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Welcome to the Workshops wiki!
(Image credit: Anita Austvika, Unsplash+)
This series aims to provide a comprehensive introduction to data science using Python tools. Through eight one-hour sessions, you will gain hands-on experience in analyzing various types of data, such as numbers, text, time-series, images, and videos. These workshops are designed to equip you with the necessary skills and knowledge for academic and professional growth in data science. Your participation in these workshops will improve your understanding of the subject and allow you to connect with peers and experts from different fields. Learning Objectives
- To impart a foundational understanding of data science methodologies and techniques.
- To facilitate hands-on experience with Python libraries such as Pandas, NumPy, and Matplotlib.
- To enable graduate students to analyze and interpret diverse types of data, including numeric, textual, time-series, image, and video data.
- Introduction to Jupyter Notebooks |
- Data Wrangling 101: Pandas in Action |
- A Probability & Statistics refresher |
- A Probability & Statistics refresher |
- Data Visualization Libraries: Matplotlib |
- Data Visualization Libraries: Seaborn |
- Exploratory Data Analysis |
- Time Series Analysis |
- Time Series Forecasting |
- Machine Learning with Scikit-Learn |
- Natural Language Processing |
- Deep Learning |
- Prompt Engineering |
- AI Tools Landscape |
- A SQL Masterclass Part 1&2
- Introduction to Big Data with Apache Spark
- How to build data apps using Streamlit
Updated: 10-04-2023
UArizona DataLab, Data Science Institute, University of Arizona, 2024.
