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.
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.
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.
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.
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.
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.