good use of time or something.
35 00:05:03.640 ⇒ 00:05:09.760 Annie Yu: Yeah, I am. Yeah, I I’m gonna try to answer. But Amber was
36 00:05:10.010 ⇒ 00:05:20.469 Annie Yu: there to talk to the client so you can chime in. I think so. The plan. I’m not sure how how much you know this, Luke, but I think the plan is to
37 00:05:21.363 ⇒ 00:05:28.130 Annie Yu: use those synthetic data to show some analysis, and they even want to see.
38 00:05:29.090 ⇒ 00:05:36.730 Annie Yu: So I think to to to provide some analysis. We probably still need those granularity.
39 00:05:37.526 ⇒ 00:05:57.600 Annie Yu: And they they did share some kind of example. Questions like, I said, like, How do people work remotely versus on site days, and also like how active they are when they work from home. So we have those example questions. And I, I think I’m just taking those questions and see
40 00:05:57.770 ⇒ 00:06:01.230 Annie Yu: if we have those data
41 00:06:01.350 ⇒ 00:06:06.949 Annie Yu: to support. But I think that’s a good question, so I’ll let probably Amber to take that.
42 00:06:09.220 ⇒ 00:06:10.830 Amber Lin: Sorry. What do you need from me?
43 00:06:12.310 ⇒ 00:06:15.670 Annie Yu: No, because right now, if we want to show
44 00:06:15.920 ⇒ 00:06:23.430 Annie Yu: analysis for those kind of answer, like remote versus on-site date activity.
45 00:06:23.580 ⇒ 00:06:27.889 Annie Yu: Also, like how many meetings people have per day and
46 00:06:30.620 ⇒ 00:06:36.330 Annie Yu: so kind of all of the questions that we want to explore are like time based.
47 00:06:37.020 ⇒ 00:06:44.419 Annie Yu: But Luke was asking because to model to do those data modeling, it will take time. And
48 00:06:44.980 ⇒ 00:06:52.580 Annie Yu: he’s wondering if it’s a good use of time if we do those modeling.
49 00:06:54.070 ⇒ 00:06:58.639 Amber Lin: I see, I see. So I guess my question for now is, is our
50 00:06:58.960 ⇒ 00:07:03.860 Amber Lin: current data set enough to produce the graphs?
51 00:07:03.970 ⇒ 00:07:04.940 Amber Lin: They asked.
52 00:07:14.312 ⇒ 00:07:17.450 Luke Daque: Currently, we have, like, I believe, a thousand
53 00:07:17.710 ⇒ 00:07:23.968 Luke Daque: rows for each table, or something like that, based on the synthetic data that we created.
54 00:07:24.880 ⇒ 00:07:32.219 Luke Daque: so probably it would. It’s like enough for a couple of days or something.
55 00:07:34.080 ⇒ 00:07:38.870 Luke Daque: So yeah, we don’t really know yet. I can check play.
56 00:07:38.870 ⇒ 00:07:52.179 Amber Lin: Technically, I think, for most of them. If it covers, say Monday the whole week, right? It covers every day of the week. It already lets us to tweak it and say, Okay.
57 00:07:52.220 ⇒ 00:08:04.040 Amber Lin: Friday, they work from home. Maybe this happens compared to other times of the week. So for the initial step if we have a full week of data that will work. But then
58 00:08:04.040 ⇒ 00:08:24.419 Amber Lin: additional stuff from that they wanted to wanted to see some historic trends. So when it comes to that is when we might need more data of okay, this team switched to remote, maybe during like a month ago. And then this is how their performance changed.
59 00:08:24.450 ⇒ 00:08:45.870 Amber Lin: So I think initially to to have to make sure we have everything to create those graphs. I think we can start from a week and I think, Annie, you’ll be able to create a lot of a lot of graphs from that already. And then after that, we probably to do more detailed analysis and more storytelling? We probably would need
60 00:08:46.910 ⇒ 00:08:49.239 Amber Lin: more than a week of data.
61 00:08:50.430 ⇒ 00:08:51.710 Amber Lin: Does that make sense.
62 00:08:53.870 ⇒ 00:08:58.299 Luke Daque: Yeah, so so we can, basically.
63 00:08:58.490 ⇒ 00:09:03.099 Luke Daque: you still want you, you want us to create the models that would be
64 00:09:03.520 ⇒ 00:09:19.249 Luke Daque: able to answer the questions that they are like asking at the moment, like even using, even when using the synthetic data will be creating models about like call duration over a week and year over year, or something like that.
65 00:09:20.880 ⇒ 00:09:21.890 Amber Lin: I?
66 00:09:23.490 ⇒ 00:09:32.059 Amber Lin: Yeah, I imagine I haven’t. I think the main problem here is that I haven’t took a look at our data sets. And
67 00:09:32.230 ⇒ 00:09:38.309 Amber Lin: I mean, if it’s not, I think ultimately, we need to create those data to answer those questions. So just
68 00:09:39.870 ⇒ 00:09:40.550 Luke Daque: Okay.
69 00:09:43.680 ⇒ 00:09:46.890 Annie Yu: The help. Okay, Amber. I think that’s helpful.
70 00:09:47.620 ⇒ 00:09:54.850 Annie Yu: And we can probably get like a like a simple, very simple
71 00:09:56.410 ⇒ 00:10:12.020 Annie Yu: model. And look, I can, if it will help, I can give you like a mock table, and I’ll try to make it as simplify as possible. So we probably only have, like, maybe, like team I think it’s called department.
72 00:10:12.140 ⇒ 00:10:22.720 Annie Yu: and then month, like days and weeks. So Monday, Tuesday, Friday, and then maybe like average meeting time
73 00:10:23.890 ⇒ 00:10:26.049 Annie Yu: and like average meetings.
74 00:10:26.050 ⇒ 00:10:26.600 Luke Daque: Sure.
75 00:10:27.168 ⇒ 00:10:35.369 Luke Daque: Yeah, that would be great. Like, if we can maybe create I don’t know like a Google sheet, or maybe just update the notion
76 00:10:36.730 ⇒ 00:10:40.390 Luke Daque: ticket with what fields we need
77 00:10:40.620 ⇒ 00:10:44.040 Luke Daque: for the final model so that we can
78 00:10:45.040 ⇒ 00:10:47.550 Luke Daque: try and put it into real or something.
79 00:10:48.824 ⇒ 00:10:52.799 Luke Daque: Cause. Like, yeah, we we don’t have a ticket for that yet, like the ticket.
80 00:10:52.800 ⇒ 00:10:53.260 Amber Lin: Yeah, that.
81 00:10:53.260 ⇒ 00:10:55.530 Luke Daque: That was very generic, right? It was like.
82 00:10:56.440 ⇒ 00:10:58.130 Luke Daque: Crying out, the joins.
83 00:10:58.550 ⇒ 00:11:03.760 Luke Daque: So yeah, if we can maybe get get some details on like what the final.
84 00:11:03.980 ⇒ 00:11:05.739 Luke Daque: The table should be right.
85 00:11:05.740 ⇒ 00:11:06.430 Amber Lin: Yeah, yeah.
86 00:11:06.430 ⇒ 00:11:09.410 Luke Daque: The date range would be cause. I think we
87 00:11:09.560 ⇒ 00:11:21.550 Luke Daque: might need to recreate the synthetic data again, to accommodate the range that we want the date range because we didn’t put that as like the criteria to create the synthetic data we just
88 00:11:21.720 ⇒ 00:11:26.980 Luke Daque: created that a thousand roles without even defining the date ranges.
89 00:11:27.250 ⇒ 00:11:44.069 Amber Lin: I see I see that makes a lot of sense. I I think it sounds like there’s something stuck mostly on my end, and then cause we need to start from the storytelling says everything here. Every every single point of data here is fake. So
90 00:11:44.420 ⇒ 00:11:44.920 Luke Daque: Right.
91 00:11:44.920 ⇒ 00:12:00.790 Amber Lin: Do you want? Do you have some time to meet a bit later? I mean you. You should get started, and then, if you have some time. We can meet also to talk about what charts we need. What do we want to see? And then we can pass it through, chatgpt to like, get get a list of required fields.
92 00:12:02.640 ⇒ 00:12:04.110 Amber Lin: Can you repeat that?
93 00:12:04.494 ⇒ 00:12:11.800 Amber Lin: Do you want? Do you have time to meet later today to define the tables we need, and then
94 00:12:11.930 ⇒ 00:12:17.280 Amber Lin: from that we can ask Gpt to create a field list for Luke.
95 00:12:20.840 ⇒ 00:12:25.040 Annie Yu: So do you mean like a mock table? And you want to
96 00:12:25.870 ⇒ 00:12:29.579 Annie Yu: participate in like making a mock table? Is that it.
97 00:12:30.315 ⇒ 00:12:31.859 Amber Lin: Not on mock tape.
98 00:12:32.070 ⇒ 00:12:33.530 Amber Lin: What do you mean? Table.
99 00:12:34.706 ⇒ 00:12:42.759 Annie Yu: I think I was trying to give look a few data fields. So he has an idea how to get them.
100 00:12:42.760 ⇒ 00:12:46.380 Amber Lin: Yeah, yeah, yeah, that would work, too. I think.
101 00:12:46.770 ⇒ 00:12:51.909 Amber Lin: essentially, we’re working backwards. Right? We’re working from the eventual deck. We want to show them.
102 00:12:52.610 ⇒ 00:13:04.320 Amber Lin: But I do know. Actually, we already know what type of tables we need. I guess I’ll I’ll let you to take those tables that they like they asked us to do, because I think they already
103 00:13:04.500 ⇒ 00:13:07.929 Amber Lin: pretty precisely to find like what graphs they want to see.
104 00:13:09.600 ⇒ 00:13:11.370 Annie Yu: Okay, I see.
105 00:13:12.170 ⇒ 00:13:18.300 Amber Lin: Do you guys have access to both the documents like the initial Google Doc and the slide deck. They sent.
106 00:13:18.710 ⇒ 00:13:20.329 Annie Yu: Yes, I do.
107 00:13:20.330 ⇒ 00:13:21.080 Amber Lin: Okay. Awesome.
108 00:13:21.080 ⇒ 00:13:27.570 Annie Yu: I guess if we want this to move like quickly, we can probably just pick one or 2 graphs
109 00:13:28.896 ⇒ 00:13:31.729 Annie Yu: like a very comprehensive one.
110 00:13:32.202 ⇒ 00:13:44.510 Annie Yu: But yeah, we can you? You can pick one of you do have a preference. I was thinking, maybe like the easiest one would be Monday, Tuesday, Wednesday, Thursday, Friday. So that way.
111 00:13:44.510 ⇒ 00:13:46.109 Amber Lin: Yeah, yeah, lovely.
112 00:13:47.590 ⇒ 00:13:48.400 Amber Lin: Do that. Then.
113 00:13:49.540 ⇒ 00:13:56.066 Amber Lin: Okay. Okay, then, we don’t need to meet about this yet. I will. I will investigate this, and then
114 00:13:56.410 ⇒ 00:14:01.399 Annie Yu: Well, honestly, I think I’ll I’ll try to just do like a very scrappy
115 00:14:03.090 ⇒ 00:14:08.889 Annie Yu: mock table to look, and then add, you ember, too, and you can add your feedback
116 00:14:09.621 ⇒ 00:14:15.980 Annie Yu: cause, you probably know, like, if we want to show something else, or we’re missing some
117 00:14:16.480 ⇒ 00:14:20.299 Annie Yu: some fields. But I think I just tried to do like a very easy
118 00:14:20.630 ⇒ 00:14:24.210 Annie Yu: like a fake table. So Luke has has an idea.
119 00:14:24.210 ⇒ 00:14:33.509 Amber Lin: Okay, that’s great. That’s great. Let’s just start from there this way, like we maybe can have something by end of Friday. That’s awesome. Okay.
120 00:14:33.650 ⇒ 00:14:41.389 Annie Yu: And I’ll I’ll just add a tab in that Google sheet that we’ve been working on. But I’ll share again.
121 00:14:41.950 ⇒ 00:14:46.230 Amber Lin: Yeah, I think I have access to it. It’s a very big Google sheet.
122 00:14:46.360 ⇒ 00:14:51.930 Annie Yu: Yeah. And you said, we said, we can have things by Friday. What does that mean? Do you mean.
123 00:14:52.299 ⇒ 00:14:58.420 Amber Lin: Like this scrappy one. Doesn’t that need to look like anything just like like, get it
124 00:14:58.900 ⇒ 00:15:03.939 Amber Lin: one, maybe just to get one graph working in the collab notebook.
125 00:15:05.140 ⇒ 00:15:06.380 Annie Yu: Okay. Yeah.
126 00:15:06.760 ⇒ 00:15:09.159 Amber Lin: Did we get that set up by any chance.
127 00:15:09.160 ⇒ 00:15:11.039 Annie Yu: Not yet. I didn’t have time.
128 00:15:11.390 ⇒ 00:15:18.209 Amber Lin: Okay, so let’s get us get that set up in bigquery. And then hopefully, we’ll have like a graph by Friday.
129 00:15:18.687 ⇒ 00:15:22.509 Annie Yu: Yeah. And but I think they, if they
130 00:15:22.760 ⇒ 00:15:27.000 Annie Yu: don’t give access to, did they give access to the big query.
131 00:15:27.000 ⇒ 00:15:33.159 Amber Lin: Did. let me check, I think, Trevor responded to that.
132 00:15:33.553 ⇒ 00:15:34.339 Luke Daque: You mean.
133 00:15:34.960 ⇒ 00:15:37.410 Annie Yu: Yeah, yeah, cause I know.
134 00:15:37.410 ⇒ 00:15:38.210 Luke Daque: Bye.
135 00:15:38.210 ⇒ 00:15:39.390 Annie Yu: Yeah, we can access that.
136 00:15:39.390 ⇒ 00:15:43.490 Annie Yu: So never mind, not, we can just create a separate one.
137 00:15:43.490 ⇒ 00:15:44.339 Amber Lin: Oh, okay.
138 00:15:45.179 ⇒ 00:15:53.849 Amber Lin: Trevor said. Oh, okay. I gave you all bigquery resource editor. Perm on the project level. Let me know if that gives you what you need. Can you guys check.
139 00:15:54.920 ⇒ 00:16:02.610 Annie Yu: Okay, and I think it’ll be helpful if we can walk me through. How to make sure I do have that.
140 00:16:02.790 ⇒ 00:16:08.050 Annie Yu: And I’m able to open a collab. Use using bigquery tables.
141 00:16:08.330 ⇒ 00:16:15.420 Amber Lin: Hey? I’ll let you guys use this meeting room. I need to hop to another meeting. But the meeting room should be available.
142 00:16:17.410 ⇒ 00:16:18.060 Luke Daque: Okay.
143 00:16:18.360 ⇒ 00:16:25.539 Amber Lin: Yeah, and and I can call you a bit later. I have a few other calls, and then I should be free.
144 00:16:25.920 ⇒ 00:16:30.341 Annie Yu: Yeah, yeah, I just tried to have my a afternoon free.
145 00:16:30.710 ⇒ 00:16:32.949 Amber Lin: I know I know me, too, I understand.
146 00:16:33.320 ⇒ 00:16:41.209 Amber Lin: Yeah. And, Luke, I talked to Utam, he said. You probably don’t need to be there for the urban times meeting, because it’s mostly gonna be sales. So.
147 00:16:41.210 ⇒ 00:16:41.760 Luke Daque: Okay.
148 00:16:41.810 ⇒ 00:16:42.869 Amber Lin: Yeah, okay.
149 00:16:42.870 ⇒ 00:16:43.680 Luke Daque: Sounds good.
150 00:16:43.905 ⇒ 00:16:44.580 Amber Lin: Let me try.
151 00:16:44.580 ⇒ 00:16:45.040 Annie Yu: Transfer.
152 00:16:45.040 ⇒ 00:16:50.090 Amber Lin: Sure. Thank you. Guys. Let me transfer the meeting room to you.
153 00:16:53.050 ⇒ 00:16:54.069 Amber Lin: Let’s see.
154 00:16:55.920 ⇒ 00:16:57.549 Annie Yu: I think I we got it.
155 00:16:57.550 ⇒ 00:16:59.180 Amber Lin: Yeah, okay, bye, guys.
156 00:16:59.180 ⇒ 00:16:59.900 Annie Yu: Thanks.
157 00:16:59.900 ⇒ 00:17:00.620 Luke Daque: See you.
158 00:17:02.620 ⇒ 00:17:06.459 Luke Daque: Yeah. So let me see, let me share my screen real quick.
159 00:17:06.460 ⇒ 00:17:07.030 Annie Yu: Yeah.
160 00:17:08.170 ⇒ 00:17:09.629 Luke Daque: Can you see my screen.
161 00:17:10.119 ⇒ 00:17:13.329 Annie Yu: Yes, so this.
162 00:17:13.329 ⇒ 00:17:14.289 Luke Daque: So.
163 00:17:14.290 ⇒ 00:17:16.970 Annie Yu: Using the the Brentforge, one.
164 00:17:18.069 ⇒ 00:17:22.769 Luke Daque: I am using the brain for each one here at the moment.
165 00:17:24.649 ⇒ 00:17:30.289 Luke Daque: but doesn’t look like I would have access to the collab.
166 00:17:30.579 ⇒ 00:17:34.599 Luke Daque: It essentially looks like this. If you
167 00:17:35.199 ⇒ 00:17:42.509 Luke Daque: check this out. The exemplary tied project, which is different, like it has this, notebooks part.
168 00:17:43.280 ⇒ 00:17:50.979 Luke Daque: Yeah, it looks like we don’t have access to this one, either. But it’s essentially just like Collab. You’re familiar with Collab, though, right?
169 00:17:50.980 ⇒ 00:17:53.109 Annie Yu: I’ve used it only once.
170 00:17:53.630 ⇒ 00:17:57.419 Annie Yu: But yeah, like, similar to Jupiter. Right?
171 00:17:57.420 ⇒ 00:18:05.240 Luke Daque: Yeah, it’s basically similar to Jupiter, where? Yeah, it’s just like, just like, quickly, right there.
172 00:18:06.180 ⇒ 00:18:12.339 Luke Daque: So it’s it looks like this. But yeah, it would have been great if, like, I can show you a different
173 00:18:13.630 ⇒ 00:18:17.980 Luke Daque: project. Let me open up my personal one.
174 00:18:18.520 ⇒ 00:18:26.599 Annie Yu: Yeah, yeah, I think as long as we can make sure, I can access bigquery models
175 00:18:27.030 ⇒ 00:18:29.850 Annie Yu: from a a collab that works.
176 00:18:31.930 ⇒ 00:18:32.750 Luke Daque: Yeah.
177 00:18:37.720 ⇒ 00:18:47.320 Luke Daque: So here’s how it what it looks like when you have access. But you don’t have access at the moment. But this is a different project like, mine.
178 00:18:47.980 ⇒ 00:18:48.639 Luke Daque: Pardon me.
179 00:18:48.770 ⇒ 00:18:54.900 Luke Daque: So yeah, it’s something like this where you can create.
180 00:18:55.230 ⇒ 00:19:01.430 Luke Daque: Well, let’s just use this template for now. So it even has like examples on like, how to query
181 00:19:03.500 ⇒ 00:19:06.770 Luke Daque: from us, from like one of your data sets here.
182 00:19:06.970 ⇒ 00:19:09.149 Annie Yu: Sorry we can introduce this.
183 00:19:09.650 ⇒ 00:19:14.000 Luke Daque: And then, yeah, and then you can use it as a data frame, basically
184 00:19:14.180 ⇒ 00:19:16.010 Luke Daque: put it into a data frame
185 00:19:16.230 ⇒ 00:19:21.840 Luke Daque: and do whatever you like from like using python or whatever.
186 00:19:23.040 ⇒ 00:19:25.490 Luke Daque: So, yeah, something like this.
187 00:19:25.890 ⇒ 00:19:26.650 Annie Yu: Is co-OP.
188 00:19:26.650 ⇒ 00:19:27.030 Luke Daque: Want to.
189 00:19:27.050 ⇒ 00:19:32.499 Annie Yu: Python only? Or is it like 8 bricks? You can switch languages.
190 00:19:32.500 ⇒ 00:19:35.609 Luke Daque: I think we can assist with additional languages. I’m not
191 00:19:37.010 ⇒ 00:19:40.780 Luke Daque: very familiar, though, but I think you can.
192 00:19:41.050 ⇒ 00:19:44.349 Annie Yu: Oh, okay, yeah, yeah, it’s fine. I think
193 00:19:44.560 ⇒ 00:19:47.750 Annie Yu: we’ll mainly probably use python. There.
194 00:19:52.180 ⇒ 00:19:53.449 Luke Daque: Oh, no! This is me!
195 00:19:57.950 ⇒ 00:20:00.479 Luke Daque: What! What language do you like?
196 00:20:00.590 ⇒ 00:20:02.689 Luke Daque: What are you like trying to.
197 00:20:03.230 ⇒ 00:20:05.679 Annie Yu: I think, for this project. We’ll we’ll
198 00:20:05.810 ⇒ 00:20:20.550 Annie Yu: use python. But I think eventually they still want the visualization in power. Bi, and I think the reason to use python, because they also want to other than the graphs. They also want to see kind of the statistics
199 00:20:20.880 ⇒ 00:20:31.637 Annie Yu: like correlation between an independent, variable, and like dependent, variable like is is
200 00:20:32.400 ⇒ 00:20:34.559 Annie Yu: like a stay stay of week.
201 00:20:35.950 ⇒ 00:20:47.840 Annie Yu: I don’t. I don’t even know I’m like blanking on now, but but they also do want to see correlation. So I said, like, if we want to see correlation, I think using python makes more sense than like just power bi.
202 00:20:48.560 ⇒ 00:20:53.010 Luke Daque: I see. Yeah, that should be fine.
203 00:20:53.590 ⇒ 00:20:55.279 Luke Daque: 2, 8. Yeah.
204 00:20:55.690 ⇒ 00:21:00.270 Annie Yu: So use you’re using like bigquery results. So is that, okay.
205 00:21:00.270 ⇒ 00:21:03.929 Luke Daque: Yeah. And then that’s the query coming from whatever the
206 00:21:04.550 ⇒ 00:21:10.709 Luke Daque: the data set and table name is like. For this instance, I’m trying to query, this table.
207 00:21:11.329 ⇒ 00:21:16.700 Luke Daque: We run this, for example, it should show the results effort capable.
208 00:21:17.430 ⇒ 00:21:18.220 Annie Yu: Yeah.
209 00:21:18.640 ⇒ 00:21:19.940 Luke Daque: Oh, yeah, it looks like.
210 00:21:23.170 ⇒ 00:21:24.403 Annie Yu: So, if
211 00:21:26.720 ⇒ 00:21:29.630 Luke Daque: I have to get access.
212 00:21:31.710 ⇒ 00:21:36.020 Annie Yu: Then I think my question now will be if they
213 00:21:36.300 ⇒ 00:21:48.770 Annie Yu: cause. I I think if they don’t give us permission, and we don’t want to wait for them to give us permission. And if I’m just gonna create a separate collab, how do I connect that to.
214 00:21:48.910 ⇒ 00:21:52.950 Annie Yu: or doable.
215 00:22:02.640 ⇒ 00:22:04.349 Luke Daque: I haven’t tried yet, but.
216 00:22:04.490 ⇒ 00:22:05.219 Annie Yu: Yeah, I think we’ve talked.
217 00:22:05.220 ⇒ 00:22:09.289 Luke Daque: But I think you can check. Yeah, I think you can check Whatam shared. I think.
218 00:22:10.870 ⇒ 00:22:13.290 Annie Yu: Yeah, I haven’t gotten a chance to check.
219 00:22:18.190 ⇒ 00:22:18.930 Luke Daque: Yeah.
220 00:22:19.120 ⇒ 00:22:20.210 Annie Yu: But also.
221 00:22:20.210 ⇒ 00:22:22.790 Luke Daque: That out because I haven’t tested it yet.
222 00:22:22.790 ⇒ 00:22:40.120 Annie Yu: Yeah, I think one question. If I can’t, I can’t figure this out, but I want to make sure we can produce something as quickly as possible. It’s okay if I just use, because I do have, like Jupiter, extension in my cursor or Vsc.
223 00:22:41.200 ⇒ 00:22:43.266 Annie Yu: So I probably like, if.
224 00:22:43.680 ⇒ 00:22:44.080 Luke Daque: Yeah.
225 00:22:44.080 ⇒ 00:22:55.130 Annie Yu: Last resort, I can still use metamors. We do have a yeah, we do have a repo. Right? So I can just create a Python Jupiter there, as well.
226 00:22:55.130 ⇒ 00:22:57.830 Luke Daque: Yeah, that. Yeah, that should be fine. As soon as.
227 00:22:58.380 ⇒ 00:23:00.340 Annie Yu: Okay, I don’t think that’s a problem.
228 00:23:00.600 ⇒ 00:23:04.979 Annie Yu: Yeah, yeah, yeah. So that would be my last resort. If I just can’t figure this out.
229 00:23:06.105 ⇒ 00:23:10.329 Annie Yu: Okay, okay? And so I’m gonna I’m gonna generate like a very
230 00:23:10.910 ⇒ 00:23:17.260 Annie Yu: scrappy table. But I can. I share my screen, too? I’m not sure if you’ve seen this before.
231 00:23:18.512 ⇒ 00:23:22.190 Annie Yu: Okay, let me see this one.
232 00:23:22.390 ⇒ 00:23:28.950 Annie Yu: Have you seen this? They’re kind of like their proposal deck to the client.
233 00:23:32.190 ⇒ 00:23:39.779 Luke Daque: Hmm! I I think I saw that, but I haven’t like looked into the details.
234 00:23:39.780 ⇒ 00:23:40.350 Annie Yu: No, no.
235 00:23:40.350 ⇒ 00:23:42.449 Luke Daque: Just like breeze through it, or something.
236 00:23:42.450 ⇒ 00:23:49.779 Annie Yu: Yeah. And I think so for this one. They try to see all these, and I think Amber. And I was saying, Oh.
237 00:23:49.910 ⇒ 00:24:02.359 Annie Yu: let’s choose one or 2 graphs, so we at least know what fields we want to show. So I think my thinking now is this, we can show something very easy now, like Monday to Sunday. How many meetings per person.
238 00:24:02.360 ⇒ 00:24:03.150 Luke Daque: Hmm.
239 00:24:03.150 ⇒ 00:24:11.948 Annie Yu: And then I think their Fridays are their work from home day. So they wanna see, oh, are people active on Fridays or not? Really
240 00:24:12.400 ⇒ 00:24:15.339 Annie Yu: So yeah, that was something. I think that was
241 00:24:15.770 ⇒ 00:24:20.089 Annie Yu: something we can probably pull up more easily.
242 00:24:20.668 ⇒ 00:24:27.849 Annie Yu: So I’ll I’ll try to make this like as simple as possible. So but that that’s the one
243 00:24:27.950 ⇒ 00:24:34.230 Annie Yu: this that was the one kind of I was anchoring on, which I hope it’s not too crazy.
244 00:24:36.120 ⇒ 00:24:42.759 Luke Daque: Yeah, yeah, if you can maybe put that in the note, the the linear.
245 00:24:42.760 ⇒ 00:24:45.079 Annie Yu: They? They said, No.
246 00:24:45.230 ⇒ 00:24:58.357 Annie Yu: no screenshots, but but they share they did share access to us. So I I’m gonna make sure you have access to this too, just in case.
247 00:24:59.910 ⇒ 00:25:00.720 Luke Daque: Yeah, sure.
248 00:25:00.720 ⇒ 00:25:06.930 Annie Yu: You have. This is this is basically like the doc version of that. And then this is the.
249 00:25:06.930 ⇒ 00:25:07.500 Luke Daque: Oh!
250 00:25:07.500 ⇒ 00:25:09.739 Annie Yu: I’ll I’ll share these 2 with you.
251 00:25:09.740 ⇒ 00:25:10.910 Luke Daque: Okay, yeah, yeah.
252 00:25:10.910 ⇒ 00:25:11.570 Annie Yu: Okay.
253 00:25:11.880 ⇒ 00:25:14.946 Annie Yu: Alright, and I’ll let you know. Once.
254 00:25:17.120 ⇒ 00:25:21.470 Annie Yu: I’m done with that mark. I I think I’ll I’ll do that just now.
255 00:25:22.290 ⇒ 00:25:23.450 Luke Daque: Okay. Sounds good.
256 00:25:23.450 ⇒ 00:25:24.240 Annie Yu: Okay.
257 00:25:25.850 ⇒ 00:25:27.260 Annie Yu: Thank you. Look.
258 00:25:27.570 ⇒ 00:25:28.589 Luke Daque: Thanks, bye, bye.
259 00:25:28.590 ⇒ 00:25:29.050 Annie Yu: I.