Shri Vile Parle Kelavani Mandal’s
MITHIBAI COLLEGE OF ARTS, CHAUHAN INSTITUTE OF SCIENCE &
AMRUTBEN JIVANLAL COLLEGE OF COMMERCE AND ECONOMICS
(AUTONOMOUS)
NAAC Reaccredited ‘A’ grade, CGPA:3.57
Affiliated to the
UNIVERSITY OF MUMBAI
Program: Master of Science
M.Sc Data Science & Artificial Intelligence
Semester I & II
Choice Based Credit System (CBCS) with effect
from the Academic year 2023-24
Academic Council No:
Agenda No:
Page 1 of 3
, PROGRAMME SPECIFIC OUTCOMES (PSO’S)
On completion of the M.Sc. Data Science & Artificial Intelligence, the learners should
be enriched with knowledge and be able to-
PSO1: Solve specific domain-specific issues by possessing in-depth domain
knowledge
PSO2: Use theoretical understanding with related technologies for problem-solving,
Result analysis, comparison and inferences.
PSO3: Build state-of-the-art data science and AI systems by combining coding, design,
and creative thinking.
PSO4: Able to conduct independent research or investigation to address practical
issues.
Preamble
This syllabus is an honest attempt to include following ideas, among other things,
into practice:
• This course is an attempt to include the following ideas and put them into
practice
• Bring uniqueness to the syllabus in comparison to similar programs offered
by other institutions
• Focus is on Data Science and Artificial Intelligence in line with the current
employability trend
• Subjects like Mathematics and Statistics are included as per the need of
curriculum
• Identify and nurture research aptitude among students
• Usage of Open Source Software
This syllabus for the semester I and semester II has tried to initiate steps to meet
these goals. By extending the syllabus to semester III and semester IV, it is
assumed that these goals will be met to a larger extent.
Page 2 of 3
, Pedagogy
Mithibai College (Autonomous) uses a variety of conventional as well as innovative teaching
methods to cater to diverse student needs and learning styles. These methods promote
engagement, critical thinking, and problem-solving.
Conventional Methods:
• Classroom Lectures: Present foundational knowledge and concepts in an
organized and structured manner.
• Experiential Learning: Applies theoretical knowledge in real-world settings
through hands-on experiences and projects.
• Team Based Learning: Develops collaboration, personalized learning, and
leadership skills through group work.
• Flipped Classroom: Promotes active learning through outside-of-class reviews
and in-class discussions, allowing for deeper analysis and synthesis of ideas.
• Group Discussion: Encourages sharing of ideas and perspectives, developing
communication and critical thinking skills.
• Project Method: Develops research, presentation, and time-management skills
through research-based projects.
• Debate: Structured argumentation develops communication and reasoning skills while
promoting research and critical analysis.
• Case Method: Uses real-life situations to teach problem-solving and
decision-making skills, allowing students to apply theoretical knowledge to practical
situations.
Innovative Methods:
• Technology Integration: Enhances engagement and promotes active learning
through immersive and interactive digital resources like Padlet, Quizlet, Kahoot,
Mindmeister etc.
• Peer Teaching: Fosters collaboration and personalized learning through student-led
instruction, allowing students to develop communication and leadership skills.
• Blended Learning: Provides flexibility and promotes self-directed learning and
digital literacy through a combination of traditional classroom lectures and online
resources.
• Community - Based Learning: Develops empathy and social responsibility
through hands-on experiences in community service, promoting a sense of social
justice and civic engagementThus our college's diverse and innovative
pedagogical methods provide students with a dynamic and engaging learning
experience that prepares them for excellence in their academic and professional
careers.
Page 3 of 3
, The courses are as follows: -
Semester – I
Course Title Credits Lecture/Week Practical/Week
Machine Learning 4(T) + 2(P) 4 4
Statistical Testing, Analysis and 4(T) + 2(P) 4 4
Inferencing
Mathematics-I 2(T) 2 -
Research Methodology 4(T) 4 -
Elective – Cloud Computing 3(T) + 1(P) 2 2
Elective – Blockchain Technologies 3(T) + 1(P) 3 2
Semester – II
Course Title Credits Lecture/Week Practical/Week
Neural Networks and Fuzzy Logic Systems 4(T) + 2(P) 4 4
Multivariate Analysis 4(T) + 2(P) 4 4
Mathematics-II 2(T) 2 -
Elective – Advance Database Management 3(T) + 1(P) 2 2
System
Elective – Analysis of Algorithms 3(T) + 1(P) 3 2
Project Implementation 4(P) - -
N.B.- (i) The duration of each theory lecture will be of 60 minutes. For theory component
value of One Credit is equal to 15 learning hours.
(ii) For practical component the value of One Credit is equal to 30 learning hours
(iii) Thus in a week, a student will study 19 hours of theory and 12 hours of practical
for semester
(iv) Any elective course will only begin if at least 65% of the entire class of students
register for it
Page 4 of 3
MITHIBAI COLLEGE OF ARTS, CHAUHAN INSTITUTE OF SCIENCE &
AMRUTBEN JIVANLAL COLLEGE OF COMMERCE AND ECONOMICS
(AUTONOMOUS)
NAAC Reaccredited ‘A’ grade, CGPA:3.57
Affiliated to the
UNIVERSITY OF MUMBAI
Program: Master of Science
M.Sc Data Science & Artificial Intelligence
Semester I & II
Choice Based Credit System (CBCS) with effect
from the Academic year 2023-24
Academic Council No:
Agenda No:
Page 1 of 3
, PROGRAMME SPECIFIC OUTCOMES (PSO’S)
On completion of the M.Sc. Data Science & Artificial Intelligence, the learners should
be enriched with knowledge and be able to-
PSO1: Solve specific domain-specific issues by possessing in-depth domain
knowledge
PSO2: Use theoretical understanding with related technologies for problem-solving,
Result analysis, comparison and inferences.
PSO3: Build state-of-the-art data science and AI systems by combining coding, design,
and creative thinking.
PSO4: Able to conduct independent research or investigation to address practical
issues.
Preamble
This syllabus is an honest attempt to include following ideas, among other things,
into practice:
• This course is an attempt to include the following ideas and put them into
practice
• Bring uniqueness to the syllabus in comparison to similar programs offered
by other institutions
• Focus is on Data Science and Artificial Intelligence in line with the current
employability trend
• Subjects like Mathematics and Statistics are included as per the need of
curriculum
• Identify and nurture research aptitude among students
• Usage of Open Source Software
This syllabus for the semester I and semester II has tried to initiate steps to meet
these goals. By extending the syllabus to semester III and semester IV, it is
assumed that these goals will be met to a larger extent.
Page 2 of 3
, Pedagogy
Mithibai College (Autonomous) uses a variety of conventional as well as innovative teaching
methods to cater to diverse student needs and learning styles. These methods promote
engagement, critical thinking, and problem-solving.
Conventional Methods:
• Classroom Lectures: Present foundational knowledge and concepts in an
organized and structured manner.
• Experiential Learning: Applies theoretical knowledge in real-world settings
through hands-on experiences and projects.
• Team Based Learning: Develops collaboration, personalized learning, and
leadership skills through group work.
• Flipped Classroom: Promotes active learning through outside-of-class reviews
and in-class discussions, allowing for deeper analysis and synthesis of ideas.
• Group Discussion: Encourages sharing of ideas and perspectives, developing
communication and critical thinking skills.
• Project Method: Develops research, presentation, and time-management skills
through research-based projects.
• Debate: Structured argumentation develops communication and reasoning skills while
promoting research and critical analysis.
• Case Method: Uses real-life situations to teach problem-solving and
decision-making skills, allowing students to apply theoretical knowledge to practical
situations.
Innovative Methods:
• Technology Integration: Enhances engagement and promotes active learning
through immersive and interactive digital resources like Padlet, Quizlet, Kahoot,
Mindmeister etc.
• Peer Teaching: Fosters collaboration and personalized learning through student-led
instruction, allowing students to develop communication and leadership skills.
• Blended Learning: Provides flexibility and promotes self-directed learning and
digital literacy through a combination of traditional classroom lectures and online
resources.
• Community - Based Learning: Develops empathy and social responsibility
through hands-on experiences in community service, promoting a sense of social
justice and civic engagementThus our college's diverse and innovative
pedagogical methods provide students with a dynamic and engaging learning
experience that prepares them for excellence in their academic and professional
careers.
Page 3 of 3
, The courses are as follows: -
Semester – I
Course Title Credits Lecture/Week Practical/Week
Machine Learning 4(T) + 2(P) 4 4
Statistical Testing, Analysis and 4(T) + 2(P) 4 4
Inferencing
Mathematics-I 2(T) 2 -
Research Methodology 4(T) 4 -
Elective – Cloud Computing 3(T) + 1(P) 2 2
Elective – Blockchain Technologies 3(T) + 1(P) 3 2
Semester – II
Course Title Credits Lecture/Week Practical/Week
Neural Networks and Fuzzy Logic Systems 4(T) + 2(P) 4 4
Multivariate Analysis 4(T) + 2(P) 4 4
Mathematics-II 2(T) 2 -
Elective – Advance Database Management 3(T) + 1(P) 2 2
System
Elective – Analysis of Algorithms 3(T) + 1(P) 3 2
Project Implementation 4(P) - -
N.B.- (i) The duration of each theory lecture will be of 60 minutes. For theory component
value of One Credit is equal to 15 learning hours.
(ii) For practical component the value of One Credit is equal to 30 learning hours
(iii) Thus in a week, a student will study 19 hours of theory and 12 hours of practical
for semester
(iv) Any elective course will only begin if at least 65% of the entire class of students
register for it
Page 4 of 3
