We're always here to help you take the next step.
We are back on campus for in-person learning. Due to limited capacity, we’ve added a couple of virtual classes (can be subject to change.)
No. Since all students will be required to be on campus starting January 3rd, 2022, you must live within driving distance of our Dallas or San Antonio campus by that time.
Your success is our priority, our people are our focus, and our mission is to empower your life change. Here’s a combination of things you won’t find elsewhere:
We do not. We are a career accelerator looking to help adults transition into careers in tech. To enroll, students must be at least 17 years old. Our average student age is around 30 years old. However, we definitely encourage kids and teens to start exploring tech early! We’re active partners with Youth Code Jam in San Antonio, and there are many other great organizations that are dedicated to helping kids learn technical skills.
Cloud Administration, Data Science, Web Development, are all in-demand fields, but they’re very different! If you’re considering coming to Codeup, we recommend you think about these two questions:
Still not sure? Talk with our Admissions Team!
No. Both of our programs are full-time. We believe learning a new skill in an immersive, full-time environment is the most effective way to jump into a new career. Both software development and data science are challenging topics, and to be successful, you’ll need a brain that’s not exhausted from a long day at work. Part-time options simply aren’t as effective as our full-time programs, which ask students to turn their learning into a full-time job.
Not exactly. For both our programs, we have a rolling admissions process and accept qualified applicants on a first-come, first-serve basis. Once a class is full, we’ll start accepting students onto a waitlist. If you’re accepted but the class is full, your acceptance rolls over to the next start date. We also want to be sure you’re prepared and set up for success in class, including having time to get your tuition plan in place and working through our pre-work assignments. This means we generally do not accept new students into a class within two weeks of the start date. If you’re interested, start your application today! Apply now.
After applying online, be on the lookout for an email with next steps. After working through the admissions process, where we very carefully assess each candidate, you’ll receive a phone call from an Admissions Manager letting you know if you’ve been accepted. If at any point you’re not sure of your application status, please don’t hesitate to reach out to email@example.com!
Yes. Students must provide their own Apple laptop capable of running the most recent operating system, with at least 8GB for both programs, and the laptop can be made no earlier than 2018. It doesn’t need to be brand new – many of our students have good luck with refurbished options. You do not need a laptop for the admissions process, you’ll just need it by your first day of class.
Macintosh systems are built on top of Open BSD, which is Linux compatible. Over 90% of our strategic placement partners that hire our graduates use Macs. If there’s a tool that runs on Linux, it will run on a Mac.
No. We learned that students were less engaged in class when they knew the course was being recorded so they could watch later. They had a harder time comprehending the material on their own.
Yes. We primarily use Slack, email and Zoom.
Data Science is a method of drawing insights from data using math, statistics, programming, and business expertise. It usually involves big data sets and automation using machine learning.
Our ideal candidate is motivated, professionally polished, and a natural problem solver. They also have experience with math/statistics, computer programming, and business. However, that person is likely already a data scientist! If you’re hungry to learn, excited about data science, and have some background in any of the above, we think you could be a fit.
No! But a high school diploma or GED (General Equivalency Diploma) is required.
At a high level, we cover the data science pipeline/process, relevant tools & technologies, modern methodologies, example projects, and important questions. More specifically, we have 16 modules: Fundamentals, Statistics, SQL, Python, Regression, Classification, Clustering, Time Series Analysis, Anomaly Detection, NLP, Distributed Machine Learning, Advanced Topics, Storytelling, Domain Expertise Development, Career preparation, and a Capstone Project. To view our full curriculum, click here.