COURSE SUMMARY
Course Title: 
Big Data Analytics and Visualization
Course Code: 
18CA331
Year Taught: 
2018
Degree: 
Postgraduate (PG)
School: 
School of Engineering
Campus: 
Amritapuri

'Big Data Analytics and Visualization' is a course offered in M. C. A. (Master of Computer Applications) program at School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri.

Introduction of big data – Big data characteristics - Volume, Veracity, Velocity, and Variety – Data Appliance Challenges and Issues, Case for Big data, Big data sources, Features of data. - Evolution of Big data – Best Practices for Big data Analytics - and Integration tools Introduction to Data Modeling, Data Models Used in Practice: Conceptual data models, Logical data models, Physical data models, Common Data Modeling Notations , How to Model Data : Identify entity types, Identify attributes, Apply naming conventions, Identify relationships, Apply data model patterns, Assign keys, Normalize to reduce data redundancy, Introduction to elementary data analysis: Measures of center: Mean, Median, Mode, Variance, Standard deviation, Range. Normal Distribution: Center, Spread, Skewed Left, Skewed Right, outlier. Correlations: Correlation Patterns: Direction relationship, Magnitude Relationship. Introduction to Bayesian Modeling: Bayes Rule, Probabilistic Modeling Introduction to Predictive Analytics: Simple Linear regression, Multiple Linear regression, Logistic Linear Regression. History of Visualization, Goals of Visualization, Types of Data Visualization: Scientific Visualization, Information Visualization, Visual Analytics, Impact of visualization Introduction to Data Processing , Map Reduce Framework , Hadoop ,HDFS , S3 Hadoop Distributed file systems, Apache Mahout, Hive,Sharding, Hbase , Impala , Case studies : Analyzing big data with twitter ,Big data for Ecommerce , Big data for blogs.

  • Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley andSASBusinessm.Series, 2012.
  • The Data Modeling Handbook: A Best-Practice Approach to Building Quality DataModels 1st Edition by Michael C. Reingruber (Author), William W. Gregory(Author) A Wiley QED publications
  • Colleen Mccue, “Data Mining and Predictive Analysis: Intelligence Gathering andCrimeAnalysis”,Elsevier, 2007
  • Correlation and Regression: Applications for Industrial Organizational Psychologyand Management (Organizational Research Methods) 1st Edition, by Philip BobkoMultiple Regression and Beyond 1st Edition by Timothy Z. Keith.