Low-Code Data Analysis with Jupyter Notebooks
In this workshop, we will introduce our Data Analysis Cookbooks, a set of open-source Jupyter Notebooks that can be used for qualitative and quantitative data analysis without an extensive knowledge of programming. Cookbooks provide low-code, templated building blocks for assessing and addressing data quality issues, performing simple statistical analyses, identifying correlations, and visualizing data.
Our focus for the workshop will not be to make everyone a data scientist in a day (obviously) but to empower participants to do more with data in less time, whether from qualitative user interviews, log files, or surveys. Participants will get introduced to Python, Pandas, MathPlotLib, and Jupyter Notebooks through a series of hands-on exercises that emphasize adaption of Cookbooks and recipes instead of custom code creation.
Requirements:
Laptop
Anaconda (free download), Python Version 3.7
Outline
* Introduction to Data Analysis, Machine Learning, and Data Science
* High-level overview and framework to situate the workshop activities
* ‘Just Enough to Get By’ Introduction to Python, Pandas, and Jupyter Notebooks
* Just enough because the cookbooks will contain much of the needed code snippets and how to use them, so participants only need a few basics to get started
* Data Assessment, Cleansing, and Preparation
* Best practices for assessing and cleansing data
* Know your data
* Metadata types
* Data quality metrics
* Manual data assessment
* Hone your data set
* Demo and hands-on exercises using Python, Pandas and Jupyter Notebooks
* Load data
* Combine data sets
* View data table and columns
* Manage missing data and empty values
* Validate data values
* Data Analysis
* More demos and hands-on
* Python techniques for slicing strings
* Isolating and counting values
* Simple statistical analysis using Pandas and NumPy to calculate:
* Mean
* Median
* Variance
* Ranges
* Creating correlations
Each of the hands-on analysis sections will include simple data visualizations using MathPlotLib and guided discussion on interpreting results
