Winning with data science : a handbook for business leaders / Howard Steven Freidman and Akshay Swaminathan.
Publisher: New York : Columbia Business School Publishing, [2024]Description: x, 259 pages ; 23 cmContent type:- text
- unmediated
- volume
- 9780231206860
- HD30.215 .F74 2024
BOOKS
| Current library | Home library | Call number | Status | Barcode | |
|---|---|---|---|---|---|
| Alfaisal University On Shelf | Alfaisal University On Shelf | HD30.215 .F74 2024 (Browse shelf(Opens below)) | Available | AU00000000020649 |
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| HD30.215 .A87 2022 Data and analytics strategy for business : | HD30.215 .B345 2022 Business analytics | HD30.215 .B36 2019 Business analytics : | HD30.215 .F74 2024 Winning with data science : a handbook for business leaders / | HD30.215 .H37 2018 HBR guide to data analytics basics for managers. | HD30.215 .N45 2018 The analytics lifecycle toolkit : | HD30.215 .P75 2022 Big data analytics : |
Includes bibliographical references and index.
Tools of the Trade -- The Data Science Project -- Data Science Foundations -- Making Decisions with Data -- Clustering, Segmenting, and Cutting Through the Noise -- Building Your First Model -- Tools for Machine Learning -- Pulling It Together -- Ethics.
"Data science is increasingly important in the business world, not just for the teams in charge of implementing it but the professionals adjacent to them. Yet not all businesspeople have a general understanding of the basics-and if senior management assigns them to work alongside a data science team, they'll need that knowledge as soon as possible without having to take online courses or dive down the Internet rabbit hole. This book provides that knowledge base, walking readers through the key ideas needed to communicate and work with a data science team. They will be able to understand the basic technical lingo, recognize the types of talent on the team and pose good questions to your data scientists to open up more insights, create opportunities, and generate value. By the end of the book they will be able to answer key questions including how data is collected and stored, what hardware and software tools are needed to analyze data, who does what on the data science team and which models should be considered for specific projects. Most critically, they will also be armed with critical questions that you can use to further probe data analysts, statisticians, data scientists and other technical experts to better understand the value of their work for a business"--

