Syllabus

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 analytics, case studies - How analytics is used in practice. Examples of successful analytics work from companies such as Google, Facebook, Kaggle, and Netflix.
• 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.
• Statistics: Probability recap, distributions, significance tests, R - The tools from statistics for understanding distributions and probability (means, variance, tail bounds). Hypothesis testing for determining the significance of an observation, and the R system for working with statistical data.
• 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.
• 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.
• 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.
• Graphs: Social Network Analysis, metrics, relational learning - Graph representations of data, with applications to social network data. Measurements of centrality and importance. Classification and prediction.
• Recommendations in social networks; neighbor and latent-based methods.
• Time-Series Analysis; dynamic time warping, dimensionality reduction, autoregressive moving averages