Table of Contents
- Business Science
- Business Science Problem Framework
- Data Science with Python Workflow
- Data Science with R Workflow
- Python
- Python Basics
- Pandas Basics
- Pandas
- Importing Data
- Jupyter
- Numpy Basics
- Beginners Python Cheat Sheet
- Intermediate Python
- Python REGEX
- Python 3 Memento
- R
- Tidiverse
- data.table
- xts
- Base R
- Data Import with readr
- Data Transformation with Dplyr
- Apply Functions with purrr
- Data transformation with data.table
- Dates and Times with lubridate
- Randomizr
- Regular Expressions
- Work with Strings with stringr
- Tidy Evaluation with rlang
- Xplain
- Sintax Comparison
- Data and Variable Transformation with sjmisc
- R Markdown (PDF)
- Package Development with devtools
- Math and Calculus
- Refresher Algebra and Calculus
- Refresher Probabilities and Statistics
- Fundamentals of Probabilities
- Big Data
- Pyspark RDD
- Pyspark DF
- Dask
- Sparklyr
- Machine Learning
- Scitk-Learn (PDF)
- Machine Learning Modelling in R
- Caret
- Estimatr
- H2O
- mlr
- Regression
- VIP Supervised Learning
- Segmentation and Clustering
- VIP Unsupervised Learning
- VIP Machine Learning Tips and Tricks
- Choosing the right model
- Deep Learning -Neural Nets
- Keras RStudio - Keras
- Deep Learning Basics
- Convolutional Neural Networkds
- Recurrent Neural Networks
- Tips and Tricks
- SQL
- SQL cheatsheet by sqltutorial (PDF)
- SQL cheatsheet by Rebel Labs
- Data Visualization
- Matplotlib
- Seaborn
- Bokeh
- Comprehensive Guide to Data Visualization in Python
- Ggplot2
- Leaflet
- Cartography
- Comprehensive Guide to Data Visualization in R
- Simple Features sf
- survminer
- Data Science in General and Others
- Shiny
- Vim
- Emacs
- Git - Atlassian's Cheatsheet
- DVC
- BASH
- Data Science Cheatsheet Takeaway