Data Science Program_

Program currently available only in San Antonio.

Upcoming
Classes

SAN ANTONIO
START:
July 13

SAN ANTONIO
START:
December 7
Apply Now

SAN ANTONIO
TBD
Apply Now

Become a Data Scientist_

Businesses are relying on data insights and actionable intelligence more than ever to make informed decisions. As a Data Scientist, you will turn data into stories and informed presentations that will drive their success. Learn exactly what your future employer needs and become a critical business asset in just 20 weeks.

Upcoming Virtual Learn to Code Workshop_

Interested in launching an exciting career as a data scientist? Curious on how you can get started? Start by joining us at our next virtual learn to code workshop, where our instructors will cover the basics of Python! Get a feel for a day in the life of a Codeup student transitioning careers with us. Tickets for our FREE workshops go quickly, so grab your tickets today!

The Curriculum_

  • Fundamentals
  • Applied Statistics
  • SQL
  • Python
  • Regression
  • Classification
  • Clustering
  • Time Series Analysis
  • Anomaly Detection
  • Language Processing
  • Dist. Machine Learning
  • Advanced Topics
  • Storytelling with Data
  • Domain Expertise Dev.
  • Career Simulation
  • Capstone Project
  • Fundamentals of Data Science

    What does a Data Scientist do? Learn the types of questions that data scientists answer every day, and how they answer them. Discover how to use the data science pipeline and machine learning methodologies to provide value to businesses and their decision makers. These include statistical and mathematical concepts, Python, Jupyter Notebooks, Linux / MacOS Terminal, SQL, Microsoft Excel & Google Sheets, Tableau, Git, and Hadoop technologies. Prerequisite: Admission to program

  • Applied Statistics

    Learn how to use applied statistics to solve practical problems. Decide what data to collect and how to collect it. Then, analyze and interpret your data and present your results. Explore the fundamentals of applied statistics in Data Science using Excel, including measures of central tendency, tests of significance, common distributions, and variable independence. Prerequisite: Fundamentals of Data Science

  • SQL

    How your data is stored matters. Use MySQL to gather, parse, clean, aggregate, and store data. Learn how to read data from SQL databases and perform basic joins, aggregates, and group-bys. Write tables, export data, and explore database structure and schema. Prerequisite: Applied Statistics

  • Python

    Learn to use Python to make raw data more valuable for analytics through, data wrangling, data analysis, data visualization, and machine learning. Explore how to use packages such as Pandas, NumPy, SciPy, Scikit Learn, and Matplotlib. Prerequisite: SQL

  • Regression

    Learn to prepare data with regression algorithms to predict numerical events. You will build on your previous skills from past modules to gather and prepare, parse, package, and analyze data from a relational database. You’ll learn new skills such as indexing, selecting, plotting, and linear regression, as well as how to evaluate the performance of regression models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case.

  • Classification

    Learn to predict data classes with classification algorithms. Gather data from multiple source types such as csv and SQL. Develop a classification model to predict categorical events. Explore topics such as grouping, aggregations, computational tools, text data, missing data, DataFrame objects, plotting, data visualization, and how to evaluate classification models. By the end of this module, you’ll deliver a final report and model predictions from a practical use case. Prerequisite: Regression

  • Clustering

    Use unstructured machine learning and clustering algorithms to find unknown patterns in data. Use clustering algorithms to identify similar groups using Python and Scikit-Learn and explore how they can be used for further analysis and modeling. Prerequisite: Classification

  • Time Series Analysis

    Use structured machine learning to employ forecasting methods and predict events over time.. You will practice with a practical application and raw data to learn how to develop, evaluate, and improve performance of the model. Explore concepts such as time dependency, accounting for seasonality, and how to effectively split data into training and test sets. By the end of the module, you will develop a time series model and deliver a final report, model, and predictions. Prerequisite: Clustering 

  • Anomaly Detection

    Learn how to detect rare or anomalous events.Students will learn methods for detecting rare or anomalous events. You’ll practice building an anomaly detection model using Python. Explore streaming and unstructured data (such as logs), regular expressions, and how to apply data science to cyber security. Prerequisite: Time Series Analysis

  • Natural Language Processing

    Use Natural Language Processing techniques to perform common tasks such as sentiment analysis and topic modeling. Use Python’s NLTK package (or an equivalent) to analyze the sentiment of tweets related to a particular subject. You will also learn how to access data from a public API, such as Twitter. Prerequisite: Anomaly Detection

  • Distributed Machine Learning

    Learn how to access distributed data from cloud platforms. Work through the data science pipeline — from gathering data through the deployment of a machine learning model. You will apply previously learned machine learning methodologies using technologies such as Spark and Hive. Understand the Hadoop framework and how to access data to develop data products, such as machine learning models. Prerequisite: Natural Language Processing

  • Advanced Topics

    With a solid understanding of the fundamentals, experience advanced data science topics such as model pipelines, A/B testing in machine learning, graph analysis, recommendation engines, R, deep learning, deployment of production models to the cloud, and NoSQL databases. Learn use cases, key concepts, and resources for diving deeper into these topics. Prerequisite: Distributed Machine Learning

  • Storytelling with Data

    Presenting your findings to colleagues is a crucial part of a Data Scientist’s work. You will learn best practices for storytelling, visualizations, presentations, calls to actions, and more. Explore important factors to adapt and appeal to various types of audiences, such as visual design. Use tools such as Matplotlib and Seaborn packages, Javascripts D3 library, R’s ggplot2 library, and Tableau and deliver a presentation advocating a recommendation based on findings. Prerequisite: Advanced Topics

  • Domain Expertise Development

    Data Science is often defined as the intersection of programming, mathematics & statistics, and business or domain expertise. However, most data scientists will switch industries during their career, and the ability to adapt quickly into a new domain is critical to maintaining the data science trifecta. In this module, you will learn frameworks for learning what domain knowledge is most relevant to their work and for quickly acquiring the skills needed to start adding value. Prerequisite: Storytelling with Data

  • Career Simulation & Preparation

    Throughout the program, you’ll learn a variety of skills related to career preparation and professional development. By the time you graduate, you will possess soft skills such as team-building and communication, as well as career development skills such as resume writing, online branding, and interviewing. We also teach best practices in project / time management, ethics, big data architecture, and portfolio development in Kaggle, Data.world, and Github. Prerequisite: Fundamentals of Data Science

  • Capstone Project

    Work in a small team to complete a real-world capstone project that uses each skill you’ve learned throughout the course. Prerequisite: Domain Expertise Development

A Day In The Life_

  • 8:30AM
  • 9:00AM
  • 9:10AM
  • 11AM
  • 12:30PM
  • 12:30PM
  • 1:30PM
  • 4:50PM
  • 5PM
  • 8: 30AM Get Prepared

    Before class, grab yourself a free cup of coffee and a snack, review the upcoming curriculum, practice some programming exercises, and hang out with your classmates!

  • 9:00AM Get Started

    At 9 AM, Instructors will get you warmed up with morning announcements, review problems, and challenge questions.

  • 9:10AM Lecture

    Instructors will guide you through the day’s new content through a combination of overview, explanation, live coding demonstrations, and practice problems.

  • 11AM Lab

    At the end of each lesson, the class will work through a set of exercises to practice and reinforce the content they’ve just been taught. Instructors and students collaborate to problem solve, give feedback, and complete the problem set.

  • 12:30PM Lunch

    From 12:30-1:30, enjoy your hour lunch in our student break area or at a nearby downtown restaurant.

  • 12:30PM Tech Talk

    During Thursday lunches, we bring in a guest speaker to talk about the tech industry. You’ll hear from developers, recruiters, hiring managers, and Codeup alums.

  • 1:30PM Presentations

    Presenting your findings in front of colleagues is crucial in the data science world. At the end of each module, you’ll have the opportunity to model and present data that showcases your new-found skills. Practice makes perfect!

  • 4:50PM Break

    Wrap up the class day at 5 PM with some review, Q/A, and heads up for the next day.

  • 5PM Study

    Spend an hour reviewing the day’s content and practicing more problems. Or, meet up with fellow students or alumni to form a study group or practice presentations.

DOWNLOAD OUR OUTCOMES REPORTS_

Looking for a new and exciting career opportunity in tech? Check out our data science reports to learn more about how you can launch a career as a data scientist with Codeup! 

Guided Admissions Process_

Our guided admissions process is the perfect time to find out if Codeup is the right fit for you and your goals. Our students come from all walks of life with a variety of educational backgrounds and work experiences, and there is no programming experience required for our Data Science program. If you are a motivated, professional, critical-thinker, we’re excited to meet you.

Explore
Submit your free application, speak to our team, and visit our campus.
1
Qualify
Complete your admissions testing and interview to gain admission.
2
Enroll
Secure your seat, work through our free pre-work, and get ready for class!
3

Financial Opportunity_

In the past year, 88% of our students have received financial assistance. Discover a variety of financial solutions and scholarship opportunities to fund your future.

Do You Have Questions?_