Data Science VS Data Analytics: What’s The Difference?

Data Engineer Infographic | Code Tech Bootcamp

By Dimitri Antoniou

A week ago, Codeup launched our immersive Data Science career accelerator! With our first-class kicking off in February and only 25 seats available, we’ve been answering a lot of questions from prospective students. One, in particular, has come up so many times we decided to dedicate a blog post to it. What is the difference between data science and data analytics?

First, let’s define some of our terms! Take a look at this blog to understand what Data Science is. In short, it is a method of turning raw data into action, leading to the desired outcome. Big Data refers to data sets that are large and complex, usually exceeding the capacity of computers and normal processing power to deal with. Machine Learning is the process of ‘learning’ underlying patterns of data in order to automate the extraction of intelligence from that data.

 

 

Now, let’s look at the data pipeline that data scientists work through to reach the actionable insights and outcomes we mentioned:

  1. We start by collecting data, which may come from social media channels, network logs, financials, employee records, or more.
  2. We then process that data into usable information stored in databases or streamed.
  3. Next, we look back on the history of that data to summarize, describe, and explain, turning the data into meaningful knowledge. Here we’re primarily using mathematics, statistics, and visualization methods.
  4. Now we convert that knowledge into intelligence, seeking to predict future events so that we can make decisions in the present. This is where practitioners will introduce mathematical/statistical modeling through machine learning to their data.
  5. Finally, we enable action by building automations, running tests, building visualizations, monitoring new data, etc.

Data professionals work at different stages of the spectrum to move data through the pipeline. On the left, Big Data Engineers specialize in collecting, storing, and processing data, getting it from Data to Information. In the middle, analysts work to understand and convert that information to knowledge. Lastly, a Machine Learning Engineer utilizes machine learning algorithms to turn intelligence into action by building automations, visualizations, recommendations, and predictions.

Data Scientists span multiple stages of this pipeline, from information to action. They will spend about 70% of their time wrangling data in the information stage. They will conduct a statistical analysis to derive knowledge. Lastly, they predict future events and build automations using machine learning.

For those technical folk out there, data science is to data engineering or machine learning engineering as full-stack development is to front-end or back-end development. For the non-technical folk, data science is the umbrella term that houses data analytics, machine learning, and other data professions.

So what’s the biggest difference between a data analyst and a data scientist? Data scientists utilize computer programming and machine learning in addition to mathematics and statistics.

Still have questions? Reach out to us. Wondering which of Codeup’s programs is right for you? We’ve got you covered. And of course, if data science gets you excited, get started with us today!

Debugging with Codey the Rubber Duck

Cody the Duck mural

By Jennifer Walker

I first encountered rubber duck debugging while attending Codeup. Each student had a duck at their seat on the first day of our 18 ½ week advanced full stack web development boot camp. The expression of each duck varied, but they all stared quietly and blankly at us as we took in our surroundings with excitement and anticipation. At the time, I had no idea what it was for or why we needed that bath time friend. Now I do.

Part of what I love about programming is the problem-solving. However, in the attempt to figure out a software solution the developer very often can get tunnel vision – stuck on solving a problem the same wrong way or just get stumped with no real direction. This is where rubber duck debugging is the most useful. It originated from a book called “The Pragmatic Programmer” by Andrew Hunt and David Thomas.

Rubber duck debugging is simple. It includes taking the problem you are trying to solve and explaining it out loud to the rubber duck. This may seem silly because most of us have not talked to inanimate objects since we were kids. However, by doing this it forces a developer to think in a different way and to look at an issue under a microscope. Talking out loud activates a different part of the human brain, which very often helps the developer solve a problem without ever talking to another person. It keeps us from wasting our own time and the time of others when the solution is right at our fingertips.

I have experienced this phenomenon myself when I try to explain aspects of programming to people who are not programmers. It forces me to think of programming in a different way. I have to lose the acronyms, and just speak plain English to a person who isn’t such an avid techie like myself. When I do that I walk away with new knowledge and a refreshed passion for what I have been discussing.

Codey, Codeup's Mascot
Photo: Photos By Marvin Pfeiffer / Staff Photographer

I also learned later that the rubber duck is the official mascot for Codeup. His name is Codey and he has a special meaning to me beyond just rubber duck debugging. I began to sketch him on the first day of class. Over time he began to express my struggles and triumphs while learning to code. He came alive for me and became a student of Codeup alongside me during my time there. If I had a bad day, he totally understood because he was in the fire with me. If I struggled to understand a concept, he got that too and listened while I talked to him about what I was trying to do. Now, as a proud graduate of Codeup working as a software developer at a fantastic company, I keep Codey with me. He is in my car and at my desk at work. He also sits on my desk at home – waiting patiently to hear my struggles and what I am trying to solve for that day.

If you do not have a rubber duck for debugging, I suggest you go out and get one!

From the Service Industry to Software Development

By Randi Mays

Randi Mays

For many teenagers, the path to self-reliance starts in one of two places: a restaurant or retail store. Until it’s time to begin a professional career, you’re working that part-time job stocking shelves, helping irate customers with expired coupons or prepping for the dinner rush. I’d venture to say I’m one of the very few who was sad to leave that lifestyle behind.

I worked in the food service and retail industries for 10 years before I attended Codeup. I took great pride in my work every day; I couldn’t go home until everything was near perfect: my work area spotless, the shelves neatly stocked and everything ready for the next shift. When it came time to leave the service industry and move on to professional work, I was initially reluctant. I had found great personal fulfillment and success in customer service. Why would I want to leave?

I have big dreams. Of course I want to travel the world, spending my vacations in exotic destinations, trying new foods, seeing centuries-old architecture, and making lasting memories. But more importantly, I wanted to work for a company with a more widespread mission than gastronomic satisfaction. I wanted to work alongside people with a passion for their work that ran far deeper than a paycheck.

After graduating from Codeup in September 2016, I began working for USAA as a software developer and I can tell you–the company is no stranger to giving. Each employee receives company paid volunteer hours and I used some of mine to volunteer at the San Antonio Food Bank among dozens of other USAA employees. Last year when Hurricane Harvey hit, USAA was quick to organize several volunteer sessions at their home campus to prepare food and other basic necessities to be delivered to people in need. They even have a system where I can automatically deduct a specified amount from my paycheck to give to charitable causes I am passionate about. I have heard story after story about their representatives on the phone going above and beyond their duties to serve members in combat zones and at home. I can’t enumerate here all of the reasons I admire USAA for its community involvement and caring, but I’m sure I’ve made my point.

There are times I look back on my experience in food service and retail nostalgically, remembering how I excelled in those positions and enjoyed the repetitive work. Then I come back to the present and remember how big an impact my employer makes serving the military community and their families, and how many lives are changed by the work I do with my team. I find great personal satisfaction and pride in my work every day, and I am just getting started.

Codeup’s Data Science Career Accelerator is Here!

Data Science description and diagram | Code Tech Bootcamp

The rumors are true! The time has arrived. Codeup has officially opened applications to our new Data Science career accelerator, with only 25 seats available! This immersive program is one of a kind in San Antonio, and will help you land a job in Glassdoor’s #1 Best Job in America.

Data Science is a method of providing actionable intelligence from data. The data revolution has hit San Antonio, resulting in an explosion in Data Scientist positions across companies like USAA, Accenture, Booz Allen Hamilton, and HEB. We’ve even seen UTSA invest $70 M for a Cybersecurity Center and School of Data Science. We built a program to specifically meet the growing demands of this industry.

Our program will be 18 weeks long, full-time, hands-on, and project-based. Our curriculum development and instruction is led by Senior Data Scientist, Maggie Giust, who has worked at HEB, Capital Group, and Rackspace, along with input from dozens of practitioners and hiring partners. Students will work with real data sets, realistic problems, and the entire data science pipeline from collection to deployment. They will receive professional development training in resume writing, interviewing, and continuing education to prepare for a smooth transition to the workforce.

We focus on applied data science for immediate impact and ROI in a business, which is how we can back it all up with a 6 month tuition refund guarantee – just like our existing Web Dev program. We’re focusing on Data Science with Python, SQL, and ML, covered in 14 modules: 1) Fundamentals; 2) Applied statistics; 3) SQL; 4) Python; 5) Supervised machine learning – regression; 6) Supervised machine learning – classification; 7) Unsupervised machine learning – clustering; 8) Time series analysis; 9) Anomaly detection; 10) Natural language processing; 11) Distributed machine learning; 12) Advanced topics (deep learning, NoSQL, cloud deployment, etc.); 13) Storytelling with data; and 14) Domain expertise development.

Applications are now open for Codeup’s first Data Science cohort, which will start class on February 4, 2019. Hurry – there are only 25 seats available! To further our mission of cultivating inclusive growth, scholarships will be available to women, minorities, LGBTQIA+ individuals, veterans, first responders, and people relocating to San Antonio.

If you want to learn about joining our program or hiring our graduates, email datascience@codeup.com!