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Introduction to Data Science with Python Part 2
The term “data science” has become a ubiquitous and all-encompassing term to address any field that utilizes data analytics in one form or another. Despite having such a broad mandate, certain elements remain consistent in the approach to data science that are interdisciplinary: data acquisition, data exploration, data modeling, and data communication. In this two-part workshop series, we introduce data science approaches that are fundamental regardless of your discipline.
Part 1 will focus on how to get, clean, structure, and explore data. Part 2 will focus on how to use data to build models and convey meaning. Along the way, you will learn how to use the most popular tools of the trade today (Jupyter Notebooks and Python) and how to advance your research by exploiting open-source libraries such as pandas, matplotlib, seaborn, and scikit-learn.
There are no pre-requisites for this workshop other than an interest in Data Science.