arvindbetrabet / Practical_Statistics_for_Data_Scientists
This repository has the Python equivalent code for the book Practical Statistics for Data Scientists: 50 Essential Concepts, by Peter Bruce and Andrew Bruce. This 1st edition book, by O'reilly Media, is a compact reference that explains 50 of the main concepts, that every aspiring Data Scientists should know.
☆35Updated 7 years ago
Alternatives and similar repositories for Practical_Statistics_for_Data_Scientists:
Users that are interested in Practical_Statistics_for_Data_Scientists are comparing it to the libraries listed below
- JupyterCon Missing Data Talk 2018☆23Updated 6 years ago
- Data Science for Good Projects☆49Updated 6 years ago
- Python data analysis course for 2017 NGCM Summer Academy☆19Updated 7 years ago
- ☆26Updated 7 years ago
- A Primer on Python for Statistical Programming and Data Science☆26Updated 5 years ago
- Notebooks of Python and R code which illustrates basic causal inference using simulated data☆23Updated 5 years ago
- A Jupyter notebook to accompany Jake VanderPlas's "Statistics for Hackers" talk from PyCon 2016.☆77Updated 6 years ago
- PyData NYC 2017: Pandas Head to Tail☆57Updated 7 years ago
- Site for a Data Science class taught by Allen Downey☆44Updated 2 years ago
- Python data science and machine learning from Ted Petrou with Dunder Data☆54Updated 2 years ago
- Introductory overview of Bayesian inference☆44Updated 5 years ago
- Few tutorials on pandas, matplotlib and seaborn☆26Updated 8 years ago
- Doing the tutorial from Brandon Rhodes PyCon 2015☆15Updated 9 years ago
- ☆26Updated 5 years ago
- ☆48Updated 6 years ago
- Data Analysis in Python☆24Updated 5 years ago
- Repository for an online class on Exploratory Data Analysis in Python☆66Updated 5 years ago
- Intermediate Python for Data Science Workshop☆24Updated 7 months ago
- iPython NOtebooks on Stats☆163Updated 7 months ago
- Repository for the PyData DC 2016 tutorial☆29Updated 8 years ago
- Tidy Data in Python Jupyter Notebook☆94Updated 2 years ago
- ☆46Updated 3 years ago
- ☆97Updated 7 years ago
- In which I implement some applications of machine learning techniques.☆30Updated 8 years ago
- Doing Bayesian statistics in Python!☆67Updated 7 years ago
- An analysis of 93,000+ data science freelancers☆57Updated 8 years ago
- Tutorial covering a new workflow available going from pandas to scikit-learn☆40Updated 2 years ago
- Tutorial on multilevel modeling, using Gelman radon example☆55Updated 9 years ago
- There are always multiple ways to complete a task in Pandas. A minimal subset of the library is sufficient for almost everything.☆83Updated 2 years ago
- Slides for my machine learning course based on Sebastian Raschka's Python Machine Learning book☆16Updated 6 years ago