INDUSTRIAL TRAINING IN BIG DATA &DATA ANALYTICS

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1 INDUSTRIAL TRAINING IN BIG DATA &DATA ANALYTICS Course Name Duration Prerequisites Who Should Attend? What to expect? Course Focus Big Data & Data Analytics 6 Months (4 hours/day) Basic familiarity of the Operating System Concepts & Linux usage. Candidates should have knowledge of Fundamentals of Programming, Java/Python/C, C++ Programming Languages knowledge is a plus, but not extremely required. Engineering undergraduates Students or Students pursuing MCA, M.Sc(CS/IT) Candidate will have hands-on with the Data Science basics, concepts and application of Data analysis. Hands-on with various Data Analytics Algorithms with Big data infrastructure for analysis which can be applied on realworld problem. At the end of the course, candidate should be able to conceptualize & design the approach for the data analysis problems. The theoretical and practical mix of the Data Analytics & Big Data course has the following objectives: To learn the fundamental concepts of Statistics To learn the basics of programming on R tool To use advanced analytical tools/decisionmaking tools/operation research techniques to analyse the complex problems and get ready to develop such new technological advancement. (R, Weka etc.) To learn the fundamental for preparing of dataset for analysis. To learn and understand the concepts of machine learning & data mining algorithms To explore the fundamental concepts of big data analytics To develop in-depth knowledge and understanding of the big data analytics domain

2 Hardware Requirements Software Requirements Course Coordinator Mr. Sanjay Madan To learn and analyse the big data using intelligent techniques To understand the applications using Map Reduce Concepts To analyse and solve problems conceptually and practically from diverse industries, such as government manufacturing, retail, education, banking/finance, healthcare and pharmaceutical. To undertake consulting projects with significant data analysis component for better understanding of the theoretical concepts from statistics, economics and related disciplines. Multi-core 64-bit CPU System, Minimum 4GB RAM, Minimum 20 GB Hard disk space. Red Hat Linux or Ubuntu or Windows Java 1.7 or latest version Virtual Machine Software Faculty Members Mr. Rakesh Kumar Sehgal Mr. Sanjay Madan Mr. Saurabh Chamotra Mr. Sanjeev Kumar Ms. KratiPaliwal Ms. Tamanna Goyal

3 INDUSTRIAL TRAINING IN BIG DATA & DATA ANALYTICS Course Contents: (6 Months Training Program) 1. Descriptive & Inferential Statistics Basic Statistics Measures of Central Tendencies and Variance Probability Distribution Normal Distribution, Central Limit Theorem Inferential Statistics Sampling, Concept of Hypothesis Testing Statistical Methods i. Z/t-tests (One sample, independent, paired), ii. Regression, ANOVA (Analysis of Variances), iii. Correlation and Chi-square. 2. Introduction to R Data types, Sub setting, Writing Data, Reading Tabular Data files, Creating a Vector & Vector operations, Initializing Data frame, Control Structures & Functions, Loop functions & Debugging Statistics in R Computing basic Statistics, Comparing means of two samples, Testing a correlation for significance, Classical Tests (t, z, F), ANOVA Data Visualization in R Creating bar chart, dot plot, Creating a scatter plot, pie chart, creating a histogram and box plot Statistical Modelling in R & Data mining in R 3. Data Mining & Processing Data Introduction to Data Mining, Data Mining Techniques Data Cleaning, Data Transformation, Data reduction Task Relevant Data & Visualization Techniques Decision Trees Introduction & Applications Types of Decision Tree Algorithms 4. Machine Learning Introduction to machine learning Regression Least Squares, Ridge Regression, Lasso Regression, k-nearest neighbors Regression & Classification Supervised Learning Discriminative Algorithms (Linear & Quadratic), Generative Algorithms, Support Vector Machines, Learning Theory, Regularization & Model Selection, Perceptron Algorithm

4 Ensemble Methods Random Forest, Neural Networks, Deep learning Unsupervised Learning k-means Clustering, Associative Rule Mining, The EM Algorithm, Factor Analysis, Principal Components Analysis 5. Introduction to Big Data Big Data Ecosystem Industries using Big data Big Data Applications Challenges when Managing & Analyzing Big Data Key Components in Big Data Analytic Environment Introduction to NoSQL Databases 6. Introduction to Hadoop& HDFS Big Data Hadoop Stack The Apache Hadoop Framework: Basic Modules Overview of Hadoop based Applications and Services HDFS Architecture, Configuration, Design & Role in Hadoop, performance & tuning, HDFS Access, Commands, APIs and Applications Hadoop Hardware and Software Requirements Hadoop Installation 7. Hadoop MapReduce Framework Introduction to MapReduce Map/Reduce Framework Data flow in MapReduce Introduction to Apache Hive,Apache Pig and HBase Analyzing data with Pig Pig architecture, program structure & execution process, Joins & filtering using Pig, Group & co-group, Schema merging & redefining functions. 8. Introduction to Spark Introduction to Spark, Components of Spark Unified Stack Resilient Distributed Dataset (RDD) Creation of Parallelized Collection & External Datasets, RDD operations Usage of SparkContext, submission of application to the cluster 9. Tools & Techniques for Analyzing Big Data Analyzing Big data using MapReduce BI Tools Exploratory graph Analysis and visualizations

5 Analyzing Big Data using Self-service BI Tools, e.g. Impala, Hive, Stinger etc. Big Data Analytics query performance enablers Managing stream computing in a Big Data environment Various techniques for streaming analytics 10. Business Analytics Consumption of Analytics, From Creation to Consumption & How to make it Consumable. Understanding of Business pain points with different types of Analytic applications. Financial Services, Healthcare, Telecom, Manufacturing Demand forecasting, steps in hypothesis creation, identify reports and deliverables, data privacy and security 11. Industrial Relevant Project