Expert Certificate Programs

EXPERT CERTIFICATE PROGRAM

Courses in the "Expert Certificate Program" are in the following specializations:

·        Project Management

·        Data Science

·        Machine Learning

·        CyberSecurity

·        Big Data

·        Cloud Computing & Virtualization

·        BlockChain

·        Deep Learning

 

All the above are "Post-Graduate Diploma Courses" and are offered in a package of three courses.  It's the student's choice to select any of the above courses and build a customized package based on his learning needs.  The maximum duration for completion of the courses chosen is 12 months.  The course content is that of SKILLSOFT on its SKILLPORT LMS.

CERTIFICATION:

Successful students are eligible for certification by the RANGNEKAR INSTITUTE OF MANAGEMENT STUDIE AND RESEARCH, Bengaluru.   

COURSE WORK:

Foundation Courses (Common to all the Expert Certificate Programs):

FC 607 Management Basics              

FC 608 Basics of Economics and Statistics

FC 609 Fundamentals of Accounting & Strategic Financial Management

FC 610 Managing Human Resource in The 21st Century

FC 611 Effective Marketing & Basic Selling Skills

(*Exempted for ACA, AICWA, ACS, FCA, M.Com, MBA, B.Com, BBM, BBA Graduates, and others as decided by RIMSR).

Concentration Courses:

PROJECT MANAGEMENT

CC 501 Principles of Project Management

CC 502 Project Leadership

CC 503 Project Best Practices – I

CC 504 Project Best Practices – II

CC 505 Project Practicum.

DATA SCIENCE

CCDS 001 Data Science Essentials

CCDS 002 Python for Data Science

CCDS 003 Data Science Using R

CCDS 004 Streaming Data Architectures

CCDS 005 Implementing Data Access & Governance Policies

CCDS 006 Statistics for Data Science #1

CCDS 007 Statistics for Data Science #2

CCDS 008 MongoDB for Data Wrangling

CCDS 009 Trifacta for Data Wrangling

CCDS 010 Deploying Data Tools for All Users

CCDS 012 Data Engineering Fundamentals

CCDS 013 Data Lakes in Practice

CCDS 014 Integrating Data Sources from the Edge

CCDS 015 Creating Data APIs for Customers

CCDS 016 Raw Data to Insights

CCDS 017 Data Research in Practice

CCDS 018 Enterprise Value Through Data

CCDS 019 Using Data to Find Data

CCDS 020 Data Visualization with Tableau for Beginners

CCDS 021 Data Visualization Essentials

CCDS 022 Tableau Data Visualization and Analytics

CCDS 023 Talend Data Integration

CCDS 024 Math for Data Science

MACHINE LEARNING

CCML 001 Perform Cloud Data Science with Azure Machine Learning

CCML 002 Powering Recommendation Engines

CCML 003 Linear Regression Models

CCML 004 Research Topics in ML and DL

CCML 005 Designing for Automation and Robotics

CCML 006 Deep Learning and Computer Vision

CCML 007 Planning for AI

CCML 008 Building ML Training Sets

CCML 009 Exploring Machine Learning

CCML 010 Exploring Artificial Intelligence

CCML 011 Developing AI and Machine Learning Solutions with Python

CCML 012 Developing AI and ML Solutions with Java

CCML 013 AI Development with TensorFlow

CCML 014 Bayesian Methods for Machine Learning

CCML 015 ML Engineer

CCML 016 Predictive Modeling Best Practices

CCML 017 ML Programmer to ML Architect - Track 1

CCML 018 ML Programmer to ML Architect - Track 2

CCML 019 ML Programmer to ML Architect - Track 3

CCML 020 ML Programmer to ML Architect - Track 4

CCML 021 Business & Leadership for ML Programmers 

CCML 022 Productivity Tools for ML Programmers

CYBERSECURITY

CCSA 001 Security Analyst Track 1: Security Analyst

CCSA 002 Security Architect Track 2: Forensics Analyst

CCSA 003 Security Architect Track 3: Vulnerability Analyst

CCSA 004 Security Architect Track 4: Security Architect

CCSA 005 Leadership for Security Architects

CCSA 006 Productivity Tools for Security Architects

CCSA 007 Security Threat Intelligence

CCSA 008 Securing User Accounts

CCSA 009 Computer Crime and Forensics

CCSA 010 Cryptography

CCSA 011 Network Security

CCSA 012 Software Development Security

BIG DATA

CCBD 001 Big Data Fundamentals

CCBD 002 Big Data – The Corporate Leadership Perspective

CCBD 003 Big Data - The Marketing Perspective

CCBD 004 Big Data – The Strategic Planning Perspective

CCBD 005 IBM BigInsights Fundamentals

CCBD 006 Big Data – The Engineering Perspective

CCBD 007 Big Data – The Sales Perspective

CCBD 008 Big Data – The Legal Perspective

CCBD 009 Accessing Data with Spark

CCBD 010 Enterprise Governance Strategies

CCBD 011 Balancing the Four Vs of Data

CCBD 012 Apache Storm Introduction

CCBD 013 Apache Kafka

CCBD 014 Apache Solr Essentials

CCBD 015 Designing and Implementing Big Data Analytics

CCBD 016 Big Data Development with Apache Spark

CCBD 017 Hadoop-Data Modeling, Installation and Maintenance

CCBD 018 Hadoop Operations

CCBD 019 Hadoop Ecosystem

CCBD 020 Getting Started with Hadoop

CLOUD COMPUTING & VIRTUALIZATION

CCCV 001 Cloud and Virtualization Fundamentals

CCCV 002 Cloud Computing for Business Professionals

CCCV 003 Cloud Computing for IT Professionals

CCCV 004 Cloud Computing Technology Fundamentals

CCCV 005 CompTIA Cloud+ CVO-002

CCCV 006 Getting Started with Cloud Services

CCCV 007 Cloud Computing for Support Engineers

CCCV 008 Major Cloud Run and Compute Platforms

CCCV 009 Cloud Standards

CCCV 010 Cloud Security

CCCV 011 Cloud Security Management

CCCV 012 Cloud Platform Security

CCCV 013 Cloud Computing Security

CCCV 014 Business Continuity Management

CCCV 015 Certified Cloud Security Professional

CCCV 016 Cloud Security Administration

CCCV 017 CloudOps

CCCV 018 Hybrid Cloud

BLOCKCHAIN

CCBC 001 Blockchains & Ethereum: Introduction

CCBC 002 Blockchains & Ethereum: Performing Transactions in Ethereum

CCBC 003 Blockchains & Ethereum: Mining and Smart Contracts in Ethereum

CCBC 004 Blockchains & Ethereum: Storing Data

CCBC 005 Blockchains & Ethereum: Smart Contract Development

CCBC 006 Blockchains & Ethereum: Metmask & the Ethereum Wallet

CCBC 007 Blockchains & Ethereum: The Geth Client

CCBC 008 Blockchains & Ethereum: Lifecycle of a Smart Contract

CCBC 009 Blockchains & Ethereum: Tool for Smart Contract Development & Ethereum

CCBC 010 Blockchains CCBC 010 Blockchains & Ethereum

CCBC 011 Blockchain Smart Contracts Programmer

CCBC 012 Blockchain Engineer

CCBC 013 Blockchain Solutions Architect

DEEP LEARNING

CCDL 001 Getting Started with Neural Networks

CCDL 002 Building Neural Networks

CCDL 003 Training Neural Networks

CCDL 004 Convolutional Neural Networks

CCDL 005 Sequence Models

CCDL 006 Recurrent Neural Networks

CCDL 007 ML Algorithms

CCDL 008 Linear Algebra

CCDL 009 Linear Regression Models

CCDL 010 Computational Theory

CCDL 011 Model Management

CCDL 012 Bayesian Methods

CCDL 013 Reinforcement Learning

CCDL 014 Math for Machine Learning

CCDL 015 Building ML Training Sets

CCDL 016 Linear Models and Gradient Descent

The principal medium of presentation of these courses is online.  However, RIMSR will facilitate the students with online and/or offline mentoring once a week.  Students registering for these courses are given free access to the online business game titled "MY BUSINESS - MY STRATEGIES."  Registered students will also get a LENOVO pad for free which is pre-loaded with tutorials, exercises, and reading materials.  

ADMISSION CRITERIA:

The following are the admission requirements:-

  • Bachelor’s Degree from a Recognized University.
  • An average score of 50% and above in the Bachelor’s Degree Program.
  • Test Score of any one of the following:- CAT/MAT/XAT/ATMA/CMAT or Any Other Management Aptitude Test (Subject to Review/Waiver)
  • Performance in the Entrance Test, and/or Pre-Admission Interview.
  • Official Transcripts from every undergraduate and graduate institution attended.
  • Work experience is preferred and may entitle a waiver of any of the eligibility criteria given above. The decision of NEF University in this regard is final and binding.

CRITERIA FOR GRADUATION:

The grading criteria for a pass in the tests/ examinations will be as per the norms prescribed by RIMSR, as detailed below:-

  • A grade of “A” signifies an exceptional, clear, and creative grasp of the concepts of the course with a demonstrated ability to apply this knowledge to specific problem situations. It also means that the student has actively participated in in-class activities and has completed all material in a neat and timely manner. The material indicates that the student spent extra time, personal energy, and critical reflection to demonstrate exceptional work.  Grades of 90% and above are rated as “A” and deemed to have passed the examination in the First Division.
  • A grade of “B” signifies a solid and required understanding of the major concepts of the course and the ability to apply those concepts. It also means that the student’s effort and class participation have exceeded the minimal basic requirements for the course. All assignments are judged to be solid in content and completed within time. This is deemed to be the minimum criteria acceptable for graduate-level student performance. Grades in the range of 60% and 89% are rated as “B” and deemed to have passed the examination in the Second Division.
  • A grade of “C” signifies a below-average demonstration and application of the concepts of the course and/or inadequate preparation in-class activities. Grades below 60% are rated as “C” and deemed to have failed in the examination. 

FEE STRUCTURE:

The fee for a package of three(3) courses is Rs. 150,000 and includes e-tutorials, reading materials, assignments, tests, examinations, and certification.  Registered students will also get a LENOVO pad for free which is pre-loaded with tutorials, exercises, and reading materials.  

The fee is payable through bank transfer and the details are as below:

BANK:

UNION BANK OF INDIA, RAJAJINAGAR BRANCH, BANGALORE 560010

POSTAL ADDRESS :

UNION BANK OF INDIA, # 20, 36th Cross, 2nd Block, Rajajinagar, Bangalore 560010, Karnataka, India

SWIFT CODE:

UBININBBGNB

IFSC CODE :

UBIN0541494

MICR CODE :

560026008

BRANCH CODE :

541494

ACCOUNT NO. :

414901010035609

ACCOUNT NAME:

RANGNEKAR INSTITUTE OF MANAGEMENT STUDIES AND RESEARCH

 

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