We require all students to be on a MacBook made no earlier than 2017, capable of running the most recent operating system and with at least 8 GB of RAM. This is not something you need for the admissions process but you will be responsible for it on the first day of class. You do not need to buy any books or software, though! Our custom curriculum is all online.
We have had students use Chapter 30, 35, and 33, as well as Vocational Rehabilitation Benefits to help cover their tuition costs for our Web Development and Data Science programs. After acceptance, you’ll work with our Financial Aid & Enrollment Manager, who is our in-house School Certifying Official, to see what benefits you have available to you and how those can fit into your tuition plan.
No, Pell Grants are designed for conventional educational institutions like colleges. We’re not college!
Visit our Scholarships page for more information on eligibility and application! We award scholarships about two weeks before a class begins so you must be on the roster by then to be eligible. Learn More
Yes! We have generous grant partners,and loan partners with very flexible payment plans - if you don't want to pay during the program and for 6 months after, you don't have to! We also partner with the Department of Veterans Affairs, and we award scholarships. After acceptance, you’ll be connected with our Financial Aid & Enrollment Manager who will help you explore all the various options available to you.
Yes. We take a $1,000 deposit to lock in your space in the program. This is your registration deposit. If you end up not joining the class, this is refunded to you. If you do join the class, this goes towards your tuition payment.
There’s no one-size-fits-all answer here, but generally, data scientists spend about 70% of their time on data wrangling, which is the phase of the data science pipeline that focuses on gathering, cleaning, and preparing data for use. Another 30% of their time is spent on model development, and the remaining time is spent on post-deployment efforts like upkeep, visualizations, storytelling, etc.
You’ll graduate with a full technical portfolio and professional portfolio. You’ll have a busy GitHub, Kaggle, and Data.World profile, as well as a number of projects from the classroom like visualizations, cleaned data sets, reports, presentations, models, and more. You’ll also complete a real-world capstone project, which will be great to present to employers.
We’re focused on helping graduates find jobs as data scientists! But depending on your background and what you like, you can also find work as a data engineer, data analyst, machine learning engineer, and related roles.
Data science is everywhere, including cyber security, SaaS, banking, and retail. Big companies like USAA, Accenture, HEB, and Booz Allen Hamilton work in this realm, as do smaller agencies like Quickpath Analytics.
Most graduates will work as entry to mid-level Data Scientists. Depending on background experience and interest, they may also focus on specific sections of the data pipeline, like Data Engineering or Machine Learning Engineering.
Yes, we will grade your quizzes, projects, and code and you’ll receive a Certificate of Completion. However, we believe (and our employer partners agree) that what matters is what people can do — not what grade, diploma, or label they have. What you're really graduating with is a new skillset and a portfolio to show off to employers!
Each student will graduate with a working, well-engineered project completed. This might be writing a Twitter clone or doing the same for another well-known website. If you were sponsored by an employer, you may produce a project of value for them. We work with you to identify an appropriate capstone project.
Developer Day is the day a Web Development student graduates, presents their project to classmates, Codeup staff, and our employer partner network, and begins their job hunt! You’ll start working on locating employment while continuing with our post-graduation curriculum. This is a set of challenges that you do while interviewing and applying for positions. We also encourage you to work on side projects with fellow grads. Even after graduation, we have weekly meetings, continue to offer career coaching, and our Placement Team is still working hard with and for you. Our job doesn't end until you have one!
Students work at a range of companies, from small startups to large corporations. Our biggest hiring partners are USAA, Accenture and HEB, but we have plenty of grads go to smaller companies out of Geekdom and around downtown.
Most graduates become entry-level professional programmers with titles like Web Developer, Front End Developer or Software Engineer. Others get tech roles such as Quality Assurance, Documentation Engineer or Support Engineer. Some graduates use their newfound knowledge to take tech-related roles like Sales Engineer or Project Manager.
Many applicants have told us they want to start their own business. If a student decides to start or operate their own business and/or do consulting, they are no longer eligible for the tuition refund guarantee.
At your disposal, you'll have career coaches, and essentially, your own recruitment team, all working to get you to a job you love. We do two things: 1) We “teach you how to fish.” We work with you on how to manage your career at a high level and also drill down on the mechanics of how to market yourself to employers. This includes things like creating resumes, building online profiles, managing social media, and nailing your interviews. You can use this information for the rest of your career. 2) We “help you find fish now.” We have dedicated staff members whose job is to find currently available job positions, build and maintain our employer partner network, and connect our graduates with potential employers.
Email us at firstname.lastname@example.org or give us a call at 210-802-7289! You’ll talk with our Employer Partnerships Team to discuss how we can best fit your needs. You’ll be first in line to see our graduates, and we’ll prominently display your logo on our website alongside our other partner employers.
The value of Codeup includes gaining the technical knowledge and skillset of a web developer or data scientist, expert instruction, hands-on curriculum with a portfolio to show employers, a close family of your Codeup classmates, a network of technology professionals to connect with, career preparation, continuing job search support even after you graduate, and the peace of mind that if you don't get hired within 6 months of graduating, you'll get your tuition back (codeup.com/refund). We also offer a great Return on Investment, one of the best you'll find.
The average salary for data scientists across experience levels in San Antonio is about $110,000. At an entry-level from our program, we project initial earnings to range between $65-$80K, depending on background experience.
You’ll graduate with our Certificate of Completion, which is like our diploma. However, we believe (and our employer partners agree) that what matters is what people can do — not what grade, diploma, or label they have. What you're really graduating with is a new skillset and a portfolio to show off to employers!
Yes! As an alum or staff member, if someone you refer completes 75% of our data science program, we’ll give you $500. Make sure they put your name on their application, or you won’t get credit! In an effort to continue cultivating inclusive growth within the tech industry, we are now offering an exclusive Women in Tech referral program to Codeup alumni. If a woman you refer completes 75% of our data science program, we’ll give you $1000. Make sure they put your name on their application, or you won’t get credit! *Women in Tech referral program cannot be used in combination with any other existing referral or promotion
The data science toolkit is constantly changing, and varies shop by shop, but will likely include: Python (and packages like Sci-kit Learn, Pandas, and Seaborn), SQL, R, Tableau, Hadoop, Hive, Jupyter Notebooks, and Github, all of which you'll learn at Codeup!
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 Expdertise Development, Career preparation, and a Capstone Project. To view our full curriculum, click here.
The average salary of a Web Developer across all levels is around $75,000i n San Antonio. As an entry-level Web Developer, the average starting salary will be around $50,000.
On top of learning how to code and develop a full-stack application, we will talk about development approaches, pair programming, debugging, good practices, and more. Our instructors (and our founders!) have been on real-world software engineering teams, so we know our stuff. Finally, we will work with you on interview and presentation skills so you can wow potential employers. View our full curriculum here.
Programming is a world in constant flux. What's popular today will likely not be ten or even five years from now. Instead of worrying about a particular stack, we focus on turning you into a well-rounded professional developer. We also teach you how to learn, so you’ll be set to go learn and work on whatever technology an employer throws at you. That being said, we are in close communication with our employer partners to constantly improve our curriculum, and we hold Technical Advisory Boards to ensure that we're staying up to date and meeting industry demands.
All classes are currently held online via Zoom with live instruction alongside your classmates! We will remain fully virtual for the foreseeable future, which allows students from all across Texas to join us. When it's safe to go back in person, our San Antonio classes will be held at 600 Navarro St, where we have two floors with five classrooms. Our Dallas classes will be held at the Novel Coworking space on 701 Commerce Ave, where we have one classroom and 2 offices on the first floor. Our Houston campus is located at 720 Rusk St.
We keep the ratio of students to instructors as low as possible. Usually, each cohort of 20-28 students has 2-3 instructors.
It is best that you do not work during any of our Codeup programs. Instruction time is from 9am-5pm daily. We have morning (8am-9am) and afternoon (5pm-6pm) study hall with instructors on-hand everyday of the week. Then, there is the homework and labs. Our job is to make you a kick-butt Developer or Data Scientist in a short period of time, so that means it is going to be intense. We won’t tell you what you can or can’t do, but we highly recommend limiting outside engagements during the program. Work and personal commitments tend to be the biggest interruptions to successfully completing our program!
You'll spend the half-hour before class preparing for the day at our virtual Office Hours or reviewing yesterday's notes or projects. Class starts at 9 am CT on Zoom. You’ll spend about half your class time in live lecture with our expert instructors, and the other half in supervised, supported project time. The curriculum is very hands-on, so even with virtual classes, you'll be working on projects with classmates while instructors and teaching assistants are available to help. Lunch starts at 12:30 pm and class resumes at 1:30 pm. At 5 pm, you’re done with class, but you're not done learning! Most students keep practicing and working on projects for a few hours each day to apply and absorb what they've learned. Students may choose to continue working together on Zoom, and instructors are available for virtual Office Hours for an hour after class.
Both of these tools can get the job done, and you’ll get a different answer depending on who you ask. While R is historically dominant, Python has emerged as the programming language for data science, and you’ll see more and more companies with job listings that allow for, if not prefer, Python. Python has a robust set of data science libraries like Pandas, Matplotlib, Scikit Learn, and Seaborn, all of which you'll learn at Codeup.
Supervised and unsupervised are two groups of machine learning methodologies. In supervised learning, a data scientist teaches the algorithm what conclusion to arrive at using a known set of possible outputs. In unsupervised learning, a computer identifies patterns without human guidance. More often than not, data scientists work with supervised learning algorithms.
Whenever you see “data” in a job title, you’re working along the data pipeline ranging from capturing high volumes of data to building machine learning automations. A data scientist spans this spectrum, while other roles focus on one phase or another. A data engineer, for example, focuses on capturing and storing data sets for others to work with. A machine learning engineer only works with automation and model deployment. A data scientist might work on data collection, data processing, future event prediction, machine learning, and more. Check out this blog to learn more about the data science pipeline and the different career roles within it!
Data scientists do complex work, which non-technical co-workers might struggle to understand. A data visualization is a visual representation of data that is easy to digest.
The data science process has about 7 steps: 1) data wrangling (getting and cleaning data); 2) exploratory data analysis, statistical inference, and data visualization; 3) feature engineering; 4) development of a predictive model (training, evaluating, optimizing, testing); 5) model deployment; 6) delivery of results (report, story, visualization); 7) model maintenance. For a visual of this process using credit card fraud detection as an example, click here.
Machine learning (ML) is what it says – your machine learns as it works! ML algorithms allow your computer to generalize decision-making beyond the specific data set it has worked with, and allows for broader automation. Check out our blog post "What is Machine Learning" for a more in-depth description!
In its simplest term, this is a family of machine learning methods based on learning data representation, as opposed to task-specific algorithms. Deep learning allows an automatic feature detection in place of manual feature engineering.
Big data differs from in-memory data, in that it is too large and complex to manage on a local computer. It’s defined by the big V’s: Velocity (data that’s collected at great speed), Volume (large amounts of data), Variety (different forms of data), and Veracity (uncertain quality of the data).
Our admissions process includes assessments to gauge your understanding of basic statistics and Python programming. However, these skills are something you can build during the admissions process. If you’re interested in data science, we encourage you to go ahead and apply. From there, our Admissions Team will work with you to figure out where your skills currently are and how to prepare for the program.
We interviewed dozens of employer partners and practitioners to build our program, and discovered something surprising: the ability to communicate your work is one of the most important skills for a data scientist! Of course, you need to be competent in math, stats, Python, and other tools/technologies. However, what separates a successful data scientist is the ability to make their work digestible, relevant, and actionable.
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.
Nope! We care more about skills and attitude than a piece of paper!
Our ideal candidate is motivated, professional, and enjoys solving problems! We want people who are hungry to learn, ready to work hard, and excited to work as developers!
The admissions process includes the application process as well as the financial aid and enrollment process. Your application process can take around 2-3 weeks, though this varies based on each candidate's availability. Financial aid and enrollment can take about the same amount of time - this is where you'll be working on your tuition plan (with help from us!) and pre-work assignments in prep for class. Altogether, we suggest trying to apply about a month and a half before your desired start date.
No! We take candidates from all different types of backgrounds: recent high school or college graduates, transitioning military, career changers, and many more. However, you must be 17+ years of age.
Nope! We do have students who come through with coding experience and Computer Science backgrounds. However, we also have students who are really successful without having prior technical experience! That is why we have the admissions process that we do - to be sure our program is a good fit for you. To further ensure that you'll be successful, accepted applicants complete about 30 hours of pre-work to prepare for the program.
Web Development and Data Science are both in-demand fields, but they’re very different! If you’re considering coming to Codeup, we recommend you think about these two questions: 1) What is your technical background? Our web development program doesn’t require any background, and has a faster ramp-up to admission. Data Science requires introductory experience in math, stats, and programming. 2) What do you want to be working on daily? While some parts of the jobs will overlap, like writing in a programming language, most everything else is different. Web development will train you to build websites and applications, while Data Science will train you to draw insights from data sets. Still not sure? Talk with our Admissions Team!
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 2017. 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.
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.
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.
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.
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!
Not exactly. For both our Web Development and Data Science 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.