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Item type | Location | Call Number | Status | Notes | Date Due |
---|---|---|---|---|---|
Book | AUM Main Library English Collections Hall | 006.31 O586 (Browse Shelf) | Checked out | invoice 2021/1473 | 18/08/2024 |
Book | AUM Main Library English Collections Hall | 006.31 O586 (Browse Shelf) | Available | invoice 2021/1473 |
006.31 M149Machine learning and data mining in pattern recognition : | 006.31 M149Machine learning and data mining in pattern recognition : | 006.31 M676Machine learning : | 006.31 O586Doing data science / | 006.31 O586Doing data science / | 006.31 P317Deep learning : |
2013-10-08: first release ; 2013-12-13: second release ; 2014-10-10: third release.
Includes index.
Introduction : What is data science? -- Statistical inference, exploratory data analysis, and the data science process -- Algorithms -- Spam filters, naive bayes, and wrangling -- Logistic regression -- Time stamps and financial modeling -- Extracting meaning from data -- Recommendation engines : building a user-facing data product at scale -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons learned from data competitions : data leakage and model evaluation -- Data engineering : MapReduce, Pregel, and Hadoop -- The students speak -- Next-generation data scientists, hubris, and ethics.
A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering.
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