Ata Science and Machine Learning Using R
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This programme aims at introducing and demonstrating through numerous practical examples the current trends in datadriven decision making using Data Science tools and machine learning methods. The programme will also introduce and use the statistical software R for exploratory data analysis, feature engineering, data wrangling and statistical analysis.
- Introduce the field of Data Science and Machine learning and its current use in various Industries for data-driven decision making.
- Introduce and use the statistical software R for data analytics.
- Handson experience of making datadriven decisions from contemporary datasets.
Contemporary business problems from various sectors of the industry shall be discussed along with relevant examples. Participants will get to apply sophisticated statistical tools using the software R to explore, analyse and interpret solutions to make data-driven decisions. Topics to be covered include supervised and unsupervised learning methods like linear regression, kNN, Naive Bayes, Discriminant analysis, logistic regression, decision trees, support vector machines, gradient boosting and Clustering algorithms among others.
Who should attend
- Industry professionals and practitioners who would like to impart data-driven decision making in their respective fields.
- Academic practitioners who would like to have an overview of the current state-of-the-art methods in these areas and use them for research and teaching.