Data science / by Lillian Pierson ; foreword by Jake Porway
By: Pierson, Lillian [author].
Contributor(s): Porway, Jake [writer of introductory text].
Series: --For dummies: Publisher: Hoboken, NJ : John Wiley and Sons, Inc., [2017]Edition: 2nd edition.Description: xvi, 364 pages : illustrations, charts ; 24 cm.Content type: text | still image Media type: unmediated Carrier type: volumeISBN: 9781119327639.Other title: Data science for dummies [Cover title].Subject(s): Information technology | Information retrieval | Databases | Data miningGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | T58.5 .P488 2017 (Browse shelf) | Available | AU00000000012694 |
Browsing Alfaisal University Shelves , Shelving location: On Shelf Close shelf browser
|
|
|
|
|
|
|
||
| T57.95 .T938 2014 Fuzzy multiple objective decision making / | T58.T4 A33 2001 Scientific management, comprising Shop management, The principles of scientific management [and] Testimony before the special House committee. | T58.5 .J47 2023 Information systems today / | T58.5 .P488 2017 Data science / | T58.6 .L376 2016 Management information systems : managing the digital firm / | T58.6 .L376 2021 Management information systems : managing the digital firm / | T58.6 .O26 2011 Management information systems / |
Includes index
Getting Started With Data Science -- Wrapping Your Head around Data Science -- Exploring Data Engineering Pipelines and Infrastructure -- Applying Data-Driven Insights to Business and Industry -- Using Data Science to Extract Meaning from Your Data -- Machine Learning: Learning from Data with your Machine -- Math, Probability, and Statistical Modeling -- Using Clustering to Subdivide Data -- Modeling with Instances -- Building models that Operate Internet-of-Things Devices -- Creating Data Visualizations that Clearly Communicate Meaning -- Following the Principles of Data Visualization Design -- Using D3.js for Data Visualization -- Web-Based Applications for Visualization Design -- Exploring Best Practices in Dashboard Design -- Making Maps from Spatial Data -- Computing for Data Science -- Using Python for Data Science -- Using Open Source R for Data Science -- Using SQL in Data Science -- Doing Data Science with Excel and Knime -- Applying Domain Expertise to Solve Real-World Problems Using Data Science -- Data Science in Journalism: Nailing Down the Five Ws (and an H) -- Delving into Environmental Data Science -- Data Science for Driving Growth in E-Commerce -- Using Data Science to Describe and Predict Criminal Activity -- The Part of Tens --Ten Phenomenal Resources for Open Data -- Ten Free Data Science Tools and Applications
Begins by explaining large data sets and data formats, including sample Python code for manipulating data. The book explains how to work with relational databases and unstructured data, including NoSQL. The book then moves into preparing data for analysis by cleaning it up or "munging" it. From there the book explains data visualization techniques and types of data sets. Part II of the book is all about supervised machine learning, including regression techniques and model validation techniques. Part III explains unsupervised machine learning, including clustering and recommendation engines. Part IV overviews big data processing, including MapReduce, Hadoop, Dremel, Storm, and Spark. The book finishes up with real world applications of data science and how data science fits into organizations