Data Science & Big data (CS-7005)
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RGPV notes CBGS Bachelor of engineering
Syllabus
UNIT 1:
Understanding Data: Data Wrangling and Exploratory Analysis, Data Transformation &
Cleaning, Feature Extraction, Data Visualization. Introduction to contemporary tools and
programming languages for data analysis like R and Python.
UNIT 2:
Statistical & Probabilistic analysis of Data: Multiple hypothesis testing, Parameter
Estimation methods, Confidence intervals, Bayesian statistics and Data Distributions.
UNIT 3:
Introduction to machine learning: Supervised & unsupervised learning, classification &
clustering Algorithms, Dimensionality reduction: PCA & SVD, Correlation & Regression
analysis, Training & testing data: Overfitting & Under fitting.
UNIT 4:
Introduction to Information Retrieval:Boolean Model, Vector model, Probabilistic Model,
Text based search: Tokenization,TF-IDF, stop words and n-grams, synonyms and parts
of speech tagging.
UNIT 5:
Introduction to Web Search& Big data: Crawling and Indexes, Search Engine
architectures, Link Analysis and ranking algorithms such as HITS and PageRank,
Hadoop File system & MapReduce Paradigm
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Text Books:
1.Field Cady, “The Data Science Handbook” , 1/e ,2018,Publisher: Wiley
2. Sinan Ozdemir, “Principles of Data Science “, 1/e, 2016Packt Publishing Limited
Books Recommended
1. Peter Bruce, “Practical Statistics for Data Scientists: 50 Essential
Concepts”,Shroff/O'Reilly; First edition, 2017
2. Pang-Ning Tan, “Introduction to Data Mining”, Pearson Edu.
3. Ricardo Baeza-Yates and Berthier Ribeiro-Neto, “Modern Information Retrieval”,
Pearson Education
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