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.

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Why modern work is so boring

Remember when I said specialization is out, generalists are in?

No? Let me remind you then.

The Book of Life just released a new chapter, “Why Modern Work Is So Boring.” The answer is the same as I’ve told you before:

One of the overriding reasons why modern work is so boring is that we keep having to do more or less the same thing every day. We have to be specialists, whereas we would – deep in our hearts – surely be so much more fulfilled if we could be wide-ranging, endlessly curious generalists.

The book points to the same cause as I did, which is division of labor.

Its interesting to note that their takeaway message is different from most. Rather than have a call to action to convince people to become generalists, they basically say, suck it up and do your part for society:

We have collectively chosen to make work pay more rather than be more interesting. It’s a sombre thought but a consoling one too. Our suffering is painful but it has a curious dignity to it, because it does not uniquely affect us as individuals…

…In suffering in this way, we are participating in the common human lot.

A message I can live with. Not necessarily a piece of advice I’ll follow, but one I respect and can find merit in.

 

 

Some suitable related reading:

The challenges of choosing a career speaks of how, collectively, we all stress about our careers and why we do so. Yes, its normal to stress.

And even if you don’t end up choosing the right career, its okay. The “right” career is all a myth anyway.

And one I’ve struggled with, the job investment trap.

 

Project 2017 (Update #1 of n?)

UPDATE:
I’ve decided. Project 2017 will be all about FOCUS.

Something I know I’ve always been bad at, but something I know I need to meet the project objectives.

The target for the first week of 2017 is to clean up 2016 leftovers.

While transitioning bullet journals, I realized I had tasks in my future log that I never got around to doing. Things that weren’t urgent but had value: Updating my resume, setting medical appointments, renewing my passsport, etc.

I can’t expect to complete them all in one week, but the target is to do the hardest step: START. Start on all the leftover tasks. Have a plan in place for all. Plan out when to do the next steps.

That’s what my first week will be all about.

danna is a dork

At around this time each year, I take some time to reflect. What were the year’s highlights? Lowlights? What were the mundane but should not be forgotten?

And then I’d think of a theme. A catchy word or phrase that sums up the year.

Except 2014.

In 2014 I didn’t even need to reflect. I KNEW what the theme was.

The Year of Firsts.

I tried a lot of things for the first time that year. From big things like formally learning 日本語 (Japanese language), to small things like watching a movie alone. I explored side streets as often as I could and tried their hole-in-the-wall restaurants. The gastronomic adventures weren’t always successful–curse you weak stomach!–but they were adventures nonetheless.

All because I made one tiny resolution at the start of 2014: To scare the shit out of myself, as often as possible.

And I realize now, as I’m struggling to come…

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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.

 

Project 2017 [Working Title]

At around this time each year, I take some time to reflect. What were the year’s highlights? Lowlights? What were the mundane but should not be forgotten?

And then I’d think of a theme. A catchy word or phrase that sums up the year.

Except 2014.

In 2014 I didn’t even need to reflect. I KNEW what the theme was.

The Year of Firsts.

I tried a lot of things for the first time that year. From big things like formally learning 日本語 (Japanese language), to small things like watching a movie alone. I explored side streets as often as I could and tried their hole-in-the-wall restaurants. The gastronomic adventures weren’t always successful–curse you weak stomach!–but they were adventures nonetheless.

All because I made one tiny resolution at the start of 2014: To scare the shit out of myself, as often as possible.

And I realize now, as I’m struggling to come up with a theme for 2016, as I can’t even remember what 2015’s theme was, that this theme thing isn’t working. That whatever I did in 2014 worked better.

So I’m changing strategies. I’m going to do what I’m paid to do.

I am going to manage Project 2-0-1-7.

These past couple of years, most of my growth were the direct results of desperation, of firefighting, or from grabbing an opportunity. All good reasons, but all reactive.

That’s what I want to change. Next year, I want to be more proactive. I want to push myself to grow, not be “forced” to grow. Because that’s the only thing different in 2014, but that one change made a resounding impact.

Over the remaining days of 2016, I’ll be project planning.

What is Project 2017?

Project 2017 Objectives.

Project 2017 Minimum Success Criteria and Methodology.

 

What is Project 2017?

Project 2017 is Danna’s strategy to make 2017 count. To push herself to grow in the year 2017. To make something good happen within the year’s timeframe.

 

Project 2017 Objectives:

  • Get into data more seriously. I plan to get more involved with data science. Or data analysis, or data visualization, or data whatever (options are open). Not only as a growing interest but as a possible career shift.
  • Shake things up in my career. It’s been uncomfortably comfortable.
  • Ingrain this self-learning discipline (again, because of data science) into something permanent.
  • Improve my writing. A perpetual resolution but this time I’m serious!
  • Network more often (i.e., actively work on a personal crux).

 

Project 2017 Minimum Success Criteria:

Project 2017 will have no minimum success criteria. I want it to be FUN. I don’t want future self to feel pressured to follow what-would-become-past self’s standards. I don’t want to be constantly checking with myself,

Are you on-track with your my life?

NO. I intend to run Project 2017 the way I run most of my projects lately: in Agile.

For the uninitiated, Agile is… Well, it’s a project management framework, but with radically different values from traditional project management.

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan”

–The Agile Manifesto

Traditional project management will always have it’s place, but there’s a reason why Agile is hot right now. It’s easy. It’s sensible. And what I love about it the most: It adapts to change.

The only thing constant is change. Cliché but true. I’m a different person from who I was in 2014. And I might will change again by the end of 2017. I want to set Project 2017 up for success then, not the standards of success I have now.

Instead of having success criteria against the whole of 2017, I’ll start small. Target something every 1-4 weeks. That’s the size of the sprint in scrum terms (Scrum = an Agile methodology. This post is quickly turning into project management bootcamp.) At the end of the sprint, decide on a new target to work towards.

It’s similar to the Kaizen method, or the method of continuous incremental improvements. I just need to align those improvements toward the objectives.

So objectives, values, and process? Check.

What I don’t have is a project mission. The “scare the shit out of myself” version of 2017.

That’s why Project 2017 is still a working title. I’m not happy with it. I want it to be more descriptive of what I’m trying to achieve.

As of writing, I have 15 days `til go-live. Crunch time.