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

Day #1

Today, I packed my lunch and rode the bus: First day of Data Science!

The instructors are really nice and the staff were really welcoming. Even the veteran students made a point to welcome us. I know we’re going to have a blast this next four months and a half!

I made it to the first day, now the fun begins.

Codeup’s Admission Process

This post is for anyone who’s thinking about applying for CodeUp’s Data Science Career Accelerator program.

Today, I’d like to share my experience with the admissions process to the data science program. I wish I could say that it was pleasurable experience. On the contrary, it was challenging.

Online Application / Campus Tour

First, I filled out the application form online. Then came the email and a call from CodeUp. We setup a time for me to take a tour of the CodeUp campus in downtown San Antonio.

IMPORTANT: Downtown parking sucks. Be prepared to pay $8-20 for parking or make other arrangements instead like public transportation, do a rideshare or get somebody to give you a ride to and from.

I also received an email that contains information about the admission process and the program itself.

IMPORTANT: Read this document thoroughly; there are multiple pages. I made the mistake of not checking and ended up just reading the first page. Needless to say, I made a fool out of myself. Embarrassing!

The tour itself was uneventful but I did ask a lot of questions.

Technical Challenges

The next step comes three challenges. First, I’ve had to write a 300-500 essay answering the question: “Why do you want to be a Data Scientist?” Think carefully because this essay acts as your pitch to be considered admission to the program. I recommend writing in a brief, clear, and concise manner but don’t make it too sterile. They really want to know your story and they want to hear passion. If I had to do it again, I would think of it as writing an epic “origin story.”

Secondly, I’ve had to take on three short technical challenges in Math/Statistics, Python Programming, and Data Analysis. The Math challenge is mostly multiple choice. The Python Programming challenge is a combination of both multiple choice questions and coding. The Data Analysis involves examining a spreadsheet and making a Powerpoint presentation.

IMPORTANT: Study, study, study! Make sure you know:

  • matrix operations
  • charts and graphs
  • probability
  • basic statistics,
  • proportions, ratios, and rates of change
  • solving single variable equations

In addition, make sure you can read and write basic Python code. Don’t forget about those functions, ranges, and list.

All in all, the good news is the exam is open-book and open-internet. You can consult the almighty Google (or Bing, Yahoo, DuckDuck Go, etc.) for a quick reference. The bad news: it is timed; you’ve only got about an hour each to complete both the Math and Python challenge.

Behavioral Interview

Third, the interviews! In my opinion, I think this part’s the hardest. I did two interviews with the CodeUp placement staff. One was over the phone and the other in person.

I narrowly made it.

Here’s what I should’ve done:

  • Exude confidence.
  • Ask meaningful questions about CodeUp.
  • Make the interview about them.

IMPORTANT: Even though you’re the one applying for a position at CodeUp’s program, don’t make the interview session about you. Ask the interviewers some thoughtful questions even if you’ve already asked other people about it and got an answer. Ask them again. Basically, try your damnest to the the interviewers in the hot seat and deflect some of the heat off of you.

Maybe I should’ve done a power pose.

I’ll do better next time, in front of real employers. I proclaim it so.

Hook, Bait, and Switch

Today I’ll share my admission experience with CodeUp.


I first came to know of CodeUp when I saw their billboard about a guy named Mariano. It gave the impression, at least to me, that anybody can be a software developer and that CodeUp can help make it happen.

CodeUp billboard: Coffee barista to Software developer.
Coffee Barista to Software Developer


After some time, another billboard went up to sweeten the pot. “Hired as a Software 1 month before graduating.”

Hired as a Software Developer 1 month before graduating
Hired Before Graduation


Afterwards, the billboard change to one with just two words: Data Science. Now, there’s been a lot of buzz about artificial intelligence, machine learning, and deep learning. While I don’t know a lot, I am aware that data science has something to do with these. I was sold.

Hook Bait and Switch
Hook Bait and Switch