Data Science With Excel
Abstract
Full Text:
PDFReferences
Pandita, R., Parnin, C., Hermans, F., & Murphy-Hill, E. (2018). No half-measures: A study of manual and tool-assisted end-user programming tasks in Excel. 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 95–103.J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in Statistics. Berlin, Germany: Springer, 1989, vol. 61.
Ruel, E., William, W., & Gillespie, B. J. (2018). Data cleaning. The Practice of Survey Research: Theory and Applications, 208–237.
Hossain, E. (2021). MS Excel in Engineering Data. In Excel Crash Course for Engineers (pp. 169–242). Springer.
Huang, Z., & He, Y. (2018). Auto-detect: Data-driven error detection in tables. Proceedings of the 2018 International Conference on Management of Data, 1377–1392.
Wang, P., & He, Y. (2019). Uni-detect: A unified approach to automated error detection in tables. Proceedings of the 2019 International Conference on Management of Data, 811–828.
Liu, R., Glover, K. P., Feasel, M. G., & Wallqvist, A. (2018). General approach to estimate error bars for quantitative structure– activity relationship predictions of molecular activity. Journal of Chemical Information and Modeling, 58(8), 1561– 1575.
Wu, Z., Wu, Z., & Rilett, L. R. (2020). Outlier Record, filtering. 2674(10), Transportation Research 167–176.
Grech, V. (2018). WASP (Write a Scientific Paper) using Excel–3: Plotting data. Early Human Development, 117, 110–112.
Kaminskyi, R., Kunanets, N., Pasichnyk, V., Rzheuskyi, A., & Khudyi, A. (2018). Recovery Gaps in Experimental Data. COLINS, 108– 118.
Biessmann, F., Salinas, D., Schelter, S., Schmidt, P., & Lange, D. (2018). “ Deep”Learning for Missing Value Imputationin Tables with Non-numerical Data. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2017–2025.
Sofalvi, S., & Schueler, H. E. (2021). Assessment of Bioanalytical Method Validation Data Utilizing Heteroscedastic Seven-Point Linear Calibration Curves by EZSTATSG1 Customized Microsoft Excel Template. Journal of Analytical Toxicology, 45(8), 772–779.
Georgieva, P., Nikolova, E., & Orozova, D. (2020). Data Cleaning Techniques in Detecting Tendencies in Software Engineering. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 1028– 1033.
DOI: https://doi.org/10.29040/ijcis.v3i3.79
Article Metrics
Abstract view : 1415 timesPDF - 618 times
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License