9:59:59.000,9:59:59.000
1[br]00:00:04,470 --> 00:00:10,000[br]Blow and this video, I'm going to introduce our week three module on presenting data.

9:59:59.000,9:59:59.000
2[br]00:00:10,000 --> 00:00:14,110[br]So our learning outcomes for this week are for you to be able to create plots from data,

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3[br]00:00:14,110 --> 00:00:18,910[br]identify the appropriate plot for type of plot for data in question.

9:59:59.000,9:59:59.000
4[br]00:00:18,910 --> 00:00:24,130[br]I want you to be able to read and interpret a plot, refine a plot more clearly, show its data,

9:59:59.000,9:59:59.000
5[br]00:00:24,130 --> 00:00:34,030[br]but then also put these plots and our other presentations in discussions of data into a well-organized notebook to present your data analysis.

9:59:59.000,9:59:59.000
6[br]00:00:34,030 --> 00:00:41,260[br]Before we dove into how to actually present data, I want to start with talking about some of the purposes of data presentation,

9:59:59.000,9:59:59.000
7[br]00:00:41,260 --> 00:00:45,190[br]because these purposes should guide your presentation design decisions.

9:59:59.000,9:59:59.000
8[br]00:00:45,190 --> 00:00:49,750[br]They should guide your evaluation of your own presentations and those of others.

9:59:59.000,9:59:59.000
9[br]00:00:49,750 --> 00:00:56,050[br]They'll also guide my evaluation of your presentation when you are submitting assignments.

9:59:59.000,9:59:59.000
10[br]00:00:56,050 --> 00:01:02,320[br]And one of the first things we need to do is guide the reader attention to important results.

9:59:59.000,9:59:59.000
11[br]00:01:02,320 --> 00:01:07,900[br]And effective presentation is not going to have every piece of thing in it.

9:59:59.000,9:59:59.000
12[br]00:01:07,900 --> 00:01:12,820[br]It's going to draw focus and attention to the important results and make it easy for

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13[br]00:01:12,820 --> 00:01:17,710[br]your reader to ask the key questions around the data analysis you're presenting.

9:59:59.000,9:59:59.000
14[br]00:01:17,710 --> 00:01:22,420[br]But then it also needs to substantiate the results and conclusions.

9:59:59.000,9:59:59.000
15[br]00:01:22,420 --> 00:01:30,010[br]So when we're presenting the data, we want to guide the reader to focus on what it is that we want them to learn,

9:59:59.000,9:59:59.000
16[br]00:01:30,010 --> 00:01:39,430[br]but also in a context that gives them the information needed to assess the validity of the conclusions that we're presenting.

9:59:59.000,9:59:59.000
17[br]00:01:39,430 --> 00:01:44,710[br]And we want to do so with integrity. It's easy to make charts that highlight the result.

9:59:59.000,9:59:59.000
18[br]00:01:44,710 --> 00:01:50,440[br]You want the user to see whether or not or not that result is rigorously defensible from the data.

9:59:59.000,9:59:59.000
19[br]00:01:50,440 --> 00:01:57,730[br]And we want to avoid making those kinds of misleading data visualizations and presentations.

9:59:59.000,9:59:59.000
20[br]00:01:57,730 --> 00:02:03,910[br]So in doing this, we want to be able to think about the audience and you're gonna be presenting data to several different audiences.

9:59:59.000,9:59:59.000
21[br]00:02:03,910 --> 00:02:05,710[br]The first audience is yourself.

9:59:59.000,9:59:59.000
22[br]00:02:05,710 --> 00:02:12,770[br]When you're working with a data set, when you're trying to understand what you're learning from it, the results of your inferences,

9:59:59.000,9:59:59.000
23[br]00:02:12,770 --> 00:02:21,070[br]your presenting data to yourself, and you need the presentation to be clear so that you understand what it is that you're learning from the data.

9:59:59.000,9:59:59.000
24[br]00:02:21,070 --> 00:02:26,980[br]So you see the next question to asks. You're not misleading yourself in the data analysis process.

9:59:59.000,9:59:59.000
25[br]00:02:26,980 --> 00:02:34,000[br]But those kinds of charts don't necessarily need the same level of Polish that a chart for an external audience.

9:59:59.000,9:59:59.000
26[br]00:02:34,000 --> 00:02:39,880[br]Would your collaborators, supervisors, et cetera, need to be able to see the data, see what you're learning?

9:59:59.000,9:59:59.000
27[br]00:02:39,880 --> 00:02:43,690[br]Maybe it's in the weekly meeting you have with your research advisor.

9:59:59.000,9:59:59.000
28[br]00:02:43,690 --> 00:02:47,130[br]You're presenting them with the results that you found.

9:59:59.000,9:59:59.000
29[br]00:02:47,130 --> 00:02:53,340[br]They are people who have a lot of knowledge of the project you're working on another problem base you're working on.

9:59:59.000,9:59:59.000
30[br]00:02:53,340 --> 00:02:58,230[br]They're gonna help you guide and refine your questions.

9:59:59.000,9:59:59.000
31[br]00:02:58,230 --> 00:03:06,660[br]Again, they perhaps don't need as much Polish as a final published result, but they're not just the ones that you're creating internally for yourself.

9:59:59.000,9:59:59.000
32[br]00:03:06,660 --> 00:03:08,550[br]You're going to be presenting to expert readers.

9:59:59.000,9:59:59.000
33[br]00:03:08,550 --> 00:03:15,030[br]If you write a scientific paper, the readers are probably usually wouldn't have some level of expertize in the topic that you're talking about.

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34[br]00:03:15,030 --> 00:03:20,550[br]They may know. They'll probably know the subject in general, but they may not know your specific work.

9:59:59.000,9:59:59.000
35[br]00:03:20,550 --> 00:03:28,650[br]You may be presenting this to decision makers, especially if you're doing a data science project in an industrial or corporate environment.

9:59:59.000,9:59:59.000
36[br]00:03:28,650 --> 00:03:29,820[br]You're providing data.

9:59:59.000,9:59:59.000
37[br]00:03:29,820 --> 00:03:39,240[br]That's going to inform the decisions that your boss, who may not have significant statistical expertize or data expertize that may,

9:59:59.000,9:59:59.000
38[br]00:03:39,240 --> 00:03:45,840[br]but they're going to be using those decision, those the data and the the data that you present in order to make decisions.

9:59:59.000,9:59:59.000
39[br]00:03:45,840 --> 00:03:52,170[br]And then finally, you may occasionally be be producing or presenting data for the general public at large.

9:59:59.000,9:59:59.000
40[br]00:03:52,170 --> 00:03:57,120[br]Each of these audiences is going to require different things from your data presentation.

9:59:59.000,9:59:59.000
41[br]00:03:57,120 --> 00:04:04,830[br]So you need to understand who it is that you're presenting the data to in order to make appropriate data presentation decisions.

9:59:59.000,9:59:59.000
42[br]00:04:04,830 --> 00:04:14,940[br]When we're presenting the data, here are some questions that are going to help us understand what it is that we need to guide the reader towards.

9:59:59.000,9:59:59.000
43[br]00:04:14,940 --> 00:04:22,110[br]So we need to be clear on the reader needs to come away knowing what we sought to find out.

9:59:59.000,9:59:59.000
44[br]00:04:22,110 --> 00:04:30,360[br]This might be just explicitly stating our research questions, but they need to know the purpose that the data we're presenting is supposed to serve.

9:59:59.000,9:59:59.000
45[br]00:04:30,360 --> 00:04:36,630[br]What are they supposed to learn from it? We then they didn't then need to see what we do learn.

9:59:59.000,9:59:59.000
46[br]00:04:36,630 --> 00:04:43,110[br]And then they need to see the supporting evidence, the context to trust the conclusions.

9:59:59.000,9:59:59.000
47[br]00:04:43,110 --> 00:04:48,330[br]It's not just enough to say here's the results, but in many cases you need to provide enough data,

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48[br]00:04:48,330 --> 00:04:56,190[br]enough context that they not only see what you learned, but they see why you believe it is true.

9:59:59.000,9:59:59.000
49[br]00:04:56,190 --> 00:05:04,350[br]And presentation with integrity shows the reader and really makes it clear to the reader what we learned.

9:59:59.000,9:59:59.000
50[br]00:05:04,350 --> 00:05:08,370[br]The evidentiary support behind it, why it flows from the data.

9:59:59.000,9:59:59.000
51[br]00:05:08,370 --> 00:05:17,280[br]Whereas dishonest presentation manipulates them into the conclusion without having the rigorous foundation underneath it.

9:59:59.000,9:59:59.000
52[br]00:05:17,280 --> 00:05:26,790[br]So I want to show you an example of a very bad graphic that came up out of the state of Georgia earlier this year.

9:59:59.000,9:59:59.000
53[br]00:05:26,790 --> 00:05:40,980[br]They presented campaign ad hundred network television, a graph that's purporting to show Kofod cases in various high population counties over time.

9:59:59.000,9:59:59.000
54[br]00:05:40,980 --> 00:05:49,170[br]But if you look closely at the Y at the X axis of this graph and you'll see this more clearly when you go and look at the slides,

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55[br]00:05:49,170 --> 00:05:57,660[br]the axis is not sorted. It starts with the twenty eighth of April and then it goes to the twenty seventh, followed by the twenty ninth.

9:59:59.000,9:59:59.000
56[br]00:05:57,660 --> 00:06:01,470[br]Then May 1st. Then April 30th. At the end we have May 2nd.

9:59:59.000,9:59:59.000
57[br]00:06:01,470 --> 00:06:06,420[br]May 7th. April 26. May 3rd. It violates the expected convention.

9:59:59.000,9:59:59.000
58[br]00:06:06,420 --> 00:06:12,030[br]And what they're what you'd need to do to show a trend over time that time goes from left to right.

9:59:59.000,9:59:59.000
59[br]00:06:12,030 --> 00:06:18,960[br]They're sorting things in. They're putting things out of order to show the trend.

9:59:59.000,9:59:59.000
60[br]00:06:18,960 --> 00:06:26,880[br]They want. In a way that's not substantiated by the data and it takes a lot of work to make a chart.

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61[br]00:06:26,880 --> 00:06:35,480[br]This bat. I'm not sure how to do it in any of the statistical software I actually use, but this is a this is an egregious example,

9:59:59.000,9:59:59.000
62[br]00:06:35,480 --> 00:06:42,220[br]but it's an example of one of the things that can happen when where when we focus on.

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63[br]00:06:42,220 --> 00:06:47,220[br]The effect we want to demonstrate over the evidentiary support for it.

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64[br]00:06:47,220 --> 00:07:04,960[br]I want to contrast with a. I want to contrast with a chart from W edi w e, b do BWAS charts created for the nineteen hundred parece X position.

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65[br]00:07:04,960 --> 00:07:11,250[br]And these were a series of charts for an exhibition to show the economic, educational,

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66[br]00:07:11,250 --> 00:07:19,660[br]etc. progress of black Americans from emancipation to the turn of the century.

9:59:59.000,9:59:59.000
67[br]00:07:19,660 --> 00:07:23,500[br]And he made a series of charts showing economic situations and things.

9:59:59.000,9:59:59.000
68[br]00:07:23,500 --> 00:07:28,090[br]And here's a bar chart that clearly shows the result.

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69[br]00:07:28,090 --> 00:07:35,350[br]The distribution of economic statuses for farmers after a year of farm labor.

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70[br]00:07:35,350 --> 00:07:39,250[br]And it shows we have the first categories of bankrupt and in debt.

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71[br]00:07:39,250 --> 00:07:46,570[br]And then we have four different or five different levels of of non-negative return up to clearing.

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72[br]00:07:46,570 --> 00:07:53,160[br]Fifty dollars or more. And it shows them the bars are proportional to the length of the data.

9:59:59.000,9:59:59.000
73[br]00:07:53,160 --> 00:07:55,290[br]Very clearly highlights these things.

9:59:59.000,9:59:59.000
74[br]00:07:55,290 --> 00:08:03,720[br]It then also does a creative thing of pulls out the separate bar that it indicates is the composite of all of the non-negative bars.

9:59:59.000,9:59:59.000
75[br]00:08:03,720 --> 00:08:08,760[br]And we can see that even if you if you add all of that, they non-negative bars together.

9:59:59.000,9:59:59.000
76[br]00:08:08,760 --> 00:08:13,860[br]It's not as many farmers as as the indebt category.

9:59:59.000,9:59:59.000
77[br]00:08:13,860 --> 00:08:17,490[br]That's not a very standard thing. These charts were hand drawn.

9:59:59.000,9:59:59.000
78[br]00:08:17,490 --> 00:08:25,260[br]But it's a creative use of the visualization to highlight in a way that's supported by the data,

9:59:59.000,9:59:59.000
79[br]00:08:25,260 --> 00:08:35,910[br]the relative distributions of a different in return levels for black American farmers at the time.

9:59:59.000,9:59:59.000
80[br]00:08:35,910 --> 00:08:42,990[br]Another one, that's another one that's creative here is this spiral bar chart.

9:59:59.000,9:59:59.000
81[br]00:08:42,990 --> 00:08:49,080[br]Again, it's an unusual thing, but the lines, we can see them going progressively longer.

9:59:59.000,9:59:59.000
82[br]00:08:49,080 --> 00:08:55,680[br]And if this were just a part of normal horizontal bar chart without the spiral that the smallest line,

9:59:59.000,9:59:59.000
83[br]00:08:55,680 --> 00:09:00,570[br]the first line, 1975, would be so small you couldn't even see us.

9:59:59.000,9:59:59.000
84[br]00:09:00,570 --> 00:09:07,050[br]This gives more space. And it shows good visualization doesn't just mean following the checklist of rules.

9:59:59.000,9:59:59.000
85[br]00:09:07,050 --> 00:09:16,920[br]It means presenting the data in a way that the conclusions and the takeaways are clear and they're rigorously supported by the underlying data.

9:59:59.000,9:59:59.000
86[br]00:09:16,920 --> 00:09:20,850[br]There's no visual tricks to make things look larger or smaller than they are.

9:59:59.000,9:59:59.000
87[br]00:09:20,850 --> 00:09:27,990[br]It transparently shows the connection between the conclusion and the underlying data that support it.

9:59:59.000,9:59:59.000
88[br]00:09:27,990 --> 00:09:33,850[br]So to wrap up, the goal of good presentation is to guide the reader to what we learned and how we know it.

9:59:59.000,9:59:59.000
89[br]00:09:33,850 --> 00:09:41,460[br]Effective. Presentation is going to highlight the important things for the reader to understand without distraction or deception.

9:59:59.000,9:59:59.000
90[br]00:09:41,460 --> 00:09:55,533[br]And we're going to see throughout more of this week and throughout more the semester how practically to go about doing that.

9:59:59.000,9:59:59.000