| 000 | 04890nam a2200769 i 4500 | ||
|---|---|---|---|
| 001 | 9781631573460 | ||
| 003 | BEP | ||
| 005 | 20241023114932.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 190417s2021 nyua fob 001 0 eng d | ||
| 020 |
_a9781631573460 _qe-book |
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| 035 | _a(OCoLC)1257033123 | ||
| 035 | _a(CaBNVSL)slc00001528 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 | _aHF5548.2 | |
| 100 | 1 |
_aSahay, Amar, _eauthor. |
|
| 245 | 1 | 0 |
_aEssentials of data science and analytics : _bstatistical tools, machine learning, and R-statistical software overview / _cAmar Sahay. |
| 250 | _aFirst edition. | ||
| 264 | 1 |
_aNew York, New York (222 East 46th Street, New York, NY 10017) : _bBusiness Expert Press, _c[(c)2021.] |
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| 300 |
_a1 online resource (xix, 460 pages) : _billustrations (some color) |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_adata file _2rda |
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| 490 | 1 |
_aQuantitative approaches to decision making collection, _x2163-9582 |
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| 504 | _a2 | ||
| 505 | 0 |
_aPart I. Data science, analytics, and business analytics. Chapter 1. Data science and its scope ; Chapter 2. Data science, analytics, and business analytics (BA) ; Chapter 3. Business analytics, business intelligence, and their relation to data science -- _tPart II. Understanding data and data analysis applications. Chapter 4. Understanding data, data types, and data-related terms ; Chapter 5. Data analysis tools for data science and analytics: data analysis using excel -- _tPart III. Data visualization and statistics for data science. Chapter 6. Basic statistical concepts for data science ; Chapter 7. Descriptive analytics_visualizing data using graphs and charts ; Chapter 8. Numerical methods for data science applications ; Chapter 9. Applications of probability in data science ; Chapter 10. Discrete probability distributions applicationsin data science ; Chapter 11. Sampling and sampling distributions: central limit theorem ; Chapter 12. Estimation, confidence intervals, hypothesis testing -- _tPart IV. Introduction to machine learning and R-statistical programming software. Chapter 13. Basics of MachLearning (ML) ; Chapter 14. R statistical programing software for data science. |
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| 506 | _aAccess restricted to authorized users and institutions. | ||
| 520 | 3 | _aData science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions.Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative. | |
| 530 |
_a2 _ub |
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| 530 | _aAlso available in printing. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat reader. | ||
| 588 | _aDescription based on PDF viewed 06/17/2021. | ||
| 650 | 0 |
_aBusiness _xData processing. |
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| 650 | 0 | _aData mining. | |
| 650 | 0 |
_aDecision making _xComputer programs. |
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| 650 | 0 | _aR (Computer program language) | |
| 653 | _aData science. | ||
| 653 | _aData analytics. | ||
| 653 | _aBusiness analytics. | ||
| 653 | _aBusiness intelligence. | ||
| 653 | _aData analysis. | ||
| 653 | _aDecision making. | ||
| 653 | _aDescriptive analytics. | ||
| 653 | _aPredictive analytics. | ||
| 653 | _aPrescriptive analytics. | ||
| 653 | _aStatistical analysis. | ||
| 653 | _aQuantitative techniques. | ||
| 653 | _aData mining. | ||
| 653 | _aPredictive modeling. | ||
| 653 | _aRegression analysis. | ||
| 653 | _aModeling. | ||
| 653 | _aTime-series forecasting. | ||
| 653 | _aOptimization. | ||
| 653 | _aSimulation. | ||
| 653 | _aMachine learning. | ||
| 653 | _aNeural networks. | ||
| 653 | _aArtificial intelligence. | ||
| 655 | 0 | _a[genre] | |
| 655 | 0 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _z9781631573453 |
| 830 | 0 |
_aQuantitative approaches to decision making collection. _x2163-9582 |
|
| 856 | 4 | 0 | _uhttps://go.openathens.net/redirector/ciu.edu?url=https://portal.igpublish.com/iglibrary/search/BEPB0001124.html |
| 942 |
_2lcc _bCIU _cOB _eBEP _QOL _zBEP9781631573460 |
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| 999 |
_c73701 _d73701 |
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| 902 |
_c1 _dCynthia Snell |
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