Reddit reviews Data Points: Visualization That Means Something

Maybe this table can help some of you to gauge how worth the bundle is.

| | | Amazon | | | Goodreads | |
|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|------------------|---------|--------------|-----------|--------------|
| Tier | Title | Kindle Price ($) | Average | # of Ratings | Average | # of Ratings |
| 1 | Painting with Numbers: Presenting Financials and Other Numbers So People Will Understand You | 25.99 | 3.9 | 20 | 4.05 | 40 |
| 1 | Presenting Data: How to Communicate Your Message Effectively | 26.99 | 2.9 | 4 | 4.25 | 8 |
| 1 | Stories that Move Mountains: Storytelling and Visual Design for Persuasive Presentations | - | 4.0 | 13 | 3.84 | 56 |
| 1 | Storytelling with Data: A Data Visualization Guide for Business Professionals (Excerpt) | 25.99 | 4.6 | 281 | 4.37 | 1175 |
| 2 | 101 Design Methods: A Structured Approach for Driving Innovation in Your Organization | 22.99 | 4.2 | 70 | 3.98 | 390 |
| 2 | Cool Infographics: Effective Communication with Data Visualization and Design | 25.99 | 4.3 | 39 | 3.90 | 173 |
| 2 | The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions | 31.71 | 3.8 | 43 | 3.03 | 35 |
| 2 | Visualize This: The FlowingData Guide to Design, Visualization, and Statistics | 25.99 | 3.9 | 83 | 3.88 | 988 |
| 3 | Data Points: Visualization That Means Something | 25.99 | 3.9 | 34 | 3.87 | 362 |
| 3 | Infographics: The Power of Visual Storytelling | 19.99 | 4.0 | 38 | 3.79 | 221 |
| 3 | Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data | 40.99 | 4.2 | 3 | 3.59 | 14 |
| 3 | Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software, 2nd Edition | 39.99 | 4.0 | 66 | 4.14 | 111 |
| 3 | Visualizing Financial Data | 36.99 | 4.7 | 4 | 3.83 | 6 |

Cool, thanks for the details.

First, the good news:
You might already realize it, but this is a tremendous field to be in. The opportunity is absolutely massive. To put it simply, I’ll say that the world (companies, institutions, and soon, individuals) are currently generating more data than we can analyze. And year-over-year we’re generating data at a faster rate.

People who are excellent at analyzing data will have lots of high-salary, high-benefit opportunities (as it is, if you have the right skill set, it’s common to get contacted by Apple, Google, Facebook, Amazon; these companies all need skilled analytics workers).

Now, the challenge:
Learning analytics is hard.

Game plan:

Short Term:

In the short term you should focus on data visualization and “visual communication.” This means, communicating with charts, graphs, and images in place of excessive words. I won’t go into the details, but the human mind is wired for visual inputs. We don’t process spreadsheets, tables, and prose that well. However, our brains are sort of wired for visual inputs. The phrase “a picture speaks a thousand words” is fairly accurate.

I agree that “storytelling” is necessary, but I sometimes dislike it because I think it confuses what we’re actually doing. Let me unpack that term a little: storytelling actually means 1. finding valuable insights, 2. communicating valuable insights.

In the early stages of your career, the easiest way to find insights and communicate them is with visualization. (note that machine learning is also awesome for finding insightful information; it will be extremely difficult to teach yourself ML though, so hold off on that until you can take a class and have a mentor at work.)

That said, here’s what you should focus on:

1. Master the “Big 3” visualizations, with all their variations
i. Bar Chart
ii. Line Chart
iii. Scatterplot

What’s important is not just being able to do them, but being able to create them fast, accurately, and knowing when to use them. 80%+ of all reporting can be done with these 3 charts and their variants.

2. Learn conceptually how each visualization functions as a tool: when to use them, why, how they are best implemented, etc.
Nathan Yao’s Data Points is pretty good for this
Stephen Few’s books are also informative, but I like his material less than Yao’s.

3. Upgrade your tools
If you want to really develop in this career path, you have to move beyond Excel. Excel is great for quick-and-dirty tasks, but for a true analytics professional, it’s not a primary tool. (It doesn’t scale well at all, it’s functionality is limited, it’s more error prone, difficult to automate.)

Here are my two favorite tools, which I highly recommend. These are the tools that I wish I knew when I started:

Tableau, R

i. Tableau
Pros:
Great for rapidly creating lots of visualizations (simple charts and graphs, as well as some exotic ones).
Great for creating dashboards (you need to have Tableau Server for this). Dashboards can take some work off of your plate if you learn to automate the process and can convince your business partners to accept an online dashboard instead of a weekly/monthly/quarterly powerpoint.

Cons:
Automation can be difficult.
Tableau is bad at data wrangling. I really dislike doing any sort of data cleaning, merging, transformation in Tableau. Tableau just isn’t great at those tasks.

ii. R
Pros: Free and highly functional for data analytics. It’s very functionality is centered around analyzing data.
Cons: The learning curve is a bit steep. It takes time.


4. Master Presentation Design
Because your deliverables are mostly PowerPoint presentations (PPTs), you should really learn slide design. Honestly, if you do this right, you’ll be ahead of most analysts; most presentations are not well designed.

i. Presentation Zen, by Garr Reynolds

ii. Clear and to the Point, by Stephen Kosslyn





In the medium to long term, you’ll need to learn “data wrangling” (gathering, combining, re-shaping data).
I’d highly recommend learning SQL and R’s “plyr” package.


If you’re serious about analytics, you should start reading my blog. I’m writing about how to learn analytics step-by-step, and I’ll eventually cover all of these above topics (data visualization, R, Tableau, data wrangling, presentation design).

Also, if you have specific questions, stop by the blog and contact me on the “Contact” page.

All the best,

sharpsightlabs.com