Dasha asks, "I want to take some classes on analytics and visualization skills - what would you recommend?"
I'd start by learning the principles of data visualization first. Edward Tufte's book, The Visualization of Quantitative Information, is the classic textbook to start with. Then look at Data Studio's introductory course, followed by Microsoft's free EdX course, followed by IBM's Statistics 101 course.
Tufte's book: https://amzn.to/3juckXq
Data Studio course: https://analytics.google.com/analytics/academy/course/10
IBM course: https://cognitiveclass.ai/badges/statistics-101
IBM course for R: https://cognitiveclass.ai/courses/data-visualization-with-r
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode dosha asks, I want to take some classes on analytics and visualization skills, what would you recommend? So really good question, a very common question.
There's a learning path or progression I would suggest taking, starting with.
Let's go ahead and bring this up here.
Starting with Edward tufte, his classic textbook, the visual display of quantitative information.
This is probably one of the oldest textbooks in the field.
And it's probably one of the best to get started in terms of how do we think about the information we want to convey? How do we format how to different charts and graphs and types, communicate information visually to somebody else.
A lot of the basic principles of data visualization are in this textbook.
So I would start by getting this textbook I'll put a link in the show notes, which you can get just down here.
If you want to click on through and get that disclosure to Amazon Associates think so that's the first place I would start because you want to get that basic knowledge that foundational knowledge first and Toughbook is one of the best in the field.
From there, start looking at some of the courses that are available.
So the first one I would start with, and I think it's probably the most practical for the average marketer is going to be the introduction to Data Studio.
So Google Analytics Academy has a number of courses for free.
Data Studio is a great basic course that teaches you the interface of Data Studio, but also applying some of those best practices for data visualization.
You'll see that in unit four data visualization basics.
Also, bonus, when you complete this course, you'll have the ability to do use Data Studio well.
And it's a very powerful free tool, plugs into Google Analytics plugs into Google Search Console a bunch of other things.
And that really is is practical.
applicable information right away.
After that, take the data visualization practical approach for absolute beginners from Microsoft.
So this is available on edX it is free.
You'll notice it's an archived course, which means that like the instructors are not online.
The the discussion forums are closed and stuff for you.
It's a course by itself.
But it's an excellent course of about a four week course, that teaches you visual literacy, and, again, applying a lot of data visualization practices.
And I think the most important module in here is thinking about the things that you're going to do wrong with data visualization.
If you've ever, ever seen the average business dashboard, they're usually a hot mess, right? There's stuff laying all over the place.
Someone's trying to cram too much information on it.
And this is a really good course for getting into think about visual literacy right? What needs to be communicated what doesn't need to be communicated.
After you've got this down, it's time to kick things up a level.
One of the challenges with data visualization is the data itself may or may not be any good.
And what data you want to communicate, also may not be available outside the box out of the box.
So really good example, Google Analytics has a ton of data in it.
But virtually no transformations of any kind.
So if you look at like your website traffic, there's no mean there's no media, there's no basic statistical approaches to it, you get what you get in the tool itself, and that's fine.
It's not fine if you want to add a quick value added insights.
To do that.
You need some statistical knowledge.
So the next course I recommend taking is over at IBM is cognitive class, go to cognitive class AI.
This is that's one on one course.
And this is actually Of course for teaching you the basics of statistics, right? So things like descriptive stats, variants, probability correlation, the one on one stuff that, frankly, we should have all taken in college, I did take a stats course in college, I did not pass it.
Because the teacher wasn't great.
We now have the opportunity to go back and fix those mistakes of the past and taking stats 101 so that we learn how to think about the data that we're presenting.
And make sure it is valid and and clear and obvious what it is that we're doing before we slap it into a visualization, right? Remember that visualization means nothing if the data that makes it up is wrong.
So stats one on ones that is I would say the fourth thing that you should take.
The final thing that you should take and this is now kicking things up a notch is data visualization with R so the programming language r again, this is a cognitive class, IBM To also free.
This is on how to do the actual visualizations in the programming language are.
So if you are doing any kind of really heavy statistical or data science work, including stuff like, you know pulling social media analytics and Google Analytics data into an environment that you can analyze it, R is the language to do that's one of the languages to do that.
And it has a visualization library built in that is a little tricky side.
But if you want to be able to programmatically do visualizations, meaning once you've done it once, and you want to rerun the airport, the next month or next week, whatever, you can literally hit you know, execute code and it will redo everything for you.
So you don't have to do it.
Again, that's the value of programmatic visualization.
This is the course to teach you how to do that.
Now you'll note that one of the prerequisites there is a an r1 on one course if you have not taken that one, I would take that one as well because that will get you all To the bare bones basics of how to use the our programming language.
Now, except for toughies book, all of these courses are free, right? So there's no financial cost in taking them.
The only cost is your time and your effort, your willingness to study.
If you take all four of these five, if you count the r1 r1 on one course, if you take all five of these, and you are diligent about it, you will have a, I'd say a great working competency of data visualization, and the ability to apply it to whatever marketing data you're looking at.
This makes you something of a unicorn.
Because this is not a skill that a lot of marketers have, right? A lot of marketers kinda shy away from the math side of things, the quantitative side of things, but if you have these skills, then you can apply your creative abilities and your quantitative abilities and drastically increase the amount of value you Bring to an organization, drastically increase the amount of money you can earn.
And you might find that you really enjoy it.
I certainly do, despite having a rough start in statistics.
So that's the order.
And I would do this these in that order so that you if you try and jump in our one on one right away, it's not for everybody.
Right? And it can be a little discouraging.
So get the foundation's down first, and then elevate into the more technical stuff afterwards.
Really good question.
Good luck with the courses, I find.
They're all very good.
They're all taught by legitimate subject matter experts.
I look for that.
in evaluating courses.
I look for people who are actually qualified to be teaching them.
And remember that a good chunk of the education for this is going to be practice.
So once you've taken the courses, you then have to put it into practice and keep putting into practice.
It's like anything else, you know, working out, whatever.
You got to keep doing it to keep yourself strong.
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