Codeup Grads Win CivTech Datathon

A screenshot of the winning project for the 2020 CivTech Datathon, created by Codeup Data Science grads.

Many Codeup alumni enjoy competing in hackathons and similar competitions. Now that we train Data Scientists, recent alumni have been competing in datathons, too. At the 2020 CivTech Datathon, teams from two Data Science cohorts, Bayes and Curie, took 1st and 2nd place! The Codeup community is killin’ it at these events and we can’t wait to highlight their achievements!

 

What is CivTech Datathon?

CivTechSA is a partnership between Geekdom, a coworking space that every current Codeup student has complimentary access to, and San Antonio’s Office of Innovation. In an effort to connect local communities, ideas, and data to help improve the City’s services, the CivTech Datathon competition was born! Using public datasets, competitors look for missing data and areas for improvement and identify impactful solutions to current civic problems. Then, they present their insights to City of San Antonio departments like San Antonio Water System (SAWS) and Via Metropolitan Transit.

Codeup Alumni Take First and Second Place in 2020

Boasting first place was the Curie team We Came, We SAWS, We Conquered with Ryan McCall, David Wederstrandt Sr., Chase Thompson, Cameron Taylor, and Jeremy Cobb. They used data from SAWS regarding sanitary sewer overflow (SSO) events and weather data from the National Oceanic and Atmospheric Association to predict the root causes of SSO events. They generated a system that SAWS could use to prioritize maintenance of the sewers to limit the risk of these events, with the potential to save hundreds of millions of city dollars while keeping gastrointestinal health risks at bay.

Taking second place was team Get on the Bus with Sean Oslin, Sara Pena, Fredrick Lambuth, Misty Garcia, Kevin Eliasen, and Faith Kane. With their project, they aim to increase ridership of public transportation. Using open data from VIA and the Census Bureau Data API, this team identified areas of improvement to make our community more diverse and equitable by making buses more accessible.

We Won in 2019, Too

As a special shoutout, at the 2019 CivTech Datathon, a team from our very first Data Science cohort competed and won the “Most Solvable” award. The team members were Ednalyn C. De Dios, Joseph Burton, and Sandra Graham. Their project was similar to our Curie team – they trained a model to predict which pipes would overflow.

Want to Compete Next Year?

If you’re passionate about improving civic issues and want to present your findings to city stakeholders too someday, Codeup can teach you how! You might actually inspire lasting change (or at least get thousands of dollars in prize money)! If these Codeup students could win first and second place at the datathon, what’s stopping you? Click here to learn more about our Data Science program. Then, we’ll see you at next year’s CivTech Datathon!

From Slacker to Data Scientist: Journey to Data Science Without a Degree

From Slacker to Data Scientist - Ednalyn De Dios shares their experience of getting 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. 

____________________________________________________________________________________________________________________________________

I don’t have a PhD. Heck, I don’t have any degree. Still, I am very fortunate to work as a data scientist in a ridiculously good company. Here’s how I did it (with a lot of help).

 

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 Montgomery GI Bill to 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 up 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 at the kitchen table. It had been more than a year since my non-profit job and I was nowhere near close to 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 was 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 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 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.

My love for data science was born.

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 in the mirror and acknowledged that I needed help. But I wasn’t sure where to look.

Then one day out of the blue, my girlfriend asked me what data science is. I jumped off my feet and started 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 a billboard. We went for a drive and I 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 outcomes.

I was sold.

 

Preparation

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. But, I did them anyway. I spent day and night going over the courses and materials, did the tests, and got the certificates!

 

Bootcamp

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

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 important than the specific tools, I learned: 

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

 

Job Hunting

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

Codeup helped me with all of these.

In addition, Codeup also helped prepare me for both technical and behavioral interviews. The student placement team taught me how to optimize answers to highlight my strengths as a high-potential candidate.

 

Post-Graduation

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

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 learned to keep moving.

 

Conclusion

It took me 3 months after graduating from Codeup to get a job. When I got the job offer, I felt very grateful, relieved, and excited.

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

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This blog post was written by Ednalyn C. De Dios for Towards Data Science: A Medium publication sharing concepts, ideas, and codes. An edited version is being shared on Codeup with permission from the author. You can reach them on Twitter or LinkedIn.

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If you’d like to learn more about how Codeup can help you launch your career in data science, schedule a call with our team today or reach out to admissions@codeup.com!

Students Discuss Their Transition into Data Science

Event Recap: Jada Shipp, Daniel Guerrero, and Ryan McCall share their Data Science Student Experience

Our Transition into Data Science Panel event was full of passion. The audience met three of our current students: Jada Shipp, formerly a Newborn Hearing Specialist, Daniel Guerrero, who was a Vaccine Product Manager, and Ryan McCall, who was an overnight Surveillance Agent at Walmart. They discussed what led them to data science, how their experience has been so far, and what they plan to do after Codeup. Read on to learn about the Codeup data science student experience and their key takeaways and advice!

 

Why data science?

The students began by discussing their “whys” and “hows” of pursuing data science at Codeup.

Jada: I was going to apply to medical school but decided it wasn’t for me. A friend suggested Codeup, which is how I learned about data science. I realized it’s something I was already interested in. I was really big on solving all my problems in school on Excel and coming up with the best graphs and digging around in numbers. I just didn’t know there was a name for this, and certainly didn’t know it was a career path.

Daniel: At a new job as a Vaccine Product Manager, they started asking me to do research about where we’re selling our product and who’s buying. I quickly discovered this was a massive, billion dollar company and there was no one there that actually knew how to use Excel or how to look at sales. It was mind blowing to me because I thought this was standard and everyone was doing it. Then I realized my company is not the only one that has this gap.

Ryan: I went to college for chemistry, then started working security but wasn’t satisfied with it. I wanted to use my brain more to think and actively solve problems at my job. So, I started looking at programming as a job career. I found data science and absolutely fell in love with it and spent two years trying to teach it to myself on the side of my security job. It was very hard, I spent most of that time figuring out what resources to trust. It’s a lot easier being taught than searching in the dark.

 

What is the learning process like at Codeup?

Lots of us know how it feels to come out of a full semester of class feeling like we didn’t absorb anything and not knowing how it applies to real life. Not here.

Jada: I thought getting admitted was a mistake and they’d kick me out at any time. I had zero coding experience and I really didn’t think I could do it. But I went step by step. ‘Maybe I can’t do this but I can probably do this one tiny part of it.’ They’ll explain it in as many different ways as they need to until your lightbulb goes on. 

Daniel: They’ll give you questions that force you to think. They won’t just give you an answer. You have to figure it out yourself but they give you the tools to be able to do that. And because of the collaborative environment, we’re learning from each other as a cohort and not just instructors. Now, I’m able to branch out into new territories and not be scared to do so because I’m well equipped.

Ryan: I’m honestly amazed by what we can do in such a short amount of time. I can do things that I thought it would take me years to do.

 

How are you liking Data Science at Codeup?

In such an immersive, fast-paced environment, it was a joy to hear how much our students have loved their experience so far.

Jada: Everything we do every week, I’m like “This is so cool! Last week was nothing!”

Daniel: Every week I have the same conversation with my parents about the program. “What are you doing this week?” “Oh it’s my favorite thing!” “You say that every week!” But other than everything, the projects are the coolest thing for me.

Ryan: We could talk about this for hours, we love this stuff! Aside from loving the material, you become like a little family. We’ve had all the same bad experiences, all the same good experiences. You’re speaking a similar language and you can figure it out together.

 

How are we helping you get the job you want?

In between lessons, students work closely with our placement team, where we help students land awesome jobs.

Jada: I want to use data in health care. I have a degree in Public Health and worked as an EMT, a scribe, and a hearing screener. Now, I want to merge my passion for public health with data science skills. I’m pretty confident I can get a job like this because the Codeup placement team helps you through every step– resume, Linkedin, interview skills. They are not gonna let you fall by the wayside. They remember you and who you are and what you’re looking for. It’s not a generic cookie cutter process where you get what you get. They know you and it’s customized for you.

Daniel: I told the placement team that I want to work with data that’s business to customer. Two days later they sent me applications and they’re actually what I asked for, in industries I actually told them about, with companies I actually mentioned. I was mind blown! The placement team will open as many doors as they possibly can to get you the right opportunity, I am more than impressed with the placement team.

Ryan: I want to be a data scientist and I don’t know what kind of data scientist, I just want to be one! And they can work with that, too!

 

Any tips for incoming students?

All three panelists said the same thing: DO THE PREWORK! 

 

Here are some resources they used:

 

We want to thank Jada, Daniel, and Ryan for sharing their passion with us and acting as mentors for prospective and current students for the night. They are each blossoming into data scientists that couldn’t hide how much they love what they do even if they tried. Do you want a similar transition? Start today by learning more. Scared it’s too risky and you won’t get a job? That’s okay, we’ll refund your tuition if you don’t. Any more excuses? There won’t be once you give us a call.

From Bootcamp to Bootcamp: Two Military Veterans Discuss Their Transition Into Tech

From Bootcamp to Bootcamp: Jeff Roeder and Benny Fields share their Codeup experience

Are you a veteran or active-duty military member considering your next steps? Our alumni have been in your boots. In a recent virtual panel, two vets discussed their transition into technology careers with Codeup: Benny Fields III, a retired Air Force Master Sergeant turned Full Stack Web Developer, and Jeffery Roeder, a Navy Intelligence Analyst turned Data Scientist. Whether you’re interested in Data Science or Web Development, here are some key takeaways from the event. 

Why Codeup?

“The GI Bill was a huge plus, but the icing on the cake was the placement program.” – Benny Fields

After retiring from the Air Force, Benny Fields took a job as a technical writer, but he quickly became more interested in the software he was writing about than the writing itself. His friend suggested looking into a coding bootcamp, which he did. He liked that Codeup accepts the GI Bill and the icing on the cake for him was learning about the work our student placement team does to get you hired.

What does Codeup’s Student Placement Team do?

“They’ll give you every imaginable tool to get placed. They have tons of connections- it’s crazy. Colleges aren’t gonna do that for you.” – Jeff Roeder

We’ll buff up your resume, set up mock interviews, and give you the know-how to nail your interviews and get a job offer. From how to dress, to what to say in a thank you letter, Jeff said it best: we’ll give you every imaginable tool to get placed in a new career. And it’s backed up by our tuition refund

How did you pass the technical assessments during the application process?

 “They basically tailored their workshop to me. That’s the kinda stuff that Codeup’s gonna do, they’ll get you there!” – Jeff Roeder

Jeff Roeder heard about Data Science classes at Codeup from a friend who had seen our “crazy billboards” (one of which features Benny). He’s a former intelligence analyst, but the admissions process wasn’t entirely a breeze for him. After studying and studying, Python just wasn’t clicking, and he failed one of his technical assessments. He was about to give up on it, but our admissions team wasn’t ready to give up on him. We personally invited him to one of our Saturday workshops where we taught him step by step how to build the foundation he needed. 

How does your military experience relate to your experience at Codeup?

“They were like ‘hey, you’re gonna learn Spanish and you only have six months to do it,’ which is much like going to Codeup to learn something new, you only have six months to do it.” – Jeff Roeder

When Jeff first joined the Navy as a linguist, he was told to learn Spanish in six months. When he joined Codeup, he was told to learn Data Science in five months. They were both immersive experiences where people of different personalities and different cultures joined together for a common goal. As Jeff put it, you may not always like someone or relate to them, but you need each other to accomplish what needs to get done.

“Coming to Codeup, I had to be flexible because I had to learn to adapt to new technologies with new people that were way younger than me and were catching on faster.” – Benny Fields

For Benny, one similarity is that in the Air Force, you have to be flexible. He was used to having a planned day and changing it at the drop of a hat. In the tech field, things are always changing, and flexibility and constant learning are essential. This is where the military flexibility really came in handy for him.

Jeff and Benny may have completed different Codeup programs but they both had the same journey from bootcamp to bootcamp. Both failed their technical assessments at first and had to leverage our resources to get through the admissions process. They both worked with our financial aid team to use their VA benefits for the course, and they both left Codeup with a job and a new skillset.

Mission accomplished.

 

If you’re looking for your next step and want to learn more about using VA benefits to attend Codeup, talk with our team today! And don’t miss our next virtual event – check out our calendar at codeup.com/events!

How to Get Started On Any Programming Exercise

Graphic header for blog "How to start any programing exercise", with photo of Codeup instructor, Ryan Orsinger

Programming is hard. Whether you’re just beginning to learn or you’ve been programming for years, you’re going to run into roadblocks and get stuck. Our Data Science Instructor, Ryan Orsinger, has seen 36 cohorts of students come through Codeup and helped build their problem-solving skills through live, audience-centered lectures. Check out his recipe for success below:

Scenario:

You’re learning to code, learning the syntax for a programming language, and working on thinking programmatically. The lesson or lecture is completed and now you’re now facing a programming problem that is expecting you to understand and apply the new content.

How do you get started?

 

Here’s your algorithm for getting started:

1. With intent, read the curriculum and the code examples.

2. Go back and deliberately read the example code very closely and slowly.

3. Copy any example code into your editor.

    • Identify the pieces of syntax that you recognize.
    • Identify the code for the new concept that you’re working with.
    • Ask yourself how the syntax or concepts you know already support and connect with the new topic or new syntax. Often, the new is relatable in terms of the old.
    • Ask yourself questions about the code example
      • What is this entire code example supposed to do?
      • What piece of the language is this new concept?
      • Is the new code a new piece of syntax? Or is it an existing piece of syntax?

4. Run the example code

5. Observe results. Think about each piece of code. What is it doing, what did you expect it to do?

6. Try modifying the example code so that you change variables to see different results (one at a time…)

If the example code demonstrates how to make a loop from 0 to 9:
– Modify the code to make a loop that starts at 1 and ends at 10.
– Modify the code to make a loop that starts at 10 and counts down to 0.

7. Try removing as many moving pieces from the code for the new concept as possible… try to isolate a unit of work that uses the new concept and test it in isolation

8. Read the first exercise problem. Read it slowly, with attention to detail.

9. Ask yourself questions about the exercise:

  • Can you explain or restate the problem in plain English?
  • Are you able to write down the steps from problem to solution in English, without using any code?
  • Break the exercise down into pieces. Each piece is either something you’ve seen or it’s new.
  • Given the concept for this lesson, identify which part of the exercise uses the new topic
  • For the new piece, what is similar between the exercise code and the example code for the lesson?

10. Work to write code for a smaller problem than the exercise asks.
If the exercise says:
– Prompt the user for a number between 1 and 50
– If the input is not numeric or out of that range, ask them again for a number. Repeat until they give a number between 1 and 50.
– Start by making sure you’re able to prompt a user, then store the result of prompt to a variable for later.
– Go after the low-hanging fruit first. Momentum begets momentum.

11. If you’re still having problems and stuck, go to step 1.

12. If friction, confusion, and “writer’s block” persist, then ask for help from another human being. Explain the steps you’ve already taken, and attempt to ask your question as clearly as possible. Here’s a good resource on how to ask effective questions!

 


Ryan Orsinger is a proud instructor here at Codeup. Check out his personal blog for more insightful information here!

 

 

 


 

If you were inspired by this article and have any questions about our programs, give us a call. We’d love to chat.

The Best Path to a Career in Data Science

Blog header image "best path to a career in data science"

In our blog, “The Best Path To A Career In Software Development,” we looked at how bootcamps provide a more direct path to a career than traditional undergraduate CS degree programs. Today we’re here to talk to you about how bootcamps provide a more direct path to a career in data science than a Master’s Degree. 

“But don’t I need a degree to get a job as a data scientist?” As a non-traditional educational model, we hear this concern a lot. The answer is yes and no. Yes, because most jobs in data science require some form of higher education. No, because that degree doesn’t have to be in something related to data science – data scientists have degrees in many different areas of study! So if the concern that lacking a piece of paper will prevent you from accelerating your career, fear no more.

While a Master’s Degree provides a lot of value, it isn’t the most direct path for a job. With the exponential growth in data generation and the race to keep up with storing and processing that data, data science no longer sits at the fringe of an ultra-specialized workforce. Companies now need much larger teams to analyze, model, and leverage the data they’ve collected. So while the field of data science might have once only been available to those skilled in highly academic algorithm development, it’s now a playground for those with some Python skills who know how to find valuable insights in a mess of data. 

 

Now let’s get a bit more specific. If modern roles in data science demand more practical skills, why is a bootcamp a better path?

 

#1: Responsive curriculum: Barely 10 years ago nobody had heard of data science. But in that short amount of time, the tools and technologies in the field have grown exponentially. Each year sees the introduction of new packages, visualization tools, and cutting edge technologies. With such a rapidly evolving landscape, it’s hard for traditional learning environments to keep pace. With our ears tuned directly to employers, we’re able to adapt quickly and ensure we’re teaching what hiring managers need.

 

#2 Hands-on Project-Based Learning: Have you ever watched Top Chef or The Great British Bake Off? You were probably pretty entertained, but how did that seared Ahi Tuna with orange mint avocado salsa and balsamic vinegar reduced amuse-bouche turn out? The sad reality is, watching experts do their thing doesn’t make you an expert. Nor does listening to lectures. Our program is built around the concept of praxis, which is essentially the practical application of theory, or the blending of theory and practice. Half of your 670 program hours are spent actually writing code, so you develop the muscle memory and experience of programming. A career in data science is like an old-time trade, like becoming a blacksmith: you have to learn from masters and practice, practice, practice. 

 

#3 Progressive Curriculum Structure: In a traditional degree, students study by taking several classes at a time. You may begin with data structures and algorithms, then move to SQL, then take Python, and so on. But real-world data science doesn’t work so neatly. You will never face a project where you’re only working with one of those tools, so this pedagogical method is misaligned with career demands. Our program focuses on real-world deliverables at every step of the journey, while exposing you to increasingly complex problems and projects. You start off applying basic tools to simple challenges. Then, we begin varying the data sets, the way you access that data, the type of methodology you use, and the deliverable you’re responsible for. To put it simply, the structure of a traditional degree teaches you how to use a hammer, a saw, and a chisel. Codeup teaches you how to build a stool, a birdbox, and a sculpture with those tools, and when to use which. 

 

#4 Job Placement Services: If education is your goal, stop reading now. If a career is your goal, then you’re in the right place. Most graduate institutions have career service offices where you can get advice on your resume and attend job fairs. But Codeup makes you a promise: get a job after graduation or get 100% of your money back. There are no two ways about that: our singular focus is your outcome. Unlike traditional institutions, we sell jobs, not education. 

 

#5 Messy Data: This is probably the most important difference between us and traditional degrees. We use real, messy, misleading, broken data so you learn how to draw insights from the real thing. Unfortunately, that is not the norm. Because of the segmented class structure, traditional degrees have to focus on using data that teach one specific skill. At Codeup, you’re always applying your tools to a real deliverable, so we’re able to use real data sets that intersect the challenges of multiple skills. 

 

Lastly, we encourage you to think about the return on your investment in your education.

Codeup*Private Master’s
  • $27,500
  • 5 months
  • 85% graduation rate
  • 88%  employment rate
  • $67,500 median starting salary
  • $62,280
  • 18-24 months
  • 61% graduation rate
  • 72.5% employment rate
  • $59,866 median starting salary

*read more on our outcomes

 

Most importantly, the opportunity cost of pursuing a master’s degree is equal to 13-19 months of employment. At a median salary of $67,500 from Codeup, that’s between $67,500-$101,250 in foregone earnings. 

So, you want a career in data science? A career accelerator like Codeup is the path for you. Still not convinced? We’re here to hear your concerns – contact us and let’s talk it through.

Getting Hired in a Remote Environment

Graphic that explains the purpose of blog; How you can get hired in a remote environment

As a career accelerator with a tuition refund guarantee, we have always been focused on employment outcomes for our students. Going Remote hasn’t changed that! We thought we’d pause today to explain how. 

 

First of all, our career placement services are built on one-on-one relationships. Our Employer Partnership Managers work with students individually to develop a professional portfolio, define a strategy, and conduct a job search. And they don’t let off until you’ve signed that offer letter! Since we’ve gone remote, our placement team has digitized their curriculum so it’s accessible to all our students, and they’ve continued working one-on-one over Zoom. 

 

Those one-on-one relationships aren’t exclusive to students. It’s the same approach our team takes with their network of hiring managers and recruiters. From curriculum advisory panels to guest speaker lunchtime talks, we involve employers as often as we can. We forge a personal relationship that encourages repeat hiring, open communication, and trust.

 

Lastly, your job search kicks off with a bang in our staple Developer Days and Data Scientist Days. Normally, these are in-person demonstrations of capstone projects that end in a reverse job fair with employers. On April 16th, we hosted our first-ever virtual Developer Day. Over 160 people tuned into it live! Not only did we maintain the quality of the event, but we increased attendance and visibility. That event, especially while remote, kick starts your job search, connects you with employers, and increases your visibility as a candidate.

 

In person or remote, we remain committed to empowering life change and helping our students land jobs in new career fields. 

If you’ve been affected by COVID-19 in any way (layoffs, health, family, etc), check out our recently announced COVID-19 Relief Scholarship.

The Remote Codeup Student Experience

Codeup Remote Classroom Experience

Communities across Texas have now lived in a remote environment for weeks. While we hoped for good news early on, Governor Abbott’s recent announcement to close schools for the remainder of the school year has confirmed that the new normal amid COVID-19 is remote. A lot of prospective students are left asking: what now? Our answer is simple: little has changed! While we are now conducting class digitally, the rest of the Codeup experience looks the same. Here’s how!

Codeup differs from traditional degrees and online programs because we offer live, full-time, and immersive instruction. Even now, our classes are taught by a team of passionate instructors whose backgrounds range from 20+ years of industry experience to Master Degrees in Adult Education. Each class is led by 2-3 expert instructors and supported by a Teaching Assistant. They deliver live lectures, respond to you in real-time, provide 1:1 support, and individualize your learning to set you up for success. Comparing us to an online boot camp is like comparing a virtual reality experience to a regular TV show. We’re still giving live instruction, we’re just teaching from behind a laptop instead of a podium.

In addition to live instruction, our students enjoy live co-learning. You’re not in this alone! In fact, our cohorts of 20-25 students provide a built-in structure of camaraderie, networking, and technical support. Some of the best learning you can do is by teaching to and learning from other students, a benefit that is lost in attending a part-time or self-paced program.

The combination of live instruction and peer-peer connectivity produces a third benefit: accountability. In this remote world where your couch is your desk, it can be challenging to stay focused and motivated. Our immersive program builds accountability through a daily course schedule, project-based learning, social support, and a helping hand. [Editor’s note: if this weren’t important and learning to code on your own were easy, I’d be writing Java right now instead of a blog post!]

Lastly, the reason Codeup offers a 100% tuition refund guarantee is that we teach directly to the needs of employer partners. Right now, more than ever before, employers need employees who are adaptable and able to work remotely! The remote Codeup experience prepares you to communicate, collaborate, and code remotely, a skill that was already valuable in the tech industry. If anything, you’re getting a little extra bang for your buck by learning a skillset most developers have to earn the hard way.

Although we wish we were on campus high-fiving, writing code, and re-enacting Star Wars lightsabers battles, we’ve realized that’s not really what the Codeup experience is about. The true Codeup experience is about learning to code from experienced professionals alongside passionate students in a live, hands-on environment that offers a 100% tuition refund guarantee. That’s what you can always expect from Codeup, whether we’re remote or not.

If you’ve been affected by COVID-19 in any way (layoffs, health, family, etc.), check out our recently announced COVID-19 Relief Scholarship.

2019: A Codeup Year In Review

Codeup in 2019

It’s official – 2019 has come and gone and we’ve hit 2020 fast! At the start of this new year, we like to reflect on 2019, giving gratitude to everyone who supported us and celebrating our many shared victories. It was a year of life changes, ground-breaking firsts, and growth.

After such a big year for our community and students, we wanted to share some of our reflection highlights with you! Here’s what happened in 2019:

2019 Firsts

  1. Launched San Antonio’s FIRST and ONLY Data Science career accelerator
  2. Graduated San Antonio’s first class of Data Scientists
  3. Expanded our program to Dallas, TX
  4. Named San Antonio #2 Best Place to Work

By the numbers

  • 129 # of individuals who changed their careers at Codeup
  • 574 total # of Codeup alumni network
  • 16 # of alumni placed in Dallas
  • 25% military veteran students
  • 27% female students
  • 52% racial and ethnic minority students
  • $189,053.81 amount of scholarship funds given by Codeup to its students
  • 49 # of partner companies who hired from us
  • $6,327,295.40 salaries earned by Codeup grads in their first jobs as Software Devs and Data Scientists

Our mission is to empower life change. If 2019 wasn’t your year, maybe 2020 will be 🙂 Reach out to us – we’d love to help you create your future!

How To Pick A Coding Bootcamp Curriculum

How to Pick A Coding Bootcamp Curriculum

If you’re thinking about entering a career as a software developer, you’ve probably researched a few different bootcamps. During your research, you’ve probably seen a few different curricula. Without already BEING a software developer, it’s hard to know what’s what. In this post, we want to explore how to think about a bootcamp curriculum and recommend strategies about how to consider the best fit.

Let’s start with some terminology. Full-stack web development integrates work on both the front-end and the back-end. The front-end is the user-facing side that you interact within a web browser. The back-end is the server-side that involves the sending and receiving of data. Consider a restaurant website. A front-end only website would show a restaurant menu with prices, dishes, and ordering information. A full-stack web application would allow you to not only view the menu but place an order and process payment information for that order, interacting with a database and back-end functionality.

Within that understanding, there are a few groupings of technologies:

  • Object-Oriented Programming and back-end tech: This list includes programming languages like PHP, Java, C#, Ruby, and Node.js. These allow you to build functionalities into a web application. 
  • Database tools: Tools like MySQL, MongoDB, PostgreSQL, SQL Server, and Oracle let you store, send, and receive information.
  • Front-end technologies: Languages and frameworks like JavaScript, Angular, React, HTML, and CSS let you design a front-end interface.
  • Web frameworks: Spring Boot and Laravel are examples of web frameworks that help you stand up web applications more efficiently. 
  • Testing tools: In production, many companies leverage a methodology called Test Driven Development. This is when developers write tests first, and code second, letting them compare their code against a standard of approval. Common technologies include JUnit, PHPUnit, NUnit, MSTest, Jasmine.

With so many technologies out there, it can be hard to pick what’s best to learn. But here’s the secret: the specific technologies do not matter. The most important thing you’ll learn during a coding bootcamp is how to use these different categories of technologies. Whether you learn PHP or Java, MySQL, or  SQL Server, the important takeaways are the fundamental concepts learned. Many Codeup alumni graduate from our Full-Stack Java program and go on to work in PHP, Python, Ruby, Groovy, and other languages. Ultimately, a loop is a loop and an array is an array. Languages differ, but once you’ve learned an OOP language, the differences become syntactical instead of conceptual. 

This leads us to an important point: the more technologies, the worse! The quality of a curriculum, and thus the value of it, is not defined by the number of technologies covered. In fact, it’s the opposite. Let’s give some examples.

Columbia University is one of the premier academic institutions in the world. They are an Ivy League university with a strong reputation. They recently expanded into the bootcamp space and launched a web development program that covers the following technologies: HTML5, CSS3, JavaScript, jQuery, Bootstrap, Express.js, React.js, Node.js, Database Theory, MongoDB, MySQL, Command Line, Git, and more. All of that in 12 weeks. Let’s decode that for you with the terms we’ve already used. This curriculum promises to teach you:

  • Object-Oriented Programming and back-end tech: Node.js
  • Database tools: Database Theory, MongoDB, MySQL
  • Front-end technologies: HTML, CSS, jQuery, Bootstrap, Express.js, React.js

Now let’s look at Codeup. We teach: HTML, CSS, JavaScript, jQuery, Java, Spring, MySQL. All of that, in 20 weeks. In the terms we’ve discussed, that’s: 

  • Object-Oriented Programming and back-end tech: Java, Spring
  • Database tools: MySQL
  • Front-end technologies: HTML, CSS, JavaScript, jQuery

The common initial thought is: why spend 20 weeks learning seven technologies when you could spend 12 weeks learning 10? And there lies the misconception. Many bootcamp curricula promise to teach you the latest and greatest technologies: React.js, Angular.js, Express.js, MongoDB, Node.js, etc. etc. etc. That may sound like a better bang for your buck, but it’s all a question of priority. Here is the reality of your choices:

  • Columbia bootcamp, broad and shallow: gain exposure to a wide variety of technologies in a short amount of time
  • Codeup, narrow and deep: gain expertise in software development fundamentals in a narrow scope of technologies

There is no inherently right answer here – it’s all about your priorities. That being said, here’s what we believe: Learning how to learn, learning how to think like a developer, and learning to program is far more important than gaining exposure to the latest web frameworks. When you understand programming fundamentals, you prepare yourself to learn whatever you want. It’s like learning how to work with a car: it’s great to know how to drive an Audi, but it’s pretty different from understanding how an Audi engine works and how it differs from a Honda. 

At Codeup, we focus on crafting you into a software developer. We focus on programming fundamentals, core web technologies, and applied practices. When you graduate, you’ve landed a job and have the skills to learn any technology. If that sounds like what you’re looking for, connect with our Admissions Team and we can tell you more! 

Click here to hear our Codeup Alumnus, Po Lin’s, story about his journey graduating with a Computer Science degree and how he supplemented Codeup’s curriculum to launch a career into software development!