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
References
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
