M.Sc. Artificial Intelligence and Data Analytics, a two-year post graduate degree program is designed to develop their skills especially in the areas of Artificial Intelligence and Data Analytics. This program also provides necessary skills in theoretical computation and mathematics to handle real-world complexities and it aims to meet up a sharp growth in industries. Upcoming trends with respect to the software industry that impact a broad range of applications are demonstrated. Various data-driven AI solutions which can be implemented to reinforce and improve the decision-making set up are instructed. Students can understand the rapid change in the emerging fields of Artificial intelligence and data analytics of computational systems.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
Salient Features
Curriculum based on Research and Development and industry needs
Career prospects in major sectors such as Agriculture. Healthcare, education and Infrastructure, Banking etc,
B.Sc. (Applied Sciences) with Mathematics and Computer Science (10+2+3 scheme) from a recognized university.
Any Bachelor’s degree in science (10+2+3) from a recognized university with at least 3 papers in Computer Science and 1 paper in Mathematics under Annual System.
PEO 1: To prepare graduates to be successful professionals in industry, government, academia, research, entrepreneurial pursuit, and consulting firms.
PEO 2: To prepare graduates to achieve peer recognition, as individuals and as a team players, through demonstration of good analytical, design, implementation, and interpersonal skills.
PEO 3: To prepare graduates to contribute to society as broadly educated, expressive, ethical, and responsible citizens with proven expertise.
PEO 4: To prepare graduates to pursue life-long learning to fulfill their goals.
PROGRAM’S OUTCOMES (PO’S)
Computational knowledge: Apply knowledge of computing fundamentals, computing specialization, mathematics, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements.
Problem analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.
Design/development of solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
Modern tool usage: Modern Tool Usage: Create, select, adapt, and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice.
Innovation and Entrepreneurship: Identify a timely opportunity and use innovation to pursue that opportunity to create value and wealth for the betterment of the individual and society at large.
Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practice.
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
Communication: Communicate effectively with the computing community, and with society, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.
Project management and finance: Demonstrate knowledge and understanding of the computing and management principles and apply these to one’s work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long learning: Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.
PROGRAM’S SPECIFIC OUTCOMES (PSO’S)
PSO 1: Ability to solve complex problems in the domain of Artificial Intelligence and Data Analytics using the current software tools and technologies.
PSO 2: Explore the concepts of Artificial Intelligence and Data Analytics and the development of computational systems in multidisciplinary fields.
PSO 3: Create a new design, model, analyze, and evaluate the solution for new real-time problems in the field of Artificial Intelligence and Data Analytics.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
About the Course
M.Sc. Artificial Intelligence and Data Analytics, a two-year postgraduate degree program is designed to develop their skills, especially in the areas of Artificial Intelligence and Data Analytics. This program also provides the necessary skills in theoretical computation and mathematics to handle real-world complexities and it aims to meet sharp growth in industries. Upcoming trends in the software industry that impact a broad range of applications are demonstrated. Various data-driven AI solutions that can be implemented to reinforce and improve the decision-making setup are instructed. Students can understand the rapid change in the emerging fields of Artificial intelligence and data analytics of computational systems.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
Careers
With your qualification, you’ll be equipped to launch or enhance a career in the artificial intelligence and/or data science sectors, as well as giving them the skills they need to work in related consultancy or to start a new tech business. Career opportunities include (but are not limited to):
Applied Artificial Intelligence and Data Science professional
Graduate analyst
Business analyst
Business Intelligence Developer
(Big) Data engineer/analyst/scientist/architect
Machine learning developer
Software engineer/architect
Data mining expert
Technology consultant
Digital marketing analyst or manager
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
FAQ's
What is an artificial intelligence and data science course?
The Artificial Intelligence and Data Science Program prepares students with the skills to perform intelligent data analysis which is a key component in numerous real-world applications. During the past ten years, data science has emerged as one of the most high-growth, dynamic, and lucrative careers in technology.
Which is the best course in Data Science?
Since data science is such a varied field, it is important to do some basic research and find out which aspects of data science you are interested in. To have a strong background in data science it is recommended that you at least hold a M.Sc in Artificial Intelligence and Data Analytics as AI and machine learning are the two most rapidly growing spheres in data science
What is the eligibility to pursue a Post graduate degree in Artificial Intelligence and Data Analytics?
To pursue an undergraduate degree in Artificial Intelligence And Data Science you must have qualified 10+2 with Physics, Mathematics, and Chemistry as your primary subjects.
Is Data Science a good career?
From a return-on-investment perspective, data science is a well-paying industry. As a data scientist, you must be prepared for constant challenges and must display an initiative towards upgrading your knowledge regularly. If you have a penchant for mathematics and programming then data science could be the right career for you.
Is there a demand for Data Scientists?
Data science is a vast and diverse field that involves components of artificial intelligence, machine learning, and the Internet of Things. Due to its diversity, there is a lot of scope in the data science industry with multiple job openings in leading companies across the globe.
What are the skills needed to become a Data Scientist?
To become a successful data scientist, you need a set of technical skills. With relation to technical skills, you must have a bachelor’s or a master’s degree in data science, you must know R programming, and Python coding, and you must know how to use the Hadoop platform and Apache Spark. Besides this, it would be a bonus for you to know about machine learning and artificial intelligence to gain an edge over your competitors in the job market.
What is the average salary that a data scientist earns?
An entry-level data scientist in India can make about Rs. 5,00,000 annually. The average salary for mid-level data scientists is Rs. 11, 00,000 annually. A senior data scientist with 10+ years of experience can make up to Rs. 20, 00,000 annually.
Who gets paid more data scientist or machine learning engineer?
ML engineers have slightly higher pay than data scientists, but there are far fewer ML engineers in the field. This is because ML engineers’ official title is often just software engineers. The average for research scientists was surprisingly low.
How Data Science and AI are related?
Data Science is a comprehensive process that involves pre-processing, analysis, visualization, and prediction. On the other hand, AI is the implementation of a predictive model to forecast future events. Data Science comprises various statistical techniques whereas AI makes use of computer algorithms.
Which companies hire graduates in Data Science and Artificial Intelligence?
Many multinational companies and organizations hire graduates in data science and artificial intelligence. Over the last few years, Amazon, Microsoft, Adobe, Uber, Samsung, Intel, Accenture, Facebook, Netflix, and Lenovo have been some of the top recruiters in the data science and artificial arena. Studies show that there is going to be a spurt in the number of jobs in this industry.
Why is AI such a hot topic lately?
A lot of things have aligned to make this an exciting time for major advancements in AI. Processing power has improved at an amazing rate — there’s been a trillion-fold increase in performance over the last 60 years The cost of data processing has become more affordable More data needs to be analyzed because businesses are capturing more signals from customer interactions. Application of AI has already improved consumer apps significantly — leading to further expectations in making life easier, spurring the need for AI technical knowhow and R&D.
What are the main challenges with AI Technology?
Let’s think of AI as an iceberg. What you see as a user is just the tip — but beneath the surface lurks a behemoth support system of data scientists and engineers, massive amounts of data, labour-intensive extraction, preparation of the data, and a huge technology infrastructure. It takes a specialized team of data scientists and developers to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user experience application.
What kind of career assistance does AI offer?
AI has application in:
Automotive Banking
Finance
Government
Healthcare
Insurance
Manufacturing
Mobile App market
Pharmaceuticals
Retail
Sports
Telecommunication
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
S.NO
NAME OF THE FACULTY
DESIGNATION
EXPERIENCE
SPECIALIZATION
1
Dr.I.Lakshmi
Associate Professor
1 and 6 months in Hindustan 17 Years other
IOT,Data Science, Security,Image Compression
2
Ms. Praisy
Assistant Professor
4 Years and 10 months in others
Computer Networks, Machine Learning, Deep Learning
3
Ms.T.Vani
Assistant Professor
7 Years in others
Computer Network 1. Deep Learning 2.Machine Learning
M.Sc. Artificial Intelligence and Data Analytics, a two-year post graduate degree program is designed to develop their skills especially in the areas of Artificial Intelligence and Data Analytics. This program also provides necessary skills in theoretical computation and mathematics to handle real-world complexities and it aims to meet up a sharp growth in industries. Upcoming trends with respect to the software industry that impact a broad range of applications are demonstrated. Various data-driven AI solutions which can be implemented to reinforce and improve the decision-making set up are instructed. Students can understand the rapid change in the emerging fields of Artificial intelligence and data analytics of computational systems.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
Salient Features
Curriculum based on Research and Development and industry needs
Career prospects in major sectors such as Agriculture. Healthcare, education and Infrastructure, Banking etc,
B.Sc. (Applied Sciences) with Mathematics and Computer Science (10+2+3 scheme) from a recognized university.
Any Bachelor’s degree in science (10+2+3) from a recognized university with at least 3 papers in Computer Science and 1 paper in Mathematics under Annual System.
PEO 1: To prepare graduates to be successful professionals in industry, government, academia, research, entrepreneurial pursuit, and consulting firms.
PEO 2: To prepare graduates to achieve peer recognition, as individuals and as a team players, through demonstration of good analytical, design, implementation, and interpersonal skills.
PEO 3: To prepare graduates to contribute to society as broadly educated, expressive, ethical, and responsible citizens with proven expertise.
PEO 4: To prepare graduates to pursue life-long learning to fulfill their goals.
PROGRAM’S OUTCOMES (PO’S)
Computational knowledge: Apply knowledge of computing fundamentals, computing specialization, mathematics, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements.
Problem analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.
Design/development of solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
Modern tool usage: Modern Tool Usage: Create, select, adapt, and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice.
Innovation and Entrepreneurship: Identify a timely opportunity and use innovation to pursue that opportunity to create value and wealth for the betterment of the individual and society at large.
Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practice.
Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
Communication: Communicate effectively with the computing community, and with society, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.
Project management and finance: Demonstrate knowledge and understanding of the computing and management principles and apply these to one’s work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long learning: Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.
PROGRAM’S SPECIFIC OUTCOMES (PSO’S)
PSO 1: Ability to solve complex problems in the domain of Artificial Intelligence and Data Analytics using the current software tools and technologies.
PSO 2: Explore the concepts of Artificial Intelligence and Data Analytics and the development of computational systems in multidisciplinary fields.
PSO 3: Create a new design, model, analyze, and evaluate the solution for new real-time problems in the field of Artificial Intelligence and Data Analytics.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
About the Course
M.Sc. Artificial Intelligence and Data Analytics, a two-year postgraduate degree program is designed to develop their skills, especially in the areas of Artificial Intelligence and Data Analytics. This program also provides the necessary skills in theoretical computation and mathematics to handle real-world complexities and it aims to meet sharp growth in industries. Upcoming trends in the software industry that impact a broad range of applications are demonstrated. Various data-driven AI solutions that can be implemented to reinforce and improve the decision-making setup are instructed. Students can understand the rapid change in the emerging fields of Artificial intelligence and data analytics of computational systems.
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
Careers
With your qualification, you’ll be equipped to launch or enhance a career in the artificial intelligence and/or data science sectors, as well as giving them the skills they need to work in related consultancy or to start a new tech business. Career opportunities include (but are not limited to):
Applied Artificial Intelligence and Data Science professional
Graduate analyst
Business analyst
Business Intelligence Developer
(Big) Data engineer/analyst/scientist/architect
Machine learning developer
Software engineer/architect
Data mining expert
Technology consultant
Digital marketing analyst or manager
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
FAQ's
What is an artificial intelligence and data science course?
The Artificial Intelligence and Data Science Program prepares students with the skills to perform intelligent data analysis which is a key component in numerous real-world applications. During the past ten years, data science has emerged as one of the most high-growth, dynamic, and lucrative careers in technology.
Which is the best course in Data Science?
Since data science is such a varied field, it is important to do some basic research and find out which aspects of data science you are interested in. To have a strong background in data science it is recommended that you at least hold a M.Sc in Artificial Intelligence and Data Analytics as AI and machine learning are the two most rapidly growing spheres in data science
What is the eligibility to pursue a Post graduate degree in Artificial Intelligence and Data Analytics?
To pursue an undergraduate degree in Artificial Intelligence And Data Science you must have qualified 10+2 with Physics, Mathematics, and Chemistry as your primary subjects.
Is Data Science a good career?
From a return-on-investment perspective, data science is a well-paying industry. As a data scientist, you must be prepared for constant challenges and must display an initiative towards upgrading your knowledge regularly. If you have a penchant for mathematics and programming then data science could be the right career for you.
Is there a demand for Data Scientists?
Data science is a vast and diverse field that involves components of artificial intelligence, machine learning, and the Internet of Things. Due to its diversity, there is a lot of scope in the data science industry with multiple job openings in leading companies across the globe.
What are the skills needed to become a Data Scientist?
To become a successful data scientist, you need a set of technical skills. With relation to technical skills, you must have a bachelor’s or a master’s degree in data science, you must know R programming, and Python coding, and you must know how to use the Hadoop platform and Apache Spark. Besides this, it would be a bonus for you to know about machine learning and artificial intelligence to gain an edge over your competitors in the job market.
What is the average salary that a data scientist earns?
An entry-level data scientist in India can make about Rs. 5,00,000 annually. The average salary for mid-level data scientists is Rs. 11, 00,000 annually. A senior data scientist with 10+ years of experience can make up to Rs. 20, 00,000 annually.
Who gets paid more data scientist or machine learning engineer?
ML engineers have slightly higher pay than data scientists, but there are far fewer ML engineers in the field. This is because ML engineers’ official title is often just software engineers. The average for research scientists was surprisingly low.
How Data Science and AI are related?
Data Science is a comprehensive process that involves pre-processing, analysis, visualization, and prediction. On the other hand, AI is the implementation of a predictive model to forecast future events. Data Science comprises various statistical techniques whereas AI makes use of computer algorithms.
Which companies hire graduates in Data Science and Artificial Intelligence?
Many multinational companies and organizations hire graduates in data science and artificial intelligence. Over the last few years, Amazon, Microsoft, Adobe, Uber, Samsung, Intel, Accenture, Facebook, Netflix, and Lenovo have been some of the top recruiters in the data science and artificial arena. Studies show that there is going to be a spurt in the number of jobs in this industry.
Why is AI such a hot topic lately?
A lot of things have aligned to make this an exciting time for major advancements in AI. Processing power has improved at an amazing rate — there’s been a trillion-fold increase in performance over the last 60 years The cost of data processing has become more affordable More data needs to be analyzed because businesses are capturing more signals from customer interactions. Application of AI has already improved consumer apps significantly — leading to further expectations in making life easier, spurring the need for AI technical knowhow and R&D.
What are the main challenges with AI Technology?
Let’s think of AI as an iceberg. What you see as a user is just the tip — but beneath the surface lurks a behemoth support system of data scientists and engineers, massive amounts of data, labour-intensive extraction, preparation of the data, and a huge technology infrastructure. It takes a specialized team of data scientists and developers to access the correct data, prepare the data, build the correct models, and then integrate the predictions back into an end-user experience application.
What kind of career assistance does AI offer?
AI has application in:
Automotive Banking
Finance
Government
Healthcare
Insurance
Manufacturing
Mobile App market
Pharmaceuticals
Retail
Sports
Telecommunication
SCHOOL OF LIBERAL ARTS & APPLIED SCIENCES
S.NO
NAME OF THE FACULTY
DESIGNATION
EXPERIENCE
SPECIALIZATION
1
Dr.I.Lakshmi
Associate Professor
1 and 6 months in Hindustan 17 Years other
IOT,Data Science, Security,Image Compression
2
Ms. Praisy
Assistant Professor
4 Years and 10 months in others
Computer Networks, Machine Learning, Deep Learning
3
Ms.T.Vani
Assistant Professor
7 Years in others
Computer Network 1. Deep Learning 2.Machine Learning
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