Your Learning Experience
Your best course for career transformation. Codeva academy features expert instruction, one-on-one career coaching, and connections to top employers to get you hired.
Fill Out The Form For FREE Classes And To View Codeva Program Packages
Launch Your Career in Data
In just weeks we turn data beginners into data pros by teaching a job-applicable balance between practice and theory. Our “Learn by Doing” training will give you hands-on experience in today’s most in-demand Data Science technologies and methodologies: from data cleaning all the way to advanced machine learning concepts. Enroll today if you’re serious about starting a career in Data!
Use Data Science to Solve Today’s Business Problems
Our Python-based curriculum introduces best practices in machine learning, statistical analysis, natural language processing, and data visualization. You’ll examine case studies and master the tools needed to find a job as a data scientist. For those who want additional experience in Python, we offer an in-person Python Fundamentals course.
Learn Alongside the Brightest Minds
Our highly motivated students come from a variety of backgrounds like data analysis, engineering, and mathematics. You’ll learn to code alongside a cohort of driven peers and develop meaningful connections with our world-renowned faculty and career services team, who come together to help you identify strengths, define goals, and connect you to our 2,250+ hiring partners.
Quarter 1: Python and Statistics Fundamentals
Students jump right into a Python-based curriculum and explore and learn statistical analysis, including frequentist and Bayesian methods. Students master fundamental data science concepts while growing in skill with libraries like numpy, scipy, and pandas. For those who need to learn Python basics, we offer an in-person Python Fundamentals course.
Quarter 2: Machine Learning & Prediction
In the second quarter, we dive into machine learning, working on real problems in classification, regression, and clustering using structured and unstructured data sets. We build a conceptual understanding of each model before practicing with libraries used in the industry.
Quarter 3: Natural Language Processing & Recommenders
Students learn natural-language processing, recommender systems, neural networks, and time-series data. We gain experience with big data and data in the cloud. By the end of this section, students aree well-versed in data science and ready to work independently.
Quarter 4: Capstone Projects & Case Studies
Students work independently on three projects unique to their interests or career aspirations. These “capstone projects” solve real problems using the technical skills students have learned throughout the course. Students also work on several group case studies throughout the program, combining real-world data with what they’ve learned each week while practicing team-based software development.