Making a Data Analysis Presentation

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From the course by Johns Hopkins University

Managing Data Analysis

1810 ratings

Johns Hopkins University

Managing Data Analysis

1810 ratings

Course 3 of 5 in the Specialization Executive Data Science

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD

From the lesson

Managing Data Analysis

Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the lecture. I'm excited to have you in the class and look forward to your contributions to the learning community. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

Routine Communication in Data Analysis6:48

Making a Data Analysis Presentation5:00

Meet the Instructors

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

This lecture provides just a couple of ideas of how to make a good data analysis

presentation.

There are a couple of key components that I feel like are important to making a good

presentation that involves data analysis,

and so I thought I would just list a couple of them here.

Just so you can kind of keep them in mind and

think about them the next time you're making your next presentation.

So, the first and I think the most important thing you want to have in

a presentation is to state the question that you're trying to answer.

I can't tell you how many times I've been in a presentation and

we've been ten minutes in and

I still don't exactly know what question this person is trying to answer.

Okay.

So, it's important, upfront, that you not assume that everyone is on the same page,

that everyone has the same background, but to state the question clearly and

succinctly so that everyone knows what the goal is.

Okay. And once everyone knows what the goal is,

we could all be kind of on the same page and oriented towards achieving that goal.

Okay. So stating the question

is very important from the get go in a presentation.

Secondary to that, you might want to describe what type of question it is that

you're trying to answer if it's not immediately obvious, okay.

So remember that we have the six types of questions that you can try to ask.

And so it's useful to the audience sometimes to know if you're trying to

ask an inferential question versus a causal one, versus a predictive one.

That can help to inform any discussion that may occur afterward in terms of

improving your analysis or modifying it.

Another thing that I like to do that I think is very useful is to show the data.

Okay. It's often helpful or

tempting to just show summaries, or you just kind of just not even data summaries,

but just kind of summaries in words of what the results are.

But it's actually very useful to show the data.

But just as a warning though, this could be a double edged sword.

Because people like to talk about data when they see it.

So if you want people to have good discussion, to have informed discussion

that may be useful to you, I think it's very useful to show the data.

People love to talk about data, but, so even if you don't want that discussion to

happen necessarily, you may be better off in the end if it does happen.

And so to this extent, I find plots are better than tables,

because plots show people, you know, a summary of the data, but

they also show people deviations from what might be expected, and so

plots are very useful for kind of producing discussion and

kind of encouraging people to think about the data.

When it's possible, if you're making a presentation and you're showing the data,

if you're showing some summary or a statistic about the data,

try to show a measure of uncertainty to go with it.

And the reason why is it just provides for a richer discussion when you can

incorporate the uncertainty into any predictions or

any estimates that you make from the data.

And so, try to have a broad array of measures of uncertainty so

that people can get the full picture of what's going on in your analysis.

So for example, if you have a primary model, it can be used to show results for

say, a bunch of secondary models or to show confidence intervals for parameters.

Lastly, I think it's important that when you present results from a data analysis,

that you separate three things,

they are the evidence, the interpretation and the decision.

Okay?

It's often easy to kind of conflate all these things into one sentence or

to one statement and I think it helps people to

provide a useful discussion if you can separate them out.

So for example, if you're looking at, let's say, an air pollutant and

some health outcome, you might find that

the increase in the air pollutant results, you estimate that

an increase in the air pollutant results in a 5% increase in the health outcome.

So that's bad.

So maybe the health outcome is mortality.

So, an increase in the pollutant results in a five percent increase in mortality.

So that's the evidence, that is the result of your analysis.

You estimated some parameter and that was the result, and

maybe there's a competence level that you can present around that too.

So, the interpretation might be, okay air pollution is bad for you, okay,

and then the decision might be we need to lower air pollution levels in

whatever environment we're thinking about.

So there's three separate components there that all could be evaluated independently.

Given a set of evidence, you're interpretation might be that,

oh if it's only 5% maybe air pollution's not so bad for you.

Or it might be 5% and it's terrible.

But your interpretation can be made separate from the evidence

that's provided.

And further more, what you do about it is further separate from what you think about

it, how you interpret it, and what the evidence is.

So, separating the evidence, the interpretation, and

the decision can help people think about the different components.

And can weigh the evidence, in terms of how they would react differently or

how important that evidence is to whatever process they're involved in.

So, I think doing that provides for a useful presentation and provides for

a more important, a useful discussion about the meaning of your results and

what we should do about them.

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