Program Preparatory Boot Camps
To ensure students are ready to hit the ground running, we offer four self-paced courses to help our wide variety of students prepare for course material. These complimentary boot camp classes for admitted students develop a basic foundational vocabulary and technical competency in preparation of the official DSA courses. Each of these boot camps will be a continually available resource for students for the entirety of the program.
Python Programming Boot Camp
This course teaches participants how to program in Python, including use of auxiliary libraries in various Python ecosystems. Participants are introduced to the iPython notebooks from the SciPy ecosystem, as well Python’s use across the spectrum of data science courses and topics. Many activities are focused on data ingestion, cleaning, manipulation, and restructuring (e.g., ETL).
Database Basics and SQL Boot Camp
This course introduces participants to the basics of database management systems and structured query language (SQL). It covers the design and development of databases, data loading, access, manipulation, and exportation. In particular, SQL is covered in detail, with brief introductions to using SQL in Python and R.
Introductory Statistics for Data Analytics
This course is an introductory probability and statistics course, providing baseline statistical vocabulary and understanding of incorporating probability and statistics into decision making. Participants are immersed in key statistical concepts including experimental design and data collection, basic statistics, distributions, CTL, etc. Participants also develop an understanding of foundational probability theory, including conditional probability, Bayesian techniques, predictive modeling, stochastic processes, etc. Learning activities continually integrate basic data and statistical visualization techniques and participants are introduced to basic statistical modeling.
R Statistical Programming Boot Camp
This course teaches participants how to program, perform basic statistical modeling, and basic visualization using R and RStudio. Various key libraries and ecosystems are introduced to teach participants how R is integrated across the entire data science curriculum and lifecycle.