In recent years, careers in artificial intelligence (AI) have grown exponentially to match up with the demands of digitally transformed industries. Although there are plenty of jobs in artificial intelligence, there is a significant shortage of tech talent with the necessary skills.

According to the job site Indeed, the demand for AI skills has more than doubled over the past three years, and the number of job postings is up by 119 percent. This suggests that employers are going to struggle to fill these positions for many years. And students and aspirants getting into the field of Artificial Intelligence are going to find themselves among the continuous increase in demand for their talent.

• Top 5 Careers in Artificial Intelligence
• Machine Learning Engineer
• Data Scientist
• Business Intelligence Developer
• Research Scientist
• Big Data Engineer/Architect

Machine Learning Engineer

The role of a machine learning engineer is at the heart of AI projects and is suitable for those who hail from a background in applied research and data science. However, it’s also necessary to be an AI programmer and demonstrate a thorough understanding of multiple programming languages.

Machine learning engineers should also be able to apply predictive models and leverage natural language processing when working with enormous datasets.

To get hired, it will help if candidates are highly experienced with agile development practices and familiar with leading software development IDE tools like Eclipse and IntelliJ.

Data Scientist

Data scientists are charged with collecting, analyzing, and interpreting large, complex datasets by leveraging both machine learning and predictive analytics. They also play a vital role in developing algorithms that enable the collection and cleaning of data for analysis. Beyond the ability to understand unstructured data, data scientists are also required to demonstrate strong analytical and communication skills to seamlessly communicate their findings with business leaders.

Business Intelligence Developer

Careers in artificial intelligence also include the position of business intelligence (BI) developer. The primary objective of this role is to analyze complex data sets to identify business and market trends. Business intelligence developers play a key role in improving the efficiency and profitability of a business.

Business intelligence developers are typically responsible for designing, modeling, and maintaining complex data in highly accessible cloud-based data platforms.

Those who are interested in this role need to possess strong technical and analytical skills. Candidates should be able to communicate with non-technical colleagues and display strong problem-solving skills.

Unlike other artificial intelligence careers on this list, business intelligence developers traditionally only have been required to have a bachelor’s degree in engineering, computer science, or a related field. However, a combination of on-the-job experience and certifications is highly desired.

As artificial intelligence starts to transform new industries, the demand for business intelligence developers will continue to grow rapidly.

Research Scientist

One of the leading careers in artificial intelligence is the job of the research scientist which is visible by the high salaries that people in this domain command. These individuals are experts in multiple AI disciplines, including applied mathematics, machine learning, deep learning, and computational statistics.

To get hired, candidates should demonstrate extensive knowledge and experience in computer perception, graphical models, reinforcement learning, and natural language processing. Most hiring companies are on the lookout for technology professionals who have an in-depth understanding of benchmarking, parallel computing, distributed computing, machine learning, and artificial intelligence.

Big Data Engineer/Architect

Big data engineers and architects have among the best paying jobs in artificial intelligence. As big data engineers and architects play a vital role in developing an ecosystem that enables business systems to communicate with each other and collate data, most companies prefer professionals who have a professional degree in mathematics, computer science, or a related field.

Compared to data scientists, this role can feel more involved, as big data engineers and architects typically are tasked with planning, designing, and developing the big data environment on Hadoop and Spark systems. Candidates also have to demonstrate significant programming experience with C++, Java, Python, and Scala. They also have to show in-depth knowledge and experience engaging in data mining, data visualization, and data migration.

Semester 1

• Engineering Mathematics - 3
• Elements of Computing System - 3
• Introduction to Electrical and Electronics Engineering - 3
• Basic Electrical & Electronics Engineering (Lab) - 2
• Fundamental Programming using Python
• Computing Systems (Lab)
• Programming using Python (Lab) - 2
• English communication - 3
• Communications – I(Lab) - 2

Semester 2

• Discrete Mathematics - 3
• Statistical Learning for AI - 3
• Operating System – Building Blocks - 3
• Design Thinking - 3
• Digital Electronics - 3
• Digital Electronics (Lab) - 2
• Business & Technical Communication - 2
• Computer Aided Design & Drafting {Lab} - 2
• Environmental Studies - 2

Semester 3

• Computer Networks - 3
• Probability And Random Variables -3
• Cloud Computing -3
• Data Structures & Algorithms - 3
• OOPS with Java - 3
• OOPS with Java (Lab) -2
• Data Structures & Algorithms (Lab) - 2
• Computer Networks lab -2
• Computational systems Biology -2

Semester 4

• AI and Intelligent Agents - 3
• Database Management System - 3
• Software Engineering - 3
• Design and Analysis of Algorithms - 3
• Robotic Operating Systems & Robot Simulation - 3
• Robotic Operating Systems & Robot Simulation (Lab) - 2 (Robotic Invigilator)
• DataBase Management System With SQL (Lab) - 2
• Online Social Network Analysis (Lab)- 2
• Shell Scripting (Lab) - 2

Semester 5

• Formal language and Automata - 3
• Ethics in Computer Science -3
• Prolog Programming -3
• Signal & Image Processing - 3
• Signal & Image Processing (Lab) - 2
• Data Mining & ML - 3
• Data Mining & ML using Python (Lab) - 2
• Prolog Programming (Lab) - 2
• Electives : 2
• Elective - Information Security
• Database Security
• Business Intelligence
• Anandam - 1

Semester 6

• Big Data Analytics -3
• Compiler Design -3
• Advanced Machine Learning -3
• Graphical Model -3
• Natural Language Processing-3
• Advanced Machine Learning with Python(Lab) - 2
• Big Data Analytics (Lab) - 2
• Natural Language Processing (Lab) -2
• Electives : 2(Lab)
• Elective - Blockchain, Internet of Things, Management Information Security
• Anandam II- 1

Semester 7

• Fuzzy Logic and Application -3
• Supervised and Unsupervised Learning - 3
• Online Machine Learning/Bandit Algorithm -3
• R Programming -3
• Project Formulation and Appraisal -3
• Campus Recruitment Training - 2
• Fuzzy Logic and Application (Lab) -2
• Mini Project (Lab) -2
• Electives : 2
• Elective - Orientation program in Enterpreneurship, Research Methodology
• Anandam III- 1

Semester 8

• Industrial Training/Internship

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.

• VGU Jaipur
• Pacific University

Data Science is gaining unprecedented traction in the job market, as Big Data, data analytics, data mining and machine learning become more relevant to the mainstream IT industry. Around the world, organizations are fiercely fighting with each other for skilled data professionals available in the market. As a result, the financial packages for different data science roles are consistently going into overdrive.

It won’t be an exaggeration to say that data science is an exciting career choice for someone interested in crunching numbers on a huge scale. But what are the probable job roles that data science professionals are expected to play in the real world? Here are the five key career paths listed out that are currently in high demand in the data science industry. The salary figures given are in US dollar terms which can be translated into currency of your country.

1. Data Scientist

A data scientist should ideally be the ‘Jack of all trades’ related to data science. Data scientists are responsible for designing and implementing processes used for modelling, data mining and research purposes. At the same time, they also contribute towards the development of data mining architectures, modelling standards, reporting and data analysis methodologies. Research is an integral part of a data scientist’s job. They also work closely with application developers to create data definitions for new databases and extract data relevant for analysis.

2. Data Architect

Data architect is undoubtedly the most sizzling job title that you can put on your curriculum vitae in 2016. Data architects develop the underlying architecture to analyze and process data in the way the organization needs it. A data architect creates the blueprint of a data science project by integrating, centralizing, protecting and maintaining the source of data from a wide range of data management systems and technologies. The ultimate objective of data architects is to make sure that the entire data environment is always available, stable and secure.

3. Data Engineer

Data engineers are the building blocks of any data science project. They are the driving force behind the design, construction, implementation and maintenance of highly scalable data management systems for data science projects. Data engineers build high-performance algorithms, prototypes, and conceptual models according to the blueprint designed by data architects. The ultimate responsibility of data engineers is to provide clean, valid and usable data to statisticians and data analysts for the purpose of data analysis.

4. Data Analyst

Data analyst is the Hercule Poirot of a data science team. This role is precisely of a detective who will look into the data to extract meaningful insights. A ‘figure-it-out’ attitude is the prerequisite to be a successful data analyst. A data analyst is expected to collect numerical information and present the result in a meaningful way – usually in the form of graphs, charts, reports or dashboards. Identifying trends and creating predictive models are among the key responsibilities that a data analyst is expected to take care of.

5. Statistician

It is the responsibility of a statistician to collect, analyze and interpret quantitative data with the help of statistical theories and methodologies. They are the ones who determine methods for collecting data and decide what data are needed to answer specific questions or problems. Strong background in statistical methodologies coupled with a logical and stats-oriented mindset are the key qualities that a statistician must possess.

6. Database Administrator

A database administrator (DBA) uses specialized software to store and organize data. Ensuring the performance, integrity and security of a database system is the primary responsibility of a database administrator. Database administrators ensure that the database is always available to all the concerned users, is performing properly and is being handled securely at all the time. They are also involved in capacity planning, database design, security configuration, backup & recovery solution, performance monitoring, and troubleshooting.

Semester 1

• Engineering Mathematics - 3
• Elements of Computing System - 3
• Introduction to Electrical and Electronics Engineering - 3
• Basic Electrical & Electronics Engineering (Lab) - 2
• Fundamental Programming using Python
• Computing Systems (Lab)
• Programming using Python (Lab) - 2
• English communication - 3
• Communications – I(Lab) - 2

Semester 2

• Engineering Mathematics II
• Physics for Engineers II
• Environmental Science
• Design Thinking
• Web Designing
• Communication Techniques lab
• Physics for Engineers II lab
• Web Designing lab
• Computer Aided Graphics
• Soft Skills and Communication

Semester 3

• Statistics and Probability
• Digital Electronics
• Data Structures using C
• OOP with Java
• Database Management Systems
• Communication skills
• Data Structures using C lab
• OOP with Java lab
• Database Management Systems lab
• Summer Project Seminar I

Semester 4

• Design and analysis of Algorithm
• Operating system
• Computer Organization and Architecture
• Computer Networks
• R-Programming Language
• Design and analysis of Algorithm lab
• Employability skills
• Computer Networks lab
• Operating system lab
• R-Programming Language Lab
• Orientation Program in Entrepreneurship

Semester 5

• Theory of Computation
• Statistical Inference
• Python Programming
• Machine Learning
• Exploratory Data Analysis
• Programme Elective I
• Programme Elective I lab
• Professional skills
• Machine Learning lab
• Python Programming lab
• Summer Project Seminar II

Semester 6

• Artificial Intelligence
• Big data Analytics
• Dimension Reduction and Model Validation
• Programme Elective II
• Programme Elective III
• Open Elective I
• Reasoning and Aptitude
• Programme Elective II lab
• Programme Elective III lab
• Artificial Intelligence lab
• Big data Analytics lab
• Dimension Reduction and Model Validation lab
• Intermediate Program in Entrepreneurship

Semester 7

• Elective IV
• Elective V
• Elective VI
• Open Elective II
• Economics for Engineers
• Project I
• Summer Project Seminar III

Semester 8

• Practical Training in Industry

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.

• VGU Jaipur
• Pacific University

India is expected to see more than a million cloud computing job roles by 2022 as more organisations shift their operations to the cloud infrastructure, says a report by Great Learning.

As the Indian cloud computing market, currently at $2.2 billion, is expected to grow to $4 billion by 2020 with an annual growth rate of more than 30%; IDC estimated more than one million new jobs to be created in India.

Skills in DevOps, software-as-a-service, infrastructure-as-a-service, automation, agile and software-defined networks are going to be critical for IT professionals to land these jobs. Keeping these skills in mind; the job profiles such as Cloud Architect, Cloud Software Engineer, Cloud Enterprise Architect and Cloud Infrastructure Engineer are in great demand, according to the report.

Roles in cloud computing will offer much higher salary package than the traditional IT services roles, and they could often double the current pay offered to the IT professionals.

In India, the salary of an entry level cloud professional varies between Rs 5 lakh to Rs 7 lakh per annum which is greater than that of a traditional IT engineer who earns about Rs. 3 lakh to 5 lakh, noted the report. “The salary for an associate working in cloud with less than 5 years of experience can range from Rs 12 to 19 lakh, while a mid-level manager can easily command upwards of Rs. 20 lakh. Cloud Architects, a new role in the cloud space of an expert who understands both IT infrastructure and applications, and can design and manage application frameworks and operations, can earn upwards of Rs 30 lakhs. In fact, MNCs like Oracle have been known to pay senior cloud professionals with 15 – 25 years of experience as much as Rs 1 crore per annum,” said Great Learning.

The report, however, mentioned that the industry has a dearth of talent with more than 1.7 million cloud jobs worldwide remaining vacant. Citing IDC data, the report said there is only one qualified candidate for 100 job postings in cloud computing across the globe. “The major reasons for this gap are lack of training, hands on experience in a cloud-based environment and industry recognized certification. This skill gap comes at a time when almost two-thirds of global enterprises are using cloud computing; and the investment being made in cloud infrastructure is 4.5 times the rate of traditional IT spending,” Great Learning report pointed out.

Semester 1

• Engineering Mathematics - 3
• Elements of Computing System - 3
• Introduction to Electrical and Electronics Engineering - 3
• Basic Electrical & Electronics Engineering (Lab) - 2
• Fundamental Programming using Python
• Computing Systems (Lab)
• Programming using Python (Lab) - 2
• English communication - 3
• Communications – I(Lab) - 2

Semester 2

• Discrete Mathematics-II
• Statistical Learning for AI - 3
• Operating System – Building Blocks - 3
• Design thinking
• Web Designing
• Web Designing Lab
• Business & Technical Communication - 2
• Computer aided graphics
• Environmental Science

Semester 3

• Probability And Random Variables -3
• Cloud Computing-3
• Data Structures & Algorithms - 3
• OOPS with Java
• OOP with Java lab
• Data Structures & Algorithms (Lab) - 2
• Computer Networks lab -2
• Computer Networks -3
• Cloud Computing Lab

Semester 4

• DataBase Management System With SQL (Lab) - 2
• DataBase Management System
• Design and Analysis of Algorithm-3
• Software Engineering- 3
• Storage and Datacenter lab
• Computer Organization and architecture
• Information Security
• Storage and Datacenter
• Shell Scripting (Lab) - 2

Semester 5

• Theory of Computation
• Cloud Computing
• Network Security
• Principles of Virtualization
• Humanities II
• Elective I
• Network Security lab
• Principles of Virtualization lab
• Summer Project Seminar II
• Elective I
  - Security architecture
  - Database Security
  - Server Security

Semester 6

• Artificial Intelligece
• Linux Administration
• Ethical Hacking
• Elective II
• Elective III
• Open Elective I
• Elective II lab
• Linux Administration lab
• Ethical Hacking lab
• Project I
• Elective II
  - Exploring Software as a service
  - Cloud Migration
  - Cloud Scripting using PaaS
• Elective II lab
  - Exploring Software as a service lab
  - Cloud Migration lab
  - Cloud Scripting using PaaS lab
• Elective III
  - Security standards and framework
  - IT Governance and Risk Management
  - Incidents Response Management
• Open Elective I
  - UI/UX Fundamentals
  - Mobile application development
  - Business Intelligence

Semester 7

• Elective IV
• Elective V
• Elective VI
• Open Elective II
• Economics for Engineers
• Elective IV lab
• Project II
• Summer Project Seminar III
• Elective IV
  - Cyber Forensics
  - Web Security and SDLC
  - Cloud Security
• Elective V
  - Hybrid Cloud Computing
  - Cloud web services
  - Cloud computing solutions
• Elective VI
  - Cloud Architectural Patterns
  - Automation and Configuration Management
  - Infrastructure Containers
• Open Elective II
  - Artificial Intelligence
  - Big data Analytics
  - Data Science
• Elective IV lab
  - Cyber Forensics lab
  - Web security and SDLC lab
  - Cloud Security lab

Semester 8

• Project III/Internship

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.

• VGU Jaipur
• Pacific University

In recent years, careers in artificial intelligence (AI) have grown exponentially to match up with the demands of digitally transformed industries. Although there are plenty of jobs in artificial intelligence, there is a significant shortage of tech talent with the necessary skills.

According to the job site Indeed, the demand for AI skills has more than doubled over the past three years, and the number of job postings is up by 119 percent. This suggests that employers are going to struggle to fill these positions for many years. And students and aspirants getting into the field of Artificial Intelligence are going to find themselves among the continuous increase in demand for their talent.

• Top 5 Careers in Artificial Intelligence
• Machine Learning Engineer
• Data Scientist
• Business Intelligence Developer
• Research Scientist
• Big Data Engineer/Architect

Machine Learning Engineer

The role of a machine learning engineer is at the heart of AI projects and is suitable for those who hail from a background in applied research and data science. However, it’s also necessary to be an AI programmer and demonstrate a thorough understanding of multiple programming languages.

Machine learning engineers should also be able to apply predictive models and leverage natural language processing when working with enormous datasets.

To get hired, it will help if candidates are highly experienced with agile development practices and familiar with leading software development IDE tools like Eclipse and IntelliJ.

Data Scientist

Data scientists are charged with collecting, analyzing, and interpreting large, complex datasets by leveraging both machine learning and predictive analytics. They also play a vital role in developing algorithms that enable the collection and cleaning of data for analysis. Beyond the ability to understand unstructured data, data scientists are also required to demonstrate strong analytical and communication skills to seamlessly communicate their findings with business leaders.

Business Intelligence Developer

Careers in artificial intelligence also include the position of business intelligence (BI) developer. The primary objective of this role is to analyze complex data sets to identify business and market trends. Business intelligence developers play a key role in improving the efficiency and profitability of a business.

Business intelligence developers are typically responsible for designing, modeling, and maintaining complex data in highly accessible cloud-based data platforms. Those who are interested in this role need to possess strong technical and analytical skills. Candidates should be able to communicate with non-technical colleagues and display strong problem-solving skills.

Unlike other artificial intelligence careers on this list, business intelligence developers traditionally only have been required to have a bachelor’s degree in engineering, computer science, or a related field. However, a combination of on-the-job experience and certifications is highly desired.

As artificial intelligence starts to transform new industries, the demand for business intelligence developers will continue to grow rapidly.

Research Scientist

One of the leading careers in artificial intelligence is the job of the research scientist which is visible by the high salaries that people in this domain command. These individuals are experts in multiple AI disciplines, including applied mathematics, machine learning, deep learning, and computational statistics.

To get hired, candidates should demonstrate extensive knowledge and experience in computer perception, graphical models, reinforcement learning, and natural language processing. Most hiring companies are on the lookout for technology professionals who have an in-depth understanding of benchmarking, parallel computing, distributed computing, machine learning, and artificial intelligence.

Big Data Engineer/Architect

Big data engineers and architects have among the best paying jobs in artificial intelligence. As big data engineers and architects play a vital role in developing an ecosystem that enables business systems to communicate with each other and collate data, most companies prefer professionals who have a professional degree in mathematics, computer science, or a related field.

Compared to data scientists, this role can feel more involved, as big data engineers and architects typically are tasked with planning, designing, and developing the big data environment on Hadoop and Spark systems. Candidates also have to demonstrate significant programming experience with C++, Java, Python, and Scala. They also have to show in-depth knowledge and experience engaging in data mining, data visualization, and data migration.

Semester 1

• English
• Theory of Mathematics
• Computer Architecture and Organization
• Programming in C
• Client Side scripting
• Office Automation
• Programming in C Lab
• Client Side scripting Lab
• HSM Effective Communication Skills

Semester 2

• English-II
• Linux Shell Scripting
• Object Oriented Programming Using Java
• Operating Systems
• Data Structures using C
• Environmental Studies
• Data Structures using C Lab
• Linux Shell Scripting Lab
• Object Oriented Programming Using Java Lab
• Personality Development

Semester 3

• DBMS
• Computer Networks
• Python Programming
• Statistics and Probability
• Digital Electronics
• DBMS Lab
• Python Programming Lab
• Business communication and Presentation Skills
• Summer Project Seminar I
• Elective-I


• Program Elective-I
• Data Visualization
• Business Intelligence

Semester 4

• Introduction to RPA Tools
• Introduction to Intelligent Process Automation
• Sensor Technology
• Business Process Management
• Elective-II
• Introduction to Intelligent Process Automation Lab
• Introduction to RPA Tools Lab
• Logical Reasoning and Thinking


• Program Elective – II
• Introduction to Data Science
• Pattern Recognition

Semester 5

• Six Sigma and Lean Methods
• Digital Image Processing
• Elective-III
• Elective-IV
• Elective-V
• Generic Elective-I
• Digital Image Processing Lab
• Elective-III Lab
• Mini Project
• Working Towards Placements
• Summer Project Seminar II


• Program Elective-III
• Test Automation using Selenium
• Software Testing


• Program Elective-IV
• Introduction to web Services
• Digital Signal Processing


• Program Elective-V
• Embedded Systems
• Design Thinking


• Generic Elective-I
• Introduction to IoT
• Cloud Computing


• Program Elective-III Lab


• Test Automation using Selenium Lab
• Software Testing Lab

Semester 6

• Major Project / Internship

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.

Data Science is gaining unprecedented traction in the job market, as Big Data, data analytics, data mining and machine learning become more relevant to the mainstream IT industry. Around the world, organizations are fiercely fighting with each other for skilled data professionals available in the market. As a result, the financial packages for different data science roles are consistently going into overdrive.

It won’t be an exaggeration to say that data science is an exciting career choice for someone interested in crunching numbers on a huge scale. But what are the probable job roles that data science professionals are expected to play in the real world? Here are the five key career paths listed out that are currently in high demand in the data science industry. The salary figures given are in US dollar terms which can be translated into currency of your country.

1. Data Scientist

A data scientist should ideally be the ‘Jack of all trades’ related to data science. Data scientists are responsible for designing and implementing processes used for modelling, data mining and research purposes. At the same time, they also contribute towards the development of data mining architectures, modelling standards, reporting and data analysis methodologies. Research is an integral part of a data scientist’s job. They also work closely with application developers to create data definitions for new databases and extract data relevant for analysis.

2. Data Architect

Data architect is undoubtedly the most sizzling job title that you can put on your curriculum vitae in 2016. Data architects develop the underlying architecture to analyze and process data in the way the organization needs it. A data architect creates the blueprint of a data science project by integrating, centralizing, protecting and maintaining the source of data from a wide range of data management systems and technologies. The ultimate objective of data architects is to make sure that the entire data environment is always available, stable and secure.

3. Data Engineer

Data engineers are the building blocks of any data science project. They are the driving force behind the design, construction, implementation and maintenance of highly scalable data management systems for data science projects. Data engineers build high-performance algorithms, prototypes, and conceptual models according to the blueprint designed by data architects. The ultimate responsibility of data engineers is to provide clean, valid and usable data to statisticians and data analysts for the purpose of data analysis.

4. Data Analyst

Data analyst is the Hercule Poirot of a data science team. This role is precisely of a detective who will look into the data to extract meaningful insights. A ‘figure-it-out’ attitude is the prerequisite to be a successful data analyst. A data analyst is expected to collect numerical information and present the result in a meaningful way – usually in the form of graphs, charts, reports or dashboards. Identifying trends and creating predictive models are among the key responsibilities that a data analyst is expected to take care of.

5. Statistician

It is the responsibility of a statistician to collect, analyze and interpret quantitative data with the help of statistical theories and methodologies. They are the ones who determine methods for collecting data and decide what data are needed to answer specific questions or problems. Strong background in statistical methodologies coupled with a logical and stats-oriented mindset are the key qualities that a statistician must possess.

6. Database Administrator

A database administrator (DBA) uses specialized software to store and organize data. Ensuring the performance, integrity and security of a database system is the primary responsibility of a database administrator. Database administrators ensure that the database is always available to all the concerned users, is performing properly and is being handled securely at all the time. They are also involved in capacity planning, database design, security configuration, backup & recovery solution, performance monitoring, and troubleshooting.

Semester 1

• English
• Theory of Mathematics
• Computer Architecture and Organization
• Programming in C
• Client Side scripting
• Office Automation
• Programming in C Lab
• Client Side scripting Lab
• HSM Effective Communication Skills

Semester 2

• English-II
• Linux Shell Scripting
• Object Oriented Programming Using Java
• Operating Systems
• Data Structures using C
• Environmental Studies
• Data Structures using C Lab
• Linux Shell Scripting Lab
• Object Oriented Programming Using Java Lab
• Personality Development

Semester 3

• DBMS
• Computer Networks
• Statistics and Probability
• Optimization Techniques
• Software Engineering
• DBMS Lab


• Business Communication and Presentation Skills
• Summer Project
• Electives-I


• Program Elective-I
• Data Analytics using Excel
• Data Warehousing and ETL Techniques

Semester 4

• Machine Learning
• Python Programming
• NoSQL Database
• Cloud Computing
• Elective-II
• Machine Learning Lab
• Python Programming Lab
• NoSQL Database Lab
• Logical Reasoning and Thinking


• Program Elective-II
• Sampling Methods
• Statistical Inference

Semester 5

• Big Data Analytics
• Dimension Reduction and Model Validation
• Elective-III
• Elective-IV
• Elective-V
• Generic Elective-I
• Big Data Analytics Lab
• Elective -V Lab
• Mini Project
• Working Towards Placements
• Summer Project Seminar II


• Program Elective-III
• Artificial Neural Network
• Social Media Analytics


• Program Elective -IV
• Predictive Analytics
• Data Mining


• Program Elective-V
• Data Visualization
• Business Intelligence


• Generic Elective-I
• R Programming
• Artificial Intelligence


• Program Elective-V Lab
• Data Visualization Lab
• Business Intelligence Lab

Semester 6

• Major Project / Internship

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.

India is expected to see more than a million cloud computing job roles by 2022 as more organisations shift their operations to the cloud infrastructure, says a report by Great Learning.

As the Indian cloud computing market, currently at $2.2 billion, is expected to grow to $4 billion by 2020 with an annual growth rate of more than 30%; IDC estimated more than one million new jobs to be created in India.

Skills in DevOps, software-as-a-service, infrastructure-as-a-service, automation, agile and software-defined networks are going to be critical for IT professionals to land these jobs. Keeping these skills in mind; the job profiles such as Cloud Architect, Cloud Software Engineer, Cloud Enterprise Architect and Cloud Infrastructure Engineer are in great demand, according to the report.

Roles in cloud computing will offer much higher salary package than the traditional IT services roles, and they could often double the current pay offered to the IT professionals.

In India, the salary of an entry level cloud professional varies between Rs 5 lakh to Rs 7 lakh per annum which is greater than that of a traditional IT engineer who earns about Rs. 3 lakh to 5 lakh, noted the report. “The salary for an associate working in cloud with less than 5 years of experience can range from Rs 12 to 19 lakh, while a mid-level manager can easily command upwards of Rs. 20 lakh. Cloud Architects, a new role in the cloud space of an expert who understands both IT infrastructure and applications, and can design and manage application frameworks and operations, can earn upwards of Rs 30 lakhs. In fact, MNCs like Oracle have been known to pay senior cloud professionals with 15 – 25 years of experience as much as Rs 1 crore per annum,” said Great Learning.

The report, however, mentioned that the industry has a dearth of talent with more than 1.7 million cloud jobs worldwide remaining vacant. Citing IDC data, the report said there is only one qualified candidate for 100 job postings in cloud computing across the globe. “The major reasons for this gap are lack of training, hands on experience in a cloud-based environment and industry recognized certification. This skill gap comes at a time when almost two-thirds of global enterprises are using cloud computing; and the investment being made in cloud infrastructure is 4.5 times the rate of traditional IT spending,” Great Learning report pointed out.

Semester 1

• English
• Theory of Mathematics
• Computer Architecture and Organization
• Programming in C
• Client Side scripting
• Office Automation
• Programming in C Lab
• Client Side scripting Lab
• HSM Effective Communication Skills

Semester 2

• English-II
• Linux Shell Scripting
• Object Oriented Programming Using Java
• Operating Systems
• Data Structures using C
• Environmental Studies
• Data Structures using C Lab
• Linux Shell Scripting Lab
• Object Oriented Programming Using Java Lab
• Personality Development

Semester 3

• DBMS
• Computer Networks
• Informatiion Security
• Priniciples of Virtualization
• Cloud Computing
• DBMS Lab
• Priniciples of Virtualization Lab
• Business Communication and Presentation Skills
• Summer Project
• Elective-I


• Program Elective-I
• Linux Administration
• Server Administration

Semester 4

• Storage and Datacenter
• Python Programming
• Cloud Web services
• Ethical Hacking
• Elective-II
• Ethical Hacking Lab
• Python Programming Lab
• Cloud Web Services Lab
• Logical Reasoning and Thinking


• Program Elective-II
• Network Security
• Database security fundamentals

Semester 5

• Digital Forensics and Investigation
• Cloud Migration
• Elective -III
• Elective - IV
• Elective -V
• Generic Elective – I
• Digital Forensics and Investigation Lab
• Elective – III Lab
• Mini Project
• Working Towards Placements
• Summer Project Seminar II


• Program Elective-III
• PowerShell Scripting
• Infrastructure Automation


• Program Elective-IV
• Cloud Security
• Application and Web Security


• Program Elective-V
• IT Governance, Risk, & Information Security Management
• Infrastructure Solutions on Cloud


• Generic-Elective-I
• IT Governance, Risk, & Information Security Management
• Infrastructure Solutions on Cloud


• Program Elective-III Lab
• PowerShell Scripting Lab
• Infrastructure Automation Lab

Semester 6

• Major Project / Internship

Pass with 50% marks (45% for SC/ST) in 10+2 with Physics & Mathematics as compulsory subjects along with one of the Following: Chemistry / Biotechnology / Biology / Technical Vocational Subjects or 2 Year Diploma after 10+2 or 3 Years Diploma after 10th.