Home
(UA Biosciences Research Labs. Image by author, CC)
Here you will find a collection of getting started Workshops prepared by the staff of the Data Science Institute.
- Introduction to the Command Line Interface Shell
- Unix Shell - Command Line Programming
- Introduction to Github Wikis
- Introduction to Github
- Github Wikis and Github Pages
- Introduction to Docker
Python is the top programming language used in general and in Data Science. This workshop for beginners, focuses in skill development in data wrangling for carrying out an exploratory data analysis using the basic Python libraries NumPy and Pandas and the visualization libraries Matplotlib and Seaborn.
- Use Jupyter Notebooks in Google Colab as an integrated development environment for programming in Python.
- Recognize a basic set of Pandas functions for data manipulation and visualization.
- Implement basic plots with Matplotlib and Seaborn: relational plots, distribution plots, categorical plots, multiplot grids.
- Interpret Time Series analysis and forecasting limits.
- Introduction to Python for Data Science - RezBaz AZ 2022.
- Jupyter Notebooks
- Pandas for Data Analysis
- Exploratory Data Analysis with Python
- Low-code Data Exploration Tools
- Outlier Analysis and Anomalies Detection
- Data Visualization with Python
- Introduction to Time Series Analysis
- Low-code Time Series Analysis
- Time Series Forecasting
- Overview of Machine Learning Algorithms
- Overview of Deep Learning Algorithms
- Digital Learning Resources Library. Data Science Institute. The University of Arizona.
- Data Learning Resources. SESYNC, University of Maryland.
Created: 01/22/2022 (C. Lizarraga); Last update: 04/17/2023 (C. Lizarraga)
- Introduction to the Command Line Interface Shell
- Unix Shell - Command Line Programming
- Introduction to Github Wikis
- Introduction to Github
- Github Wikis and Github Pages
- Introduction to Docker
- Introduction to Python for Data Science - RezBaz AZ 2022.
- Jupyter Notebooks
- Pandas for Data Analysis
- Exploratory Data Analysis with Python
- Low-code Data Exploration Tools
- Outlier Analysis and Anomalies Detection.
- Data Visualization with Python
- Introduction to Time Series Analysis
- Low-code Time Series Analysis
- Time Series Forecasting
- Overview of Machine Learning Algorithms
- Overview of Deep Learning Algorithms
- Introduction to Machine Learning with Scikit-Learn
Carlos Lizárraga, Data Lab, Data Science Institute, University of Arizona.