- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
- [Michele] Statistics is the science of learning from data, and today, we have so much data that we need to use computers to make sense of it. What is exciting is that, by using computers for statistics, we can often avoid the complex mathematics of textbook statistical methods and replace them with straightforward, brute force computation. This course is not meant to replace a standard introduction to statistics. Instead, I will show you how to use Python to implement key statistical techniques and how to understand statistics better by experimenting with Python and with real-world data sets.
We will use Python's most powerful and broadly adopted packages for math, visualization, and statistics, numpy, Mapio lib, pandas, step models, and PyMC3. We will ingest data, clean it, describe it, and visualize it. I will then introduce the basic techniques of statistical inference and statistical monitoring, including baseline inference. I'm Michele Vallisneri and I'd like to welcome you to this course.
Python: Data Analysis (2015)with Michele Vallisneri2h 16m Intermediate
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
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