Month: January 2019

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.

Hook

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

Bait

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

Switch

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

Introduction to Data Analysis – Part III

This is a continuation of Chapter 1 summary of Python Data Analytics by Fabio Nelli. Click here for Part I  or here for Part II.

Quantitative and Qualitative Data Analysis

QUANTITATIVE: numerical & categorical, quantitative predictions, more objective conclusion. QUALITATIVE: textual visual audio, qualitative predictions, more subjective conclusion.
Quantitative and Qualitative Data Analysis

Open Data

Introduction to Data Analysis – Part II

This is a continuation of Chapter 1 summary of Python Data Analytics by Fabio Nelli. Click here for Part I.

The Data Analysis Process

Data analysis is nothing more than a sequence of steps:

  1. Problem definition
  2. Data extraction
  3. Data preparation: Cleaning
  4. Data preparation: Transformation
  5. Data exploration and visualization
  6. Predictive modeling
  7. Model validation/test
  8. Deployment: visualization and interpretation of results
  9. Deployment: deployment of solutions
problem, extraction, preparation, exploration & visualization, predictive modeling, model validation , deployment, solution
The Data Analysis Process

Problem Definition

“Data analysis always starts with a problem to be solved.” A study of the system is conducted and is designed to be able to make informed predictions or choices.

“Building a good team is certainly one of the key factors leading to success in data analysis.” Fabio recommends an effective cross-disciplinary team.

Data Extraction

As much as possible, sample data must reflect the real world. In addition to data selection, extracting and using the best data sources is another issue to keep in mind.

Data Preparation

Data preparation comprises of obtaining, cleaning, normalizing, transforming, and optimizing a data set. Although it may seem that data preparation is less problematic, it actually requires the more resources and more time to be completed. Potential problems includes data values that are ambiguous, missing, replicated, or out of range.

Data Exploration/Visualization

Exploring data involves “searching the data in graphical or statistical presentation to find patterns, connections, and relationships. Data visualization is the best tool to highlight possible patterns.”

Summarization is the process where data are reduced without sacrificing important information. Clustering is used to find groups united by a common attributes. Another step of analysis focuses on identification of relationships, trends, and anomalies in the data.Other methods of data mining automatically extract important facts or rules from the data.

Predictive Modeling

Predictive modeling is used to create or choose a statistical model that predicts the probability of a result. The purpose of these models is to make predictions about the data values and to classify new data products.

The models can be divided into three types:

  • Classification models: if the result is categorical
  • Regression models: if the result is numerical
  • Clustering models: if the result is descriptive

Some of the methods include linear regression, logistical regression, classification and regression trees, and k-nearest neighbors.

Some models explain the characteristics of the system under study in a clear and simple way while some models have limited ability to explain the characteristics of systems but still make good predictions.

Model Validation

Validation of the model is the test phase. Data is called the training set when used to build model. It is called validation set when used to validate the model.

Comparing data enables us to evaluate the error and estimate the limits of validity.

This process allows you to numerically evaluate the effectiveness of the model and compare it with other existing models.

Deployment

This is the final step of the analysis process which aims to translate the result into a benefit. Normally, it consists of “writing a report for management or for the customer who requested the analysis.”

In the report, the following topics are discussed:

  • Analysis results
  • Decision deployment
  • Risk analysis
  • Measuring the business impact

We’ll conclude this summary by discussing quantitative/qualitative data analysis and open data sources in part III.

Introduction to Data Analysis – Part I

The following is my attempt to summarize the first chapter of the book, Python Data Analytics by Fabio Nelli.

– E.C. De Dios

According to Merriam-Webster, data is “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.” I usually just think of it is as anything that can be recorded or measured.

In the book, Fabio makes the distinction that “data actually are not information” and that “information is actually the result of processing.” He then proclaims that data analysis is the “process of extracting information from raw data.”

Data Analysis

“Data analysis allows you to forecast possible responses of systems and their evolution in time.” Its aim is not the mathematical models themselves but the quality of the its predictive power.

The search for data, their extraction, and preparation are also part of the data analysis process because of their importance in the critical role and influence in the success of the results.

All stages of data analysis employ different techniques of data visualizations. It’s all about the charts!

Knowledge Domains of the Data Analyst

Fabio also points out that data analysis is a multi-disciplinary field and is “well suited to many professional activities. He adds, “a good data analyst must be able to move and act in many different disciplinary areas.”

Not only is it necessary to know other disciplines, it is also imperative that a data analyst know “how to search not only for data, but also for information on how to treat that data.”

Computer Science

Knowledge of information technology is necessary to know how to use the various tools like applications and programming languages which in turn are needed to perform data analysis and visualization.

Mathematics and Statistics

Data analysis requires a lot of complex math. Statistics form the concepts that form the basis of data analysis. Bayesian methods, regression, and clustering are just some of the most commonly used techniques in data analysis.

Machine Learning and Artificial Intelligence

Machine learning analyzes data in order to recognize patterns, cluster, or trends and then extracts useful information in an automated way.

Professional Fields of Application

Better understanding of where the data comes from greatly improves their interpretation. It is good practice to find consultants to whom you can pose the right questions about your data.

Types of Data

Data is divided into two distinct categories:

  • Categorical (nominal and ordinal)
  • Numerical (discrete and continuous)

Categorical data are observations that can be divided into groups or categories. Nominal variables has no intrinsic order while ordinal variables has a predetermined order.

Numerical data are measured observations. Discrete variables can be counted while continuous values assume any value within a defined range.

Next in part II, we will explore the process of data analysis in detail.