438_AmeeruddinMohammed
- 1. International School of Engineering awards Certificate in Engineering Excellence in Big Data Analytics and Optimization to Ameeruddin Mohammed on successful completion of all the requirements of the 352-hour program conducted between November 15, 2014 and April 26, 2015 followed by a project defense. This program is certified for quality of content, assessment and pedagogy by the Language Technologies Institute (LTI) of Carnegie Mellon University (CMU). LTI also provided assistance in curriculum development for this program. Dated this eleventh day of August, two thousand and fifteen. Dr. Dakshinamurthy V Kolluru Dr. Sridhar Pappu President Executive VP - Academics 01CSE03/201505/438 Program details are on the back
- 2. Mode: Classroom Teaching Topics Covered Certificate Type Certificate of Participation Assessment-based training program Professional certification Planning and Thinking Skills for Architecting Data Science Solutions Why build models or use data to run a business? What kind of models are built? Were do models not work? How do you make predictions? When does big unstructured data become important? Thinking tools: Approximations and estimations, Geometric visualization of data and models Choosing the right models and architecting a solution: Structure and anatomy of models, Problematic data and choosing the right experimentation Sources of errors in predictive models and techniques to minimize them Interacting with technical and business teams; Case study Essential Engineering Skills in Big Data Analytics Reading from Excel, CSV and other forms; Data exploration (histograms, bar charts, box plots, line graphs and scatter graphs); Storytelling with data: The science, ggplot, bubble charts with multiple dimensions, gauge charts, tree maps, heat maps and motion charts Data pre-processing of structured data: R, Handling missing values, Binning, Standardization, Outliers/Noise, PCA, Type conversion Fundamentals of Probability and Statistical Methods Probabilistic analysis of data and models, Analyzing networks and graphs: Analyzing transitions, Markov chains and unstructured data Computing the properties of an attribute: Central tendencies (Mean, Median, Mode, Range, Variance, Standard Deviation); Expectations of a Variable; Describing an attribute: Probability distributions (Discrete and Continuous) - Bernoulli, Geometric, Binomial, Poisson and Exponential distributions; Special emphasis on Normal distribution; Central Limit Theorem; t-distribution Describing the relationship between attributes: Covariance; Correlation; ChiSquare Inferential statistics: How to learn about the population from a sample and vice-versa, Sampling distributions, Confidence Intervals, Hypothesis Testing; ANOVA Statistics and Probability in Decision Modeling Regression (Linear, Multivariate Regression) in forecasting; Analyzing and interpreting regression results; Logistic Regression for classification Trend analysis and Time Series; Cyclical and Seasonal analysis; Box-Jenkins method; Smoothing; Moving averages; Auto -correlation; ARIMA – Holt-Winters method Bayesian analysis and Naïve Bayes classifier; Bayesian Belief Networks Optimization and Decision Analysis Genetic algorithms: The algorithm and the process, Representing data, Why and how do they work? Linear Programming: Graphical analysis; Sensitivity and Duality analyses Integer and Binary programming: Applications, Problem formulation, Solving in R Goal programming; Data development analysis Quadratic programming Engineering Big Data with R and Hadoop Ecosystem Introduction—Big Data, Hadoop applications; Parallel and Distributed computing; Introduction to algorithms; Concurrent algorithms; Linux refresher; NoSQL; GFS; HDFS; CDH4-HDFS Map Reduce: YARN Map Reduce Applications: Text Mining, Page Rank, Graph processing Hadoop ecosystem components: Pig, Hive, HBase, Sqoop, Mahout, Spark, H2O, Hama, Flume, Chukwa, Avro, Whirr, Hue, Oozie, Zookeeper R-Hadoop Text Mining, Social Network Analysis and Natural Language Processing Introduction to text mining and text pre-processing: Write a web crawler to collect data, R, Find unique words and counts, Handling number, Punctuations, Stop words, Incorrect spellings, Stemming, Lemmatization and TxD computation Unstructured vs. semi-structured data; Fundamentals of information retrieval Properties of words; Vector space models; Creating Term-Document (TxD) matrices; Similarity measures Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking) Text classification and feature selection: How to use Naïve Bayes classifier for text classification Evaluation systems on the accuracy of text mining Sentiment Analysis Natural Language Analysis Discussion of text mining tools and applications Methods and Algorithms in Machine Learning — Unsupervised and Supervised Rule based knowledge: Logic of rules, Evaluating rules, Rule induction and Association rules Construction of Decision Trees through simplified examples; Choosing the "best" attribute at each non-leaf node; Entropy; Information Gain; Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with numerical variables; Other measures of randomness; Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as rules Specialized decision trees (oblique trees) Ensemble and Hybrid models AdaBoost, Random Forests and Gradient boosting machines K-Nearest Neighbor method; Wilson editing and triangulations; K-nearest neighbors in collaborative filtering, digit recognition Motivation for Neural Networks and its applications; Perceptron and Single Layer Neural Network, and hand calculations; Learning in a Neural Net: Back propagation and conjugant gradient techniques; Application of Neural Net in Face and Digit Recognition Deep Learning techniques Connectivity models (hierarchical clustering); Centroid models (K-Means algorithm); Distribution models (Expectation maximization); Spectral clustering Linear learning machines and Kernel methods in learning VC (Vapnik-Chervonenkis) dimension; Shattering power of models Algorithm of Support Vector Machines (SVM) Communication, Ethical and IP Challenges for Analytics Professionals Why is Communication important? How to communicate effectively: Telling stories Communications issues from daily life using examples using audio, video, blogs, charts, email, etc. Seeing the big picture; Paying attention to details; Seeing things from multiple perspectives Challenges: Mix of stakeholders, Explicability of results, Visualization Guiding Principles: Clarity, Transparency, Integrity, Humility Framework for Effective Presentations; Examples of bad and good presentations Writing effective technical reports Difference between Legal and Ethical issues Challenges in current laws, regulations and fair information practices: Data protection, Intellectual property rights, Confidentiality, Contractual liability, Competition law, Licensing of Open Source software and Open Data How to handle legal, ethical and IP issues at an organization and an individual level The “Ethics Check” questions