Hands-on data analysis in R for finance
Material type:
TextPublication details: New York CRC Press 2023 Description: xx, 393 pISBN: 9781032340975Subject(s): Data Analysis | FinanceDDC classification: 006.3 Summary: The 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
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Indian Institute of Management LRC General Stacks | Finance & Accounting | 006.3 COL (Browse shelf(Opens below)) | 1 | Available | 005620 |
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| 005.8 ROH Financial cybersecurity risk management: | 006.3 COL Hands-on data analysis in R for finance | 302.3 FIS Beyond reason: using emotions as you negotiate | 303.482 SHE Dark commerce: | 306.70954 KIR Fifteen judgments: cases that shaped India’s financial landscape |
1. 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
The 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
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