000			01856nam a22002057a 4500
005			20240207173720.0
008			240207b |||||||| |||| 00| 0 eng d
020			_a9781032340975
082			_a006.3 _bCOL
100			_aCollard, Jean-Francois _914060
245			_aHands-on data analysis in R for finance
260			_bCRC Press _aNew York _c2023
300			_axx, 393 p.
365			_aGBP _b74.99
500			_a1. Your Working Environment 2. Reading Data in R 3. Financial Data 4. Introduction to R 5. Functions 6. Data Transformation 7. Merging Data Sets 8. Graphing Using Ggplot 9. Returns and Returns-based Statistics 10. Portfolios 11. Modeling Returns and Simulations 12. Linear and Polynomial Regression 13. Fixed Income 14. Principal Component Analysis 15. Options 16. Value at Risk 17. Time Series Analysis 18. Machine Learning 19. Presenting the Results of Your Analyses 20. Appendix: Main Packages Seen in this Book
520			_aThe subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet
650			_aData Analysis _915336
650			_aFinance _913546
942			_cBK _2ddc
999			_c5793 _d5793