Master of Science in Data Science and Analytics

Transcription

1 Master of Science in Data Science and Analytics

2 Societal Need Hiring Demand by Metro Area for Big Data Experience in Canada

3 Data Scientist We need teams Statistics/ Math Software Engineering Machine learning Computer science Visualization Communication Domain expertise

4 Raw Data- the process

5 Vocabulary: the language of data science machine learning supervised learning unsupervised learning training set or training sample test set or test sample k-nearest neighbors exploratory data analysis Regression residual sum of squares least squares estimators Classification Prediction Overfitting cross-validation loss functions Misclassification Labels Euclidean distance bias, variance bias-variance trade-off Pig, hive, mongodb

6 Core Competencies Stats/ math* SWE ML CS Application Domains Stats with R DB * ML Algorithms for massive data geospatial EDA Programming (Python) * Data Mining Systems for Big Data ** bioinformatics Probability Data Structures and Algorithms * NLP ** Visualization ** Social media Graph Programming for big data Cognitive computing Business (marketing, economics, finance

7 Data Science Skill Set Depth and Breadth Depth: Machine learning Statistics Domain knowledge Breadth: Software engineering Distributed computing Communication Business acumen

8 Ryerson s commitment to Data Science and Big Data Programs Certificate Masters Undergrad courses Research Data Science Lab Social Media Lab Laboratory for Systems, Software and Semantics Privacy and Big Data Institute RC4 Projects Canada s Big Data Consortium

9 Masters program certificate From Oracle White Paper, 2013

10 Core Courses Designs of Algorithms and Programming for Massive Data Machine Learning Management of Big Data and Big Data Tools Data Mining and Prescriptive Analysis

11 Core Courses Design of Algorithms and Programming for Massive Data Review of tools from algorithm design, models and lower bounds, basic external memory data structures, I/O efficient algorithms, Elementary graph algorithms in external memory, linear algebra, optimization and statistics; Compressed sensing and popular methods in sparse signal reconstruction, including L1-minimization, greedy algorithms, message passing algorithms and statistical methods Performance analysis of sparse signal reconstruction techniques; Matrix approximation and decomposition techniques and theory Data clustering and spectral algorithms

12 Core Courses Machine Learning Review of Basic Probability Theory Linear/Non-linear Regression Parameter estimation Classification Generative vs. Discriminative Models, Bayesian Decision Theory, Naïve Bayes, Logistic Regression, Decision Trees, K-Nearest Neighbours Clustering K-means Clustering, Mixtures Models and Expectation-Maximization Algorithm Dimensionality Reduction Time Series Models Markov Models, Hidden Markov Models, Viterbi Algorithm, Forward-Backward Algorithm, Baum-Welch/Expectation-Maximization Algorithm Support Vector Machines (SVM) Separable case, Non-separable case, Kernel Trick Neural Networks Model Selection and Evaluation Cross Validation, Classifier Performance Evaluation

13 Core Courses Management of Big Data and Big Data Tools Overview of database design and ER models Overview of relational model, relational algebra and SQL Semi-structured data and XML Distributed databases and replication MapReduce, Hadoop, Pig, Hive, etc. Unstructured data and NoSQL Key-value stores Mango DB and document store Graph databases

14 Core Courses Data Mining and Prescriptive Analysis Data Mining Techniques Datasets, Algorithms Bayesian networks and Graphical models Neural Networks and Support Vector Machines Bayesian Decision Theory Stochastic Optimization: Basic setup; Statistical Learning as a Stochastic Optimization problem Overview of different fields: Statistics, Signal Processing, Optimization, Machine Learning Approaches SAA (sample average approximation) approach in Stochastic Optimization/ERM (Empirical Risk Minimization) in Machine Learning SA (stochastic approximation) approach in Stochastic Optimization/Online Learning approach in Machine Learning Hybrid approach: Using SA/Online techniques to minimize an empirical objective. Examples from Machine Learning

15 Prerequisite Courses Statistics and R Programming Database Management Data Structures and Algorithms

16 Programs in Data Analytics Certificate in Data Analytics, Big Data, and Predictive Analytics Aligned with CAPS INFORMS Launched in September 2014

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18 Resources [1] Bill Howe, Intro. to Data Science, Univ. of Washington, 2013: [2] Book: Doing Data Science: Straight Talk from the Frontline, Orielly, [3] Data Science Intro: [4] Google Flu: [5] [6] Wrong Earthquake Prediction: [7] VVV Origin: [8] Basic Stats: [9] Informs Study Guide: Ed/Analytics-Certification/Study-Guide Lecture 1 18

19 Resources [1] Bill Howe, Intro. to Data Science, Univ. of Washington, 2013: [2] VVV Origin: [3] [4] [5] [6] [7] system-processing [8] [9] Lecture1 19

20 References Schutt, R. (2013), Next-Gen Data Scientists, Strata Talk, Feb Davenport, T., and Patil, D.J.(2012), Data Scientist: The Sexiest Job of the 21 st Century, Harvard Business Review. Davenport, T., Barth, P., and Bean, R. (2012), How Big Data is Different, MITSloan Management Review, Fall Kirin, D., Prentice, P.K., and Ferguson, R.B. (2012), Innovating with Analytics, MITSloan Management Review, Fall Surma, J. (2011), Chapter 1: An Introduction to Business Intelligence, Business Intelligence: Making Decisions through Data Analytics. Harvard Business Publishing.

21 Thank you