Data Science Courses and Modules Overview
Explore data science courses at Bryn Mawr College including a 300-level computer science elective, a 200-level data visualization course, and topics such as statistical methods, machine learning, topological data analysis, and network analysis. Engage in project-oriented learning and work with real-life data sets. Dive into machine learning modules focused on the process of doing machine learning effectively, using tools like iPython notebook and Scikit-learn.
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Presentation Transcript
Data Science Dianna Xu Bryn Mawr College 1
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 2
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 3
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 4
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 5
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? 6
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 7
iPython Notebook Modules http://occam.olin.edu/sites/default/files/DataScienceMaterials/machine_learning_le cture_1/Machine%20Learning%20Lecture%201.html http://occam.olin.edu/sites/default/files/DataScienceMaterials/machine_le arning_lecture_2/Machine%20Learning%20Lecture%202.html 8
Open-source, python-based package for machine learning. Principal strength is a consistent API and enforcement of "good" machine learning practices 9
