<p>Lecture, three hours; discussion, one hour. Requisites: courses 203A, 203B, 212A. Introduction to data science tools in Python. Topics include Python programming, Jupyter Notebook for literate programming, NumPy and SciPy for scientific computing, pandas for data analysis and manipulation, Matplotlib and other libraries for data visualization, scikit-learn for machine learning, and deep learning libraries. S/U or letter grading.</p>

Biostatistics

Jonathan and Karin Fielding School of Public Health

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BIOSTAT 203C - Introduction to Data Science in Python

Introduction to Data Science in Python

MS3: Employ computational methods of applied regression to analysis of biomedical data sets

Master one version control tool for reproducible and collaborative research

Develop efficient data base management storage strategies

MPH2: Contribute to the analysis of public health studies in collaborative multidisciplinary teams

Employing Python concepts for modifying and combining data bases

Understand different modes of parallel computing and know parallel computing utilities in Python

Master Python tools for data ingestion from various sources and data bases

Creating effective statistical tables and graphics for summarizing public health data

MPH4: Contribute to the analysis of public health studies in collaborative multidisciplinary teams

In homework assignments, students will practice Python programming, importing big public health data sets from text files, web and data bases, visualizing data, implementing interactive apps for exploring data, performing data analytics on big and messy data, and designing and running simulation studies using parallel and cloud computing

Understand principles of reproducible research and learn how to prepare and present statistical analysis results using Python

MPH5: Prepare written and oral presentations providing public health insights based on statistical analyses

MS5: Communicate results of biostatistical research both orally and in writing

General Catalog

Introduction to Data Science in Python

BIOSTAT 203C

4 units