Data Science With R detailed syllabus scheme for Computer Engineering (CE), 2017-18 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 8th Sem Scheme of Computer Engineering (CE), 2017-18 Onwards, do visit CE 8th Sem Scheme, 2017-18 Onwards. For the Elective-XII scheme of 8th Sem 2017-18 onwards, refer to CE 8th Sem Elective-XII Scheme 2017-18 Onwards. The detail syllabus for data science with r is as follows.
Data Science With R Syllabus for Computer Engineering (CE) 4th Year 8th Sem 2017-18 DBATU
Course Objectives and Outcomes:
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Detailed Contents:
Data Mining Patterns: Cluster Analysis: K-means Clustering, K-medoids Clustering, Hierarchical Clustering, Expectation Maximization, Density estimation; Anomaly Detection: Show outliers, Calculating Anamolies; Association Rules: Mine for Association; Data Mining Sequences: Eclat, arulesNBMiner, Apriori, Determining Sequences using TraMineR, Similarity in sequences.
Machine Learning: Data Partitioning Model, The Train Model, Predicting events with machine learning – automatic forecasting package; Supervised and Unsupervised learning.
Reference Book:
- Dan Toomey, “R for Data Science”, PACKT Publication, First Edition, 2014.
- Data Science from Scratch: First Principles with Python, O’Reilly Media.
- Hadley Wickham, Garrett Grolemund, R for Data Science Import, Tidy, Transform, Visualize, and Model Data, O’Reilly Media.
- Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley Publication.
- Julia Silge, David Robinson, Text Mining with R: A Tidy Approach, O’Reilly Media.
For detail syllabus of all subjects of Computer Engineering (CE) 8th Sem 2017-18 onwards, visit CE 8th Sem Subjects of 2017-18 Onwards.