Learn fundamental concepts of data science in preparation for dealing with the challenges of data analytics.  Gain knowledge of Python programming basics and Python libraries for data analysis.  Learn data wrangling techniques, such as data preparation, cleaning, reshaping, and manipulating data.  Develop knowledge of data visualization techniques, and incorporating statistical data analysis methods, including probability distributions, sampling, estimation, and hypothesis testing.  Explore machine learning and other special topics, such as geospatial data analystics and causal inference. NOTE: Credit can be obtained for only one of CS 2545, STAT 1001, DATA 1003.