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).

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.

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.

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.