Democratize Data Science

Every once in a while, I would come across an article that decries online data science courses and boot camps as pathways towards getting a data science job. Most of the articles aim not to discourage but serve as a reminder to take a hard look in the mirror first and realize what we’re up against. However, a few detractors have proclaimed that the proliferation of these online courses and boot camps have caused the degradation of the profession.

To the latter, I vehemently disagree.

Bridging the Skill Gap

Data science have captured popular imagination ever since Harvard Business Review dubbed data scientist as the sexiest job of the 21st century. More than seven years later, data science remains one of the most highly sought-after job markets today. In fact, due to the dynamics of supply and demand, “the United States alone is projected to face a shortfall of some 250,000 data scientists by 2024¹.”

As a result, capitalism and entrepreneurship answered the call and companies like Codeup have vowed to “help bridge the gap between companies and people wanting to enter the field.”²

In addition, AutoML libraries like PyCaret are “democratizing machine learning and the use of advanced analytics by providing free, open-source, and low-code machine learning solution for business analysts, domain experts, citizen data scientists, and experienced data scientists”³.

The availability of online courses, boot camps, and AutoML libraries has led a lot of data scientists to raise their brows. They fear that boot camp alumni and self-taught candidates would somehow lower the overall caliber of data scientists and disgrace the field. Furthermore, they are afraid that the availability of tools like AutoML would allow anyone to be a data scientist.

I mean, God forbid if anyone thinks that they too can be data scientists! Right?


The Street Smart Data Scientist

Alumni of boot camps and self-taught learners, like myself, have one thing going for them: our rookie smarts. To quote Liz Wiseman, author of the book Rookie Smarts:

In a rapidly changing world, experience can be a curse. Being new, naïve, and even clueless can be an asset. — Liz Wiseman

Rookies are unencumbered. We are alert and constantly seeking like hunter-gatherers, cautious but quick like firewalkers, and hungry and relentless like frontiersmen⁴. In other words, we’re street smart.

Many are so bogged down by “you’ve got to learn this” and “you’ve got learn that” that they forget to stress the fact that data science is so vast that you can’t possibly know everything about anything. And that’s okay.

We learn fast and adapt quickly.

At the end of the day, it’s all about the value that we bring to our organizations. They are, after all, the ones paying our bills. We don’t get paid to memorize formulas or by knowing how to code an algorithm from scratch.

We get paid to solve problems.

And this is where the street smart data scientist excels. We don’t suffer from analysis paralysis or be bothered with theories, at least not while on the clock. Our center of focus is based on pragmatic solutions to problems, not on academic debate.

This is not to say we’re not interested in the latest research. In fact, it’s quite the contrary. We are voracious consumers of the latest development in machine learning and AI. We drool over the latest development in natural language processing. And we’re always on the lookout for the latest tool that will make our jobs easier and less boring.

And AutoML

So what if we have to use AutoML? If it gets us to an automatic pipeline where analysts can get the results of machine learning without manual intervention by a data scientist, the better. We’re not threatened by automation, we’re exhilarated by it!

Do not let perfection be the enemy of progress. — Winston Churchill

By building an automatic pipeline, there’s bound to be some tradeoffs. But building it this way will free our brain cells and gives us more time to focus on solving other higher-level problems and produce more impactful solutions.

We’re not concerned about job security, because we know that it doesn’t exist. What we do know is that the more value we bring to a business, the better we will be in the long run.

Maybe They’re Right?

After all this, I will concede a bit. For the sake of argument, maybe they’re right. Maybe online courses, boot camps, and low-code machine learning libraries really do produce low-caliber data scientists.

Big maybe.

But still, I argue, this doesn’t mean we don’t have value. Data science skills lie on a spectrum and so does companies’ maturity when it comes to data. Why hire a six-figure employee when your organization barely has a recognizable machine learning infrastructure?

Again, maybe.

The Unicorn

Maybe, to be labeled as a data scientist, one must be a unicorn first. A unicorn data scientist is a data scientist who excels at all facets of data science.

Image for post
Hckum / CC BY-SA (

Data science has long been described as the intersection between computer science, applied statistics, and business or domain knowledge. To this, they ask, how can one person possibly accumulate all those knowledge into just a few months? To this, we also ask the same question, how can a college grad?

Unicorns do exist I believe, but they also have had to start from somewhere.

So why can’t we?


A whole slew of online courses and tools promise to democratize data science, and this is a good thing.

Thank you for reading. If you want to learn more about my journey from slacker to data scientist, check out the article From Slacker to Data Scientist: My journey into data science without a degree.

And if you’re thinking about switching gears and venture into data science, start thinking about rebranding now The Slacker’s Guide to Rebranding Yourself as a Data ScientistOpinionated advice for the rest of us. Love of math, optional.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1] Harvard Business Review. (June 3, 2020). Democratizing Data Science in Your Organization.

[2] San Antonio Express-News. (June 3, 2020). Software development bootcamp Codeup launching new data science program.

[3] Towards Data Science. (June 4, 2020). Machine Learning in Power BI Using PyCaret.

[4] The Wiseman Group. (June 4, 2020). Rookie Smarts
Why Learning Beats Knowing in the New Game of Work.

This article was first published in Towards Data Science’ Medium publication.

From Slacker to Data Scientist

My journey into data science without a degree.

Butterflies in my belly; my stomach is tied up in knots. I know I’m taking a risk by sharing my story, but I wanted to reach out to others aspiring to be a data scientist. I am writing this with hopes that my story will encourage and motivate you. At the very least, hopefully, your journey won’t be as long as mine.

So, full speed ahead.

I don’t have a PhD. Heck, I don’t even have any degree to speak of. Still, I am very fortunate enough to work as a data scientist in a ridiculously good company.

How I did it? Hint: I had a lot of help.

Never Let Schooling Interfere With Your Education — Grant Allen

Formative Years

It was 1995 and I had just gotten my very first computer. It was a 1982 Apple IIe. It didn’t come with any software but it came with a manual. That’s how I learned my very first computer language: Apple BASIC.

My love for programming was born.

In Algebra class, I remember learning about the quadratic equation. I had a cheap graphic calculator then, a Casio, that’s about half the price of a TI-82. It came with a manual too so I decided to write a program that will solve the quadratic equation for me without much hassle.

My love for solving problems was born.

In my senior year, my parents didn’t know anything about financial aid but I was determined to go to college so I decided to join the Navy so that I could use MGIB pay for my college. After all, four years of service didn’t seem that long.

My love for adventure was born.

Later in my career in the Navy, I was promoted as the ship’s financial manager. I was in charge of managing multiple budgets. The experience taught me bookkeeping.

My love for numbers was born.

After the Navy, I ended volunteering for a non-profit. They eventually recruited me to start a domestic violence crisis program from scratch. I had no social work experience but I agreed anyway.

My love for saying “Why not?” was born.

Rock Bottom

After a few successful years, my boss retired and the new boss fired me. I was devastated. I fell into a deep state of clinical depression and I felt worthless.

I recall crying very loudly in the kitchen table. It has been more than a year since my non-profit job and I’m nowhere near close as having a prospect for the next one. I was in a very dark space.

Thankfully, the crying fit was a cathartic experience. It gave me a jolt to do some introspection, stop whining, and come up with a plan.

“Choose a Job You Love, and You Will Never Have To Work a Day in Your Life. “ — Anonymous

Falling in Love, All Over Again

To pay the bills, I’ve been working as a freelance web designer/developer but I wasn’t happy. Frankly, the business of doing web design bored me. It was frustrating working with clients who think and act like they’re the expert on design.

So I started thinking, “what’s next?”.

Searching the web, I’ve stumbled upon the latest news in artificial intelligence. It led me to machine learning which in turn led me to the subject of data science.

I was infatuated.

I signed up for Andrew Ng’s machine learning course on Coursera. I listened to TwitML, Linear Digression, and a few other podcasts. I revisited Python and got reacquainted with git on Github.

I was in love.

It was at this time that I made the conscious decision to be a data scientist.

Leap of Faith

Learning something new was fun for me. But still, I had that voice in my head telling me that no matter how much I study and learn, I will never get a job because I don’t have a degree.

So, I took a hard look at the mirror and acknowledge that I need help. The question now is where to start looking.

Then one day out of the blue, my girlfriend asked me what data science is. I jumped off my feet and starting explaining right away. Once I stopped explaining to catch a breath, I managed to ask her why she asked. And that’s when she told me that she’d seen a sign on the billboard. We went for a drive and saw the sign for myself. It was a curious billboard with two big words “data science” and a smaller one that says “Codeup.” I went to their website and researched their employment outcome.

I was sold.


Before the start of the class, we were given a list of materials to go over.

Given that I had only about two months to prepare, I was not expected to finish the courses. I was basically told to just skim over the content. Well, I did them anyway. I spent day and night going over the courses and materials. Did the tests, got the certificates!


Boot camp was a blur. We had a saying in the Navy about the boot camp experience: “the days drag on but the weeks fly by.” This was definitely true for the Codeup boot camp as well.

Codeup is described as a “fully-immersive, project-based 18-week Data Science career accelerator that provides students with 600+hours of expert instruction in applied data science. Students develop expertise across the full data science pipeline (planning, acquisition, preparation, exploration, modeling, delivery), and become comfortable working with real, messy data to deliver actionable insights to diverse stakeholders.”¹

We were coding in Python, querying the SQL database, and making dashboards in Tableau. We did projects after projects. We learned about different methodologies like regression, classification, clustering, time-series, anomaly detection, natural language processing, and distributed machine learning.

More importantly, the experience taught us the following:

  1. Real data is messy; deal with it.
  2. If you can’t communicate with your stakeholders, you’re useless.
  3. Document your code.
  4. Read the documentation.
  5. Always be learning.

Job Hunting

Our job hunting process started from day one of boot camp. We updated our LinkedIn profile and made sure that we’re pushing to Github almost every day. I even spruced up my personal website to include the projects we’ve done during class. And of course, we made sure that our resumé is in good shape.

Codeup helped me with all of these.

In addition, Codeup also helped prepare us for both technical and behavioral interviews. We practiced answering questions following the S.T.A.R. format (Situation, Task, Action, Result). We optimized our answers to highlight our strengths as high-potential candidates.


My education continued even after graduation. In between filling out applications, I would code every day and try out different Python libraries. I regularly read the news for the latest development in machine learning. While doing chores, I listen to a podcast, a TedTalk, or a LinkedIn learning video. When bored, I listened to or read books.

There’s a lot of good technical books out there to read. But for the non-technical ones, I recommend the following:

  • Thinking with Data by Max Shron
  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neill
  • Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez
  • Rookie Smarts: Why Learning Beats Knowing in the New Game of Work by Liz Wiseman
  • Grit: The Power of Passion and Perseverance by Angela Duckworth
  • The First 90 Days: Proven Strategies for Getting Up to Speed Faster and Smarter by Michael Watkins

Dealing with Rejection

I’ve had a lot of rejections. The first one was the hardest but after that, it kept getting easier. I developed a thick skin and just moved on.

Rejection sucks. Try not to take it personally. Nobody likes to fail, but it will happen. When it does, fail up.


It took me 3 months after graduating from boot camp to get a job. It took a lot of sacrifices. When I finally got the job offer, I felt very grateful, relieved, and excited.

I could not have done it without Codeup and my family’s support.

Thanks for reading! I hope you got something out of this post.

To all aspiring data scientists out there, just don’t give up. Try not to listen to all the haters out there. If you must, hear what they have to say, take stock of your weaknesses, and aspire to learn better than yesterday. But never ever let them discourage you. Remember, data science skills lie on a spectrum. If you’ve got the passion and perseverance, I’m pretty sure that there’s a company or organization out there that’s just the right fit for you.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1] Codeup Alumni Portal. (May 31, 2020). Resumé — Ednalyn C. De Dios

This article was first published in Towards Data Science‘ Medium publication.

Fundamentals of Data Science: Transforming Data into Action

  • Data
  • Information
  • Knowledge
  • Intelligence
  • Action


  • Statistical analysis
  • Regression
  • Classification
  • Clustering
  • Time series analysis
  • Anomaly detection
  • NLP
  • Distributed ML
  • Graph analysis
  • Recommender systems
  • Neural networks / Deep learning

Data Science Pipeline

  1. Planning
  2. Acquisition
  3. Preparation
  4. Exploration
  5. Modeling
  6. Delivery
  7. Maintenance

Fundamentals of Data Science: History and Future of Data Science


Capability increased due to decreasing cost of data storage, cpu, and bandwidth.

Demand increased due to large amount of data being generated.


  • Demand for talent
    • “The future is so bright, Ada would need shades” – Joseph Burton
  • Emerging subdisciplines
    • Machine Learning Engineer
    • Data Visualization Engineer
    • Data Journalist
    • Big Data Engineer
  • Continued reduction in technical learning curve
    • Automation around machine learning and data wrangling
  • Ethics
    • risk of discrimination in “Black Box” models
    • machine learning can be used for bad as well as for good

Fundamentals of Data Science: What is Data Science?

Data science understood through

  • vocabulary
  • industry leaders
  • myths
  • data products


An interdisciplinary science and supported by data (digital representation of information), Data Science combines formal science (Math, Logic) and applied science (Sociology, Stats, Computer Science).

It provides actionable intelligence via testable explanations, predictions, interactive intelligence, and intelligent machines.

Industry Leaders

“… a hybrid skill set that combines analytical, statistical, development and engineering skills that enable a team to provide value insights, and direction to people.”

Ann-Jinette Hess, Data Scientist/Manager @ Rackspace

“… equal parts hacker, stats geek, and entrepreneur.”

Chris Chapo, Data Scientist @ Analytical-Solution

“…detecting patterns that can then be used to help people make better decisions.”

Alice Zhen, Data Scientist/Manager @ Amazon


  1. Data Science == Statistics
    • Used in data science but it’s only a small part of it
  2. Data Science == Business Analyst
    • light on decision science and heavy on KPI reporting
  3. Data Science == Data Science
    • no common understanding between hiring managers, recruiters, and applicants
  4. Data Science curriculum is consistent across educators
    • different curricula
  5. If I want to be a data scientist, I just need to learn how to use R or Python.
    • autoCAD does not make an architect


  • Recommenders – YouTube, Netflix, Social Media, Pinterest, Amazon
  • Optimization – UPS No Left Turn Project
  • Advertising – how to make people click ads
  • Social Services – The Crisis Text Line
  • Cyber Security – account takeovers, fraud detection