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 like USAA, Accenture, HEB, and Booz Allen Hamilton work in this realm, as do smaller agencies like Quickpath Analytics.

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 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.

What is the Data Science process?

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.