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Stony Brook University
CSE 51d Data Science Fundamentals
Tentative Syllabus
Lecture 1: Introduction
Introduction to Data Science
Big Data products
Scoping Projects, Asking good questions
The Data Mining process
Course introduction
Lecture 2: Data Preparation
Basic Data Types
Data Analytics Major Building Blocks: A Bird's Eye View
Data Collection, Cleaning, Integration, Storage
Lecture 3-4: Statistics
Statistics basics, Experiment design, Pitfalls
Observational and Longitudinal studies
Sampling
Probability Distributions
Hypothesis Testing
Significance
Lecture 5: Optimization
Convexity, Convex functions
Convex Optimization: unconstrained/constrained
Gradient Descent and variants
Lecture 6-8: Statistical Learning
Statistical Models and Likelihood, Likelihood Principle, MLE foundations
Machine Learning Concepts & Tasks:
Classification, Regression, Clustering, Dimensionality Reduction
Machine Learning: Specific algorithms
Lecture 9-10: Visualization
Visualization fundamentals
Charts, Graphs, Infographics
Interactive Visualization
Summarization
Lecture 11-13: Data Mining
Data Mining Concepts & Tasks:
Association Rules, Similarity Search, Cluster Analysis, Outlier Analysis
Data Mining: Specific algorithms
Lecture 14-17: Data: Unstructured vs. Structured
Time-Series Analysis
Text Mining/Information Retrieval
Network Analysis
Spatial Data Mining
Lecture 18-19: Matrix Methods
Matrix Factorization: Models and Algorithms
Matrix-Vector Product & Applications
Lecture 20-21: Computing at Scale
Memory, Parallelization, Map-Reduce
Hadoop, Pig, HBase, Hive
Spark, Spark SQL
Lecture 22: Data Science in the Real-World
Data Journalism
Provenance, Privacy, Ethics, Governance
Books:
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Data Mining –The Textbook
http://link.springer.com/book/10.1007/978-3-319-14142-8
(FREE) Mining Massive Datasets
http://mmds.org/
(FREE) The Elements of Statistical Learning
http://statweb.stanford.edu/~tibs/ElemStatLearn/
Doing Data Science
http://columbiadatascience.com/doing-data-science/
Data Science for Business
http://www.data-science-for-biz.com/
(FREE) An Introduction to Information Retrieval
http://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
The Visual Display of Quantitative Information
http://www.edwardtufte.com/tufte/books_vdqi
Convex Optimization
http://web.stanford.edu/~boyd/cvxbook/
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