PG Diploma in Data Analytics and Networking | PGD DA & N

This PG Diploma Programme at K. J. Somaiya College of Engineering (SVU) will acquaint the participants with data analytics and networking concepts. There is a growing demand for data analytics experts across many domains including finance, technology, education, healthcare, public sector to improve performance and sophistication of an organization. This programme will help the beginners to understand the fundamentals about the Data Analytics and networking along with hands on practical sessions with industry required software tools. Latest technologies such as big data analysis, data science, cloud computing, AI & ML, system virtualization etc. will be covered in first semester.

In next semester, this programme will provide industry oriented training related to the field of data communication. Domains such as Computer Communication Networks, Network Security, IoT etc. will be emphasized upon. Participants will benefit in the form of hands-on experience on industry grade infrastructure used for data networking. Additionally, participants will get an opportunity to get certification (through CISCO Academy) recognized by networking industry. The PG programme will help fresh graduates and early career professionals to acquire good placement opportunities through campus placements. This programme will set a strong foundation for promising career in the field of Data Analytics and Networking.

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Key Information

Objectives

  • Provide basic and fundamental information about the field of data science and networking concepts
  • Provide hands on experience on the widely used software and provide industry oriented training for data analysis and networking
  • Make the participants industry ready to pursue career in the field of data analytics and networking

Learning Outcomes

After completion of the PG diploma programme in Data Analytics and Networking, participants will be able

  • LO1:To understand fundamentals of big data, data science, AI & ML and data network technology
  • LO2: To learn and apply different software tools required for data analysis .
  • LO3: To demonstrate knowledge of computer networking and related security
  • LO4:To understand the various concepts related to sensor network and IoT
  • LO5: To learn the technologies through project based learning to solve practical problems

Assessment Method

  • Open book tests / MCQs (multiple-choice questions) / mini project / case studies etc.
  • Assessment based on laboratory activities and projects

Eligibility Criteria

  • Graduation with minimum 6 months of work experience in relevant field and holding a minimum 3 years Bachelor's Degree in Computer Science / Information Technology / Electronics / Instrumentation / Electronics and Telecommunication / Mathematics / Statistics / Physics / Computational Sciences from any recognized university with minimum 40% in the qualifying degree OR
  • Engineering with Computer Science / Information Technology / Electronics / Instrumentation / Electronics and Telecommunication / Mathematics / Statistics / Physics / Computational Sciences / Allied programs / Any Engineering Graduate with minimum 40% in the qualifying degree from any recognized university OR
  • Engineering Diploma in Computer Science / Information Technology, Electronics and Telecommunication / Allied programmes with minimum 2 years of work experience in relevant field

List of Courses

SEM -I
  • Data Science: Principles and Practices
  • Machine Learning
  • Big Data and Hadoop
  • Elective
    • System Virtualization and Cloud Computing
    • Artificial Intelligence
    • Python for Data Analysis and Scientific Computing
    • Data Analytics with R programming
  • Project –I
  • Project -II
SEM -II
  • Computer Communication Networks
  • Cryptography and Network Security
  • Sensor Networks and IoT
  • Elective
    • Optical Networks
    • Wireless Networks
    • Embedded Networking
    • Internet and Voice Communication
  • Project –I
  • Project -II

Highlights & Mode of Study

Highlights

  • Syllabus aligned to industry-recognized skills with emphasis on hands on training of the techniques and tools
  • Understanding the latest tools and technologies used in industry
  • Support of Cisco Centre of Excellence setup
  • Experiential Learning : Courses backed by laboratories/projects for practical exposure
  • Flexible Schedule for working professionals

Mode of Study

  • Part Time
  • Online and / or Offline
  • 15 Hrs per week + Project
  • Flexibility in conduction of theory and laboratory courses