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M.E – BDA

M.E Big Data Analytics

Big Data Analytics offers end-to-end comprehensive Information and Communication Technology infrastructure services to meet all your business needs. Their objectives are primarily focused around disrupting service for clients, companies, entities or organizations that they don’t agree with for various reasons, commercial, personal, political etc,.

Master of Engineering in Big Data Analytics, a postgraduate course (full-time two years course degree) is offered by Anna University, Chennai and approved by AICTE, New Delhi. Big Data provides customized IT consulting, solutions and services to customers from different industry verticals. It collaborate with the clients to understand their business needs and fill the gaps in their software development and delivery processes.

Employment scopes :

This course provides a wide job opportunity to the Big data analytics and data science roles. In 2020 global estimate calls for 2.7 million job postings for in this domain. By 2020, India will face a demand-supply gap of 2,00,000 data science and big data analytics professionals.
Big Data Analytics is considered to be the most wanted expertise by 75 percent Internet of Things (IoT) providers, and over 68 percent of them are struggling to find employees with relevant expertise. The scope of professional opportunities is anticipated to grow in years to come.
With popular brands Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell have invested more than $15 billion in software firms specializing in Data Management Analytics, increasing the demand for Information Management specialists across multiple industry and domain-types.
In 2017, three out of top five technical jobs were involved with analytics (Data Architects had a median salary of $122,000, Data Managers, a median salary of $109,000, and Data Engineers a median salary of $105,000). The monetary benefits of shifting to a Data Analytics career can prove to be better than those of other IT professionals.
Especially for B2B companies, Big Data Analytics gives better insights about their customers and eliminates the risks of guesswork. As part of the evidence-based segmentation and reporting structure, you’ll be able to build more-fluid and user-driven customer experience with the firm.

M.E Big Data Analytics

PROGRAMME EDUCATIONAL OBJECTIVES (PEOs)

  • PEO 1- Enable students to integrate theory and practice for problem solving.
  • PEO 2- Empower students to critically analyze current trends and future issues from a system perspective at multiple levels of detail and abstraction.
  • PEO 3- Prepare students to critically analyze existing literature, identify the gaps and propose innovative and research oriented solutions for Big Data.
  • PEO 4- Enable students to pursue lifelong multidisciplinary learning as professional engineers and scientists.
  • PEO 5- Enable students to effectively communicate technical information, function effectively on teams, and apply computer engineering solutions within a global, societal, and environmental context by following ethical practices.

PROGRAMME OUTCOMES (POs)

  • Apply knowledge of Mathematics, Science, Engineering fundamentals and an engineering Specialization for hazard identification, risk assessment, analysis the source of incidents and control of occupational Dieses & hazards.
  • Design, Establish, Implement maintain and continually improve an occupation health and safety management system to improve safety.
  • Conduct investigations on unwanted incidents using e.g. (Root cause analysis, what if analysis) and generate corrective and preventive action to prevent repetition and happening of such incidents.
  • Design complex man, machine, and material handling systems using human factors engineering tools so as to achieve comfort, worker satisfaction, efficiency, error free and safe work practice workplace environment.
  • Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings so as to provide practical solutions to safety problems.
  • Communicate effectively on occupational health and safety matters among the employees and with society at large.
  • Demonstrate understanding of the societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to occupation health and safety practices.
  • Understand and commit to comply with legal and contractual requirements, professional ethics and responsibilities and general norms of engineering practice.
  • Understand the impact of Health safety and environment solutions on productivity, quality and humanity protection at large.
  • Demonstrate the use of state of the art occupational health and safety practices in controlling risks of complex engineering activities and understand their limitations.

PROGRAMME SPECIFIC OUTCOMES (PSOs)

  • PSO – 1 Ability to Utilize Data Science Principles
  • PSO – 2 Ability to Analyze Data, Software & Programming
  • PSO – 3 Analysis and Interpretation of data
LIST OF MAJOR EQUIPMENTS
Big Data Analytics Laboratory

Advanced Data Structures Lab

Learning objectives :

  • To understand heap and various tree structures like AVL, Red-black, B and Segment trees
  • To understand the problems such as line segment intersection, convex shell and Voronoi diagram

List Of Major Equipments

  • Standalone desktops with C compiler, C++ compiler.
  • Server with C compiler, C++ supporting 18 terminals or more.

Big Data Computing Laboratory

Learning objectives :

  • To set up single and multi-node Hadoop Clusters.
  • To solve Big Data problems using Map Reduce Technique.
  • To learn NoSQL query.
  • To design algorithms that uses Map Reduce Technique to apply on Unstructured and structured data.
  • To learn Scalable machine learning using Mahout.

List Of Major Equipments

  • IBM X3650 M4 Big Data model
  • Hadoop software
  • Installation of PIG,HIVE 

Big Data Query Languages Laboratory

Learning objectives :

  • To understand the basic programming constructs of R and understand the use of R in Big Data analytics.
  • To solve Big data problems using Map Reduce Technique in R, HADOOP.
  • To develop Pig scripts for analyzing large un-structured and semi-structured data.
  • To develop program for Query processing using Hive.
  • To perform analytics on Big data streams using Hadoop Streaming API.To learn to work on Sqoop.

List Of Major Equipments

  • IBM X3650 M4 Big Data model
  • Hadoop software
  • Installation of PIG,HIVE

Machine Learning Techniques Laboratory

Learning objectives :

  • To apply the concepts of Machine Learning to solve real-world problems
  • To implement basic algorithms in clustering & classification applied to text & numeric data
  • To implement algorithms emphasizing the importance of bagging & boosting in classification & regression
  • To implement algorithms related to dimensionality reduction
  • To apply machine learning algorithms for Natural Language Processing applications

List Of Major Equipments

  • Netbeans
  • Weka Tool
  • Mat Lab

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