PGD01C04
Core theory
4 credits
5 modules · 30 topics

Data Analytics and Prediction

Predictive modelling: process, regression and classification performance, discriminant methods, ROC analysis, and tree/rule ensembles.

Course outcomes
  • Understand predictive modeling techniques for data analytics.

  • Apply data preprocessing techniques for big data.

  • Measure the performance of classification and regression models.

  • Use Classification Trees and Rule-Based Models in big data analytics.

References
  1. Kuhn & Johnson, Applied Predictive Modeling, 2e, Springer, 2018.

  2. Ankam Venkat, Big Data Analytics, Packt, 2016.

  3. EMC Education Services, Data Science and Big Data Analytics, Wiley, 2015.

  4. Wickham & Grolemund, R for Data Science, O'Reilly, 2017.

  5. Joel Grus, Data Science from Scratch, O'Reilly, 2015.

  6. James D. Miller, Statistics for Data Science, Packt, 2017.

  7. Thomas Rahlf, Data Visualization with R: 100 Examples, Springer, 2017.