How do Data Scientists spend most of their time?

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 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.

What will I have to show employers as an entry-level Data Scientist?

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.

What kinds of companies employ Data Scientists?

Data science is everywhere, including cyber security, SaaS, banking, and retail. Big companies in San Antonio like USAA, Accenture, HEB, and Booz Allen Hamilton work in this realm, as do smaller agencies like Quickpath Analytics.

Does the referral bonus apply to referral for the Data Science program?

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

What does your Data Science curriculum cover?

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.