Data Analytics involves being about to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making. The course will cover a number of topics, including:

• Introduction to Data Science and Machine Learning: motivating successful analytic examples (Walmart, Google, and Twitter), introducing Supervised, Unsupervised, and Reinforcement Learning, measuring performance / regret, fundamental limits (the No-Free-Lunch-Theorem and Bias);
• Probability recap, e.g., sample spaces, random variables, distributions, heavy-tails, quantiles, Q-Q plots, Bayes, correlation;
• Statistics recap, e.g., hypothesis testing, chi-square distributions, density estimation (MoM and MLEs), confidence intervals and application to voting;
• Stochastic bandits as a fundamental example of Reinforcement Learning: naïve Explore-then-Exploit strategy and UCB bounds;
• Regression: linear regression, least squares, logistic regression - Predicting new data values via regression models. Simple linear regression over low dimensional data, regression for higher dimensional data via least squares optimization, logistic regression for categoric data;
• Matrices: Linear Algebra, SVD, PCA - Matrices to represent relations between data, and necessary linear algebraic operations on matrices. Approximately representing matrices by decompositions (Singular Value Decomposition and Principal Components Analysis). Application to the Netflix prize;
• Classification: Trees, NB, Support Vector Machines, Kernel Trick - Building models to classify new data instances. Decision tree approaches and Naive Bayes classifiers. The Support Vector Machines model and use of Kernels to produce separable data and non-linear classification boundaries. The Weka toolkit;
• Clustering: hierarchical, k-means, k-center - Finding clusters in data via different approaches. Choosing distance metrics. Different clustering approaches: hierarchical agglomerative clustering, k-means (Lloyd's algorithm), k-center approximations. Relative merits of each method;
• Basic tools: command line tools, plotting tools, programming tools - The wide variety of tools available to work with data, including unix/linux command line tools for data manipulation (sorting, counting, reformatting, aggregating, joining); tools such as gnuplot for displaying and visualizing data;
• A number of hands-on exercises involving real data and to be solved in either the Weka toolkit, Python, or R.