Data Science and Data Mining
Pipelines, Clustering, Association Rules and Model Evaluation
About this guide
A concise exam companion for data science and data mining. It covers the data-science pipeline and data quality, exploratory analysis and feature engineering, similarity and distance, classification and clustering, association rule mining, dimensionality reduction, evaluation and validation, text and web mining, and scalable mining, closing on privacy, bias and reproducibility. Every chapter ends with a worked example carried through step by step, so you can check the reasoning yourself.
Inside the guide
- Data-science pipeline and data quality
- Exploratory analysis and feature engineering
- Similarity and distance
- Classification and clustering
- Association rule mining
- Dimensionality reduction
- Evaluation and validation
- Text and web mining
- Scalable mining
- Closing on privacy
- Bias and reproducibility
Questions students ask
What does Data Science and Data Mining cover?
It covers the data-science pipeline and data quality, exploratory analysis and feature engineering, similarity and distance, classification and clustering, association rule mining, dimensionality reduction, evaluation and validation, text and web mining, and scalable mining, closing on privacy, bias and reproducibility.
Who is Data Science and Data Mining for?
It is written for university students revising data science and data mining, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I read part of it for free?
Full Marks Press offers a free sample chapter at fullmarkspress.com/free, and the full guide is free to read with Kindle Unlimited.