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. Worked examples are marked the way an examiner marks them, so you can see why an answer earns the grade.
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 get a free copy?
Yes. Full Marks Press offers free review copies to students and educators at fullmarkspress.com/free.