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.

 

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DataQuest: Day 3-ish.

Quick update to say I’ve given DataQuest a try.

It’s radically different from the Microsoft or MOOC approach. Zero videos, all lab work. They’re big fans of the learn by doing approach.

I’m still on the (free) Python introduction, but already I can say it’s a step above Interactive Python. There are fewer walls of text and more chances to play around with code.

It costs ~29USD a month though. I haven’t been on the program long enough to judge if its worth it.

Also, I’ve decided to go for the Data Analyst path. I feel it’s less intimidating than the Data Scientist path. And I like that the progress bar goes up faster due to the smaller scope (I’m a bit of a completionist gamer, sorry). I can switch tracks later on anyway.

A more in-depth review in the works.

I may not love data science

Rather, I may not love data science as a whole. Just a part of it.

I’ve been having these thoughts since I started the statistics course for the Microsoft Data Science Program.

It’s boring.

I’m sorry, but it’s true. I’ve made no secret of how much I hate lectures. This course… it’s sickeningly brimming full of it. And there’s been no lab activities so far. Only reading comprehension quizzes which, frankly, can be answered by a simple Ctrl+F*.

My lack of interest has reflected in my progress. I used to average around a month per course, but now I’ve been stuck with the introductory modules for a while. It’s not realistic for me to complete by the end of the year. It’s put my target study schedule at risk.

It’s been so bad it’s made me question if I’m cut out for this whole big data business after all.

BUT BUT BUT I still have some semblance of faith.

Maybe, just maybe, I don’t have to be a data scientist. Maybe I just have to be good enough to be part of a data science team. As what the keynote speaker from the recent big data conference said,

He calls the data scientist a unicorn: difficult to find and even harder to keep. For most businesses starting out with analytics, investing in a data scientist will be too much of an overhead. Instead, he recommends to build out a data science team with distributed data science skills. e.g., Team members would include a statistics expert, a communicator, a programmer, a visualizer, etc.

–my notes from Isaac Reyes’ keynote speech during #BigDataPH2016

I’m pretty confident in my communication and visualization skills. I have some programming background. It’s statistics that’s my crux. I know I’ll have to study it anyway, just so I can speak the same language.

But I have to accept I may not have the affinity towards statistics as other data science skills.

It’s helping that I’ve been working on infographics and Excel charts lately. It’s reminded me of how much I love visualizing information. And discovering FlowingData? Peg, right there.

Actions

Of course, I can’t allow this feeling of disinterest to fester. I have to move on. So here are some of the productive procrastination I’ve been doing:

  1. Make myself excited about data science again.

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I bought the book Dataclysm on a whim. It’s not exactly a data science book. But it is full of insights the author picked up while analyzing his own data from managing the popular dating site OkCupid.

It’s a fascinating look into what kind of story numbers can tell you. I’m just on the first chapter on dating, and already I find it interesting how women are much more transparent with their love interests than men. And a bit disheartened to find how men are obsessed with youth.

It’s this ability to tell stories using numbers that got me curious about data science in the first place.

2. Switch gears.

I planned to start on coding when the new year starts, but I wanted to code so much more than to sit through another statistics lecture.

I’d never coded before so, at a reader’s recommendation, I started on Interactive Python‘s “How to Think Like a Computer Scientist.”Except I’ve surprised myself by saying,

Hey, I know this.

It might not be much, but apparently I do have some background in programming. I’d forgotten how much coding I did back in school, and even my first official job (creating and modifying UNIX accounts).

3. Don’t just switch gears, switch the whole damn car.

One problem I’m finding with MOOC-based learning is how it’s heavy on the videos, but limited follow-through. I thought the problem was already pretty bad with the Microsoft courses, but this statistics MOOC by Columbia just takes the cake. It’s a big problem for hands-on learning types like myself.

Unsurprisingly people have complained asked this before and one answer that frequently pops up is DataQuest:

At Dataquest, our unique teaching approach means that you’ll be able to learn all the relevant data science concepts, then build your own projects. These projects will help build your skills, and also form a portfolio that you can show to potential employers.

–DataQuest, “Why Learn Data Science?”

I’m finding this prospect of project-based learning very appealing. I’ll give it some more thought, but if any of you have tried it before I’d appreciate the feedback.

Locally, Data Seer offers data science training. Based on the schedules though it looks to be those workshop-type trainings I attend just for compliance. The ones I don’t really learn from but look damn nice on the resume. I ‘d be happy to be corrected though.

So… the hunt for a learning style that works is still on!

*I know, I know, it’s not the proper way to learn blah blah blah. Cut me a break ok? I’m an engineer. It’s ingrained in me to try to find the most efficient way.

Big Data Analytics Conference 2016

The Philippine analytics industry is still in it’s infancy. There is a demand for the skills NOW, and this demand will grow even more in the coming years.

This is the key takeaway from the Big Data Analytics Conference 2016, held at Enderun Tent last 15 November 2016. It was the first conference of it’s kind and scale in the Philippines, gathering participants from the IT, business, academic, and health industries.

As someone considering a possible career shift, I wanted to find out if there will be a market to shift to, and what are the kind of skills they’re looking for.

Below are my notes from the event, along with some insights.

Continue reading “Big Data Analytics Conference 2016”

Microsoft DAT206x: Analyzing and Visualizing Data with Excel Review

You never actually analyze and visualize data, but this course is worth taking as it’s a good introduction to using Power Pivot and Power Query–both of which are useful for managing large amounts of data in Excel. Just make sure you manage your expectations.

Update: To follow my progress in this program, check the Microsoft Professional Program tag.

 

Context

For those who are following this blog for my data science updates, it might be of interest to you that I am still working on Microsoft’s Professional Program for Data Science  (on beta). I have recently completed my second course, Analyzing and Visualizing Data with Excel.

This was my gateway course to the program. Excel enthusiasts at work had recommended it as a good introduction to PowerPivot, and it was only later that I found out the course was part of a larger data science program.

My primary purpose for taking the course was increasing my proficiency in Excel. I currently manage a large-scale project with an equally large-scale tracking spreadsheet. The spreadsheet easily gets out of hand due to the sheer number of assets involved and because it pulls data regularly from multiple data sources. I was hoping the course would help me clean up the data and make it sustainable to maintain in the long run.

Because of this, I’m reviewing the course from a more practical Can I use this at work? perspective rather than its relation (or lack of) to data science.

It took me about a month to complete, starting September 2016. You can follow my progress in the MS Data Science Program by using my tag Microsoft Professional Program.

Continue reading “Microsoft DAT206x: Analyzing and Visualizing Data with Excel Review”