Data Exploration in Python

If you're a fledgling data scientist with only cursory statistical training and little experience with real world data sets, you may feel like you're stumbling around in the dark when you're asked to interpret and present data to decision makers. How do you validate the data? What analytic model should you use? How do you differentiate between correlation and causation? How do you ensure that your data is solid and your conclusions are on target?

Allen Downey, Professor of Computer Science at Olin College of Engineering, author of Think Stats, Think Python, and Think Complexity, provides safe passage around the common pitfalls of exploratory data analysis, so you can manage, analyze, and present data with confidence.

• Learn the fundamental tools and methodologies used in data science
• Discover best practices regarding the ETL (Extract, Transform, and Load) process and data validation
• Use the open science framework: practice version control, replication, and data pipelining
• Grasp the effectiveness of CDFs (Common Data Formats) in visualizing distributions
• Choose the correct analytic model for your data
• Comprehend statistical inference, effect size, confidence intervals, and hypothesis testing
• Discern the relationship between variables: understand scatter plots and scatter plot alternatives
• Understand correlation, linear least squares, linear regression, and logistic regression
• Master the Zen of testing your data and your conclusions