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Data Science

Data Science. Dianna Xu Bryn Mawr College. The Course. 300-level Computer Science elective CS majors and minors Pre- reqs : CS1, CS2 (Data Structures), Discrete Math and Linear Algebra U nstructured data and explorative data analysis

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Data Science

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  1. Data Science Dianna Xu Bryn Mawr College

  2. The Course • 300-level Computer Science elective • CS majors and minors • Pre-reqs: CS1, CS2 (Data Structures), Discrete Math and Linear Algebra • Unstructured data and explorative data analysis • Iterative processes that require programming skills and and knowledge of algorithms

  3. 200-level Data Visualization Course • Taught Spring 2014 at Haverford • Stats basics, linear regression • Clustering • Baby network analysis – PageRank • Visualization with D3 • 50% overlap of students

  4. Topics • Statistical methods (2-3 weeks) • basics, regression analysis, Bayesian methods • Machine learning background (2-3 weeks) • multivariate regression and logistic regression • Dimensionality reduction (3 weeks) • PCA, SVD, Kernel PCA, other non-linear methods • Topological data analysis (2-3 weeks) • manifold learning, intro to TDA • Network analysis (2-3 weeks) • collaborative filtering, community detection

  5. Project-Oriented • Students will team up for a semester-long project on data analysis for local "data clients" • Faculty members who have "real life" data sets and research questions • Library, registrar and institutional research

  6. Data Sets • Digital Du Chemin • repertory of polyphonic songs from 16th-century France • Dark Reactions • chemical experiments with associated reactants and results • Maine athletes • Anil's bio data?

  7. Machine Learning Modules • Focused on the process of doing (good) machine learning i.e. • (step 1) Pose a problem in the language of machine learning • (step 2) Gather data • (step 3) Choose a potential method for solving the problem • (step 4) Setup an experiment to properly evaluate your method • (step 5) Evaluate experiment and possibly go to step 2 or 3 • Possible toolsets: • iPython notebook • Scikit-learn

  8. iPython Notebook Modules http://occam.olin.edu/sites/default/files/DataScienceMaterials/machine_learning_lecture_1/Machine%20Learning%20Lecture%201.html http://occam.olin.edu/sites/default/files/DataScienceMaterials/machine_learning_lecture_2/Machine%20Learning%20Lecture%202.html

  9. Open-source, python-based package for machine learning. Principal strength is a consistent API and enforcement of "good" machine learning practices

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