Page dedicated to data exploratory analysis, preparation, cleaning, pre-processing / wrangling, generation, feature engineering and other related topics

• The Ultimate Python Seaborn Tutorial: Gotta Catch ‘Em All
• Exploratory Analysis
• Understand Your Machine Learning Data With Descriptive Statistics in Python
• Visualize Machine Learning Data in Python With Pandas
• 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset
• Exploring and Transforming H2O DataFrame in R and Python
• ML with H2O by Sudalai Rajkumar (slide 20 onwards)
• How to Use Statistics to Identify Outliers in Data
• Fundamentals of Data Visualization
• Helpful Python Code Snippets for Data Exploration in Pandas

• Data cleaning
• Spend Less Time Cleaning Data with Machine Learning
• Helpful Python Code Snippets for Data Exploration in Pandas - lots of python snippets to select / clean / prepare
• Working with missing data
• Journal of Statistical Software - TidyData

• Data Preprocessing vs. Data Wrangling in Machine Learning Projects
• Improve Model Accuracy with Data Pre-Processing

• See discussion on how data cleaning/preprocessing went wrong resulting in poorly performing model

• Synthetic data generation — a must-have skill for new data scientists
• How to Generate Test Datasets in Python with scikit-learn
  • Python packages
    • scikit datasets package
    • Generate data using Python libraries
  • R packages
    • Distributions generation package in R
    • sythpop - an R package that generates data
    • Another R package
    • Set of R examples
    • Very good examples of how R’s packages can be used to generate datasets time series, adjusting correlations and visualise them
    • Examples of how Scikit and R’s packages can be used to generate synthetic data

• Random database/dataframe generator
• MyRiad Toolkit (Paper: http://vldb.org/pvldb/vol5/p1890_alexanderalexandrov_vldb2012.pdf) - focuses on how to generate massive amounts of data following a database schema (create data for your relational db with users, orders, etc)
• Generating Synthetic Data to Match Data Mining Patterns

• SMOTE with Imbalance Data
• imbalanced-learn library
• SO discussion on using Python libraries
• Simple example of how stock prices can be generated

• (Computer Vision example, see code

• Building a simple Generative Adversarial Network (GAN) using TensorFlow
• GENERATIVE ADVERSARIAL NETWORKS (GANS) FOR TEXT USING WORD2VEC: PART 1
• GAN for 2D data generation
• Generative Adversarial Networks (GANs) for Discrete Data
• Boundary-seeking GANs: A new method for adversarial generation of discrete data
• Introductory guide to Generative Adversarial Networks (GANs) and their promise!
• Private Synthetic Data Generation via GANs (Supporting PDF)
• GANs (generative adversarial networks) possible for text as well?

• Basic Feature Engineering With Time Series Data in Python
• Zillow Prize - EDA, Data Cleaning & Feature Engineering
• Feature-wise transformations

• An Introduction To Statistical Learning with Applications in R
• Statistical Inferencing
  • Understanding statistical inference [video]
  • Four ideas of Statistical Inference
  • An Introduction to Statistical Learning [book]
  • Statistical Inference [course]

• Having a lot more training examples of one type of object than the other types
• Accidentally testing the neural network using images that were in the training set
• Training the neural network on data that is easier to recognize or more consistent than the real-world data it will be used to classify later on

See under Data Science and related cheatsheets and also under Tools & Libraries, Cheatsheets, Resources

• Data Science Primer
• 27 Amazing Data Science Books Every Data Scientist Should Read
• Coursera course: Getting and Cleaning Data
• Data Science courses on Coursera
• Data courses on Udemy
• Data courses on Udacity
• Learn Data Science by bitgrit
• Latest Machine learning, visualization, data mining techniques. Online Master�s in Data Analytic from Penn State
• Data Science Handbook

• 12 Best Practices for Modern Data Ingestion
  • PDF
• A Rubric for ML Production Readiness - by Jiameng Gao from Applied Deep Learning Meetup in Feb 2019 (Paper: https://ai.google/research/pubs/pub46555)
• Rules of Machine Learning: Best Practices for ML Engineering

• Data Science Primer
• How to Prepare Data For Machine Learning
• What is Data Mining and KDD
• The KDD process for extracting useful knowledge from volumes of data
• Data Mining: Practical ML Tools and Techniques by Witten, Frank and Mark 3rd edition
• Foundational Methodology for Data Science - IBM Analytics Whitepaper
• Coursera Data Science Methodology course
  • From Problem to Approach and From Requirements to Collection
    • Business Understanding
    • Analytic Approach
    • Data Requirements
    • Data Collection
  • From Understanding to Preparation and From Modeling to Evaluation
    • Data Understanding
    • Data Preparation
    • Modeling
    • Model Evaluation

• [Python Data Science Handbook on Azure git repo](https://noteboo ks.azure.com/jakevdp/projects/PythonDataScienceHandbook/tree/notebooks)
• Python for data analysis
• Python Data Science Handbook
• House prices
  • Old example notebook: examples/data/notebooks
  • Regression example: https://colab.research.google.com/drive/19uoDyGAxJ0zCwPT6cNb1xkYOfySNZChV
  • Classification example: https://colab.research.google.com/drive/1i-fOhU87wWrzgnTV0o54MQyHmRVJK0qt

See Programs and Tools

• Grakn and Graql - not just graphs, or graph database knowledge graphs | Docs | Quick start | GitHub
  • See example in the ../examples/data/databases/graph/grakn folder
• Redis Graph | Blogs | Videos
• Neo4j
• Gun: A realtime, decentralized, offline-first, mutable graph database engine
• Cayley: An open-source graph database

• How to build a data science project from scratch
• Common mistakes when carrying out machine learning and data science
• A Rubric for ML Production Readiness - Breck et al. 2017 by Jiameng Gao (28 rules to follow, suggested by Google) | Original Paper by Google

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