Dear Microsoft, I’m confused.

Last year I heard about your Professional Program for Data Science.

I’ve been following along, albeit slowly, as I’ve been supplementing your content with other MOOCs. But the point is I’ve been following along and still intend to.

Your content is good. Not the best, but good.

Here’s the thing though: Why suddenly announce Microsoft Advanced Analytics?

On the surface it looks all shiny and new with the focus on Cortana Intelligence and Machine Learning.

Looking under the hood though, I see the course catalog and certifications mirror those of the original program.

What gives?

Are these two different, or the same? Are they meant to be complementary? Where does one stop and the other begin?

SO. MANY. QUESTIONS.

A data journalism peg: NY Times on Uber’s psychological mind games.

The New York Times is right up there with the Guardian’s Datablog in my data journalism aspirations.

One of my favorite posts of theirs is Snow Fall: a coverage of the 2012 Tunnel Creek avalanche. Its a wonderful mixture of storytelling, visualizations, and traditional journalistic interviews.

Go check it out first, I promise you won’t regret it. Just don’t forget to come back.

Unlike the Datablog however, the Times doesn’t collate their data viz content into a single page (IKR? Not even a tag!), so I often miss out on great content unless it hits viral.

(Before you suggest I subscribe to the Times, did you know they publish about 230 pieces of content daily? I’m not willing to sift through that!)

So I’m glad I didn’t miss out on this latest one: their coverage on How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons.

nyt_uber
This is a serious journalism piece. Not a game. I think.

What’s to like:

  • Interactive simulations!
  • The feature viz is a throwback to the 8-bit games of the 80s–which is kind of meta, given the post talks about how Uber experimented with video game techniques to maximize profit.
  • Charts. Charts. Charts. And interactive ones at that.
  • A union of social science with data science. How exciting! I like how they incorporated psychological vocabulary into the piece (e.g. loss aversion, ludic loop, binge-watching, etc).
  • “Uber exists in a kind of legal and ethical purgatory.” Please excuse me while I writer-geek out over this analogy.

Its a pretty length piece which will take about half an hour to get through, but I argue its worth it.

The best path to data science starts with the problem.

In the third grade, my science teacher sent shockwaves when she failed the final projects of more than half the class (thankfully I was in the minority).

This is it??? This is all you have?!

You can do better than this. These are too easy.

Give me something that’s actually worth… something!

Let me remind you: WE WERE THIRD-GRADERS. We were little brats who had never been told we sucked, much less failed.

Stricken by this failure, one classmate approached me after class to ask for advice. He had always been in the top 10 of the class. This must have devastated him.

Too bad I was never good at consoling, even as a kid. So instead I told him a story.

Of how I was playing outdoors the day before and was bothered by mosquitoes. Of how, try as I might, I couldn’t find where my mom hid the insect spray.

So I just used the first thing I found in the kitchen: Maggi savor.

(For those outside the Philippines, maggi savor is a blend of liquid seasoning, something like soy sauce but with garlic and lime.)

And to my surprise it worked. Not as effectively as insect spray, but the mosquitoes no longer buzzed as actively as before.

You can guess what happened next: Classmate wins title of “Best Project” for his study on The feasibility of soy sauce as a mosquito repellent alternative. I was… well, I passed so all was well.

 

Why am I sharing this story?

Because to me, my experiment had been nothing more but a curious solution to play outdoors.

But to my friend, and to my science teacher, it was a problem worth solving.

And as it turns out, that’s how to become a data scientist.

 

 

One of the most popular posts I’ve written on this blog is Getting started with Data Science, for the complete beginner. Its also one of my first posts.

Since then, many articles on the same topic have come up. But of note is this one published in Forbes  (originally from Quora). It answers the question, “What’s the best path to becoming a data scientist?”

  1. Pick a topic you’re passionate or curious about.
  2. Write the tweet first.
  3. Do the work.
  4. Communicate.

 

Where I said have a personal project, the writer took it to the next level by recommending to have a public portfolio:

I recommend building up a public portfolio of simple but interesting projects. You will learn everything you need in the process, perhaps even using all the resources above.

Makes sense right? More and more we’re judged by what we can do, no longer by the credentials we have. Artists, architects, and now programmers and developers… more and more jobs require having a portfolio.

 

What I haven’t considered is to write the tweet first.

Is the project even worth pursuing?

It sounds obvious, but people are eager to jump into a random tutorial or class to feel productive and soon sink months into a project that is going nowhere.

Ouch. I think she’s talking about me.

She’s got a good point though.

 

So. I now know I have to revisit my projects and write their tweets… but how do I talk about that portfolio?

If you’re like me and data science isn’t your day job, how do you talk about what are, essentially, your side projects?

It’s unfortunate that side projects are often overlooked by the people who aren’t actively working on them. Side projects can be immensely rewarding to talk about. They demonstrate a lot about how you work.

 

Thankfully LinkedIn has the ability to showcase projects. Its the perfect avenue to showcase your portfolio.

In person though, you may want to try this approach:

  1. Start with the problem
  2. Define your approach
  3. Share the challenges you faced
  4. End with the results
  5. Follow-up with what you would do differently

Again, it starts with the problem.

 

Like most things, the start is the most difficult step.

Finding the right problem is hard. But it might not need to be. It might already be there, right in front of you, just under your nose… and you just haven’t recognized it as a problem yet. Just like maggi savor.

In order to re-course my path to data science, the first thing I’m doing is to take a second look. But this time with a fresh set of eyes.

Danna on Data

It’s been a while since I’ve talked about my data analysis self-study.

I’ve been trying this and that, but haven’t felt anything was worth writing about. I mean, who would want to know that I tried something and failed, right?

Oh wait. Me. I would want to know.

When I’m about to try something new, like skincare or a restaurant, I look up blogs for reviews. I try to see if I can relate to the blogger and put myself in their shoes–Would I have failed as well?

It saves me a lot of effort because someone else has already gone through the experience for me.

That’s why I’m writing about all my data science-related updates so far, incomplete and disorganized as they are. Maybe it’ll help.

Continue reading “Danna on Data”

How These Three Women Made Mid-Career Pivots Into Data Science

The thing that bothered me the most about the #BIGDATAPH2016 Conference was how majority of the speakers wanted to source their data science talent from the university system.

I’ve talked again and again how uncomfortable I feel about this. How domain expertise, having business and industry context, is such a vital skill–not only in data science but across all domains. I feel its something we’ll miss out on if we focus too much on the technical aspect and not enough on the stories to be told.

So when I read about people who have managed to do it, people who defied the odds by pivoting their careers toward data science… I get excited. Encouraged. Especially when its women!

READ: How These Three Women Made Mid-Career Pivots Into Data Science

There’s more than one path into a successful data job than through the university system’s “talent pipeline.”

But while widening the so-called “talent pipeline” is one important way to narrow that gap, it’s not the only solution. If girls can be exposed to STEM programs early on in their educational careers, there’s no reason why adult women can’t make the leap into a data-based role later on in their professional ones.

Out of the women featured I could relate to Rebekah Iliff the most. She talks about making numbers tell a story, the same reason why I started studying data in the first place.

Iliff says saw herself as a storyteller—being able to think creatively by putting disparate pieces together. A in her world could just as well be connected to D as to B. The only hitch, she felt, was that results of those connections were more a matter of faith than calculable ROI; it was more art than science.

I’d make a guess that Iliff’s MBTI profile would say she’s an iNtuitive rather than Sensing. INtuitives tend to see the big picture. All the relationships and connections, but miss out on the details.

It’s the same reason why I moved from engineering and into project management. I didn’t like not knowing what I was building/testing/supporting things for. I envied how project managers got an end-to-end view. How they could see things from end-to-end, from initiation all the way to production. How they could see how different streams of work depended on each other. How everything was a balancing act, and the project manager was master juggler.

These days though, I’m getting greedy again. I want to see even more. I want to see the layers above the technology. I want to see the user impact–not only upon release, but months after. I want to see the large tech strategies that came into play to be able to decide on which projects to fund.

Things all far above my pay grade. Which is why I’m trying to skill up, and thankful for articles like the above for inspiration.