Programme Details

Master of Science (Computer Science with Data Science)

  • Pune Lavasa Campus
  • Open From : 08-Dec-2024
  • Open Until : 16-Mar-2025

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

| Overview

The MSc in Computer Science with Data Science at CHRIST University Pune Lavasa is a two-year, full-time postgraduate program that integrates advanced computer science concepts with the latest trends in data science. Designed for students who wish to become experts in the evolving fields of artificial intelligence, machine learning, and big data, this program provides a solid foundation in core computer science while deepening your knowledge of data-driven technologies.

With a strong emphasis on research, practical learning, and industry collaboration, students are exposed to cutting-edge tools and technologies like Python, R, cloud computing, and deep learning. The program focuses on solving real-world problems through hands-on projects, internships, and live industry partnerships, ensuring that students gain the skills needed to succeed in top tech roles. Specialised electives in areas like Computer Vision, Big Data Analytics, and Natural Language Processing provide students with the expertise needed to tackle the challenges of tomorrow’s technology landscape.

 

| Why choose this programme

  • Cutting-Edge Curriculum combining Computer Science and Data Science to meet industry demands.
  • Research-Oriented approach, offering opportunities for independent research and academic publications.
  • Hands-on Learning through internships, industry projects, and specialisation electives.
  • Exposure to Emerging Technologies like AI, Cloud Computing, Blockchain, and IoT.
  • Strong Industry Collaboration with tech companies for real-world exposure and career advancement.
  • Specialization Options in Computer Vision, Big Data Analytics, and Natural Language Processing.
  • Comprehensive training in Data Science tools including Python, R, Data Mining, and Deep Learning.

 

| What will you learn?

  • Core Computer Science concepts like Data Structures, Algorithms, and Software Engineering.
  • Advanced techniques in Data Science, including Machine Learning, Data Mining, and Big Data Analytics.
  • Practical skills in AI, Deep Learning, Natural Language Processing, and Computer Vision.
  • Data Visualisation and Business Intelligence tools for analyzing and presenting complex data.
  • Cloud Administration, Data Security, and Web Development practices for modern tech solutions.
  • Research Methodologies for formulating and implementing data-driven projects.
  • Specialised knowledge in Spatio-Temporal Analytics, Social Media Analytics, and Quantitative Data Analysis.
  • Industry-Specific Applications like Financial Analytics, IoT, and Mobile Application Development.
  • The ability to manage and lead data-driven projects and collaborate effectively in team-based environments.

 

| Modules

 

Year 1

  • Computer Science Foundations
    Programming, Data Structures, and Algorithms
    Database Management and Data Engineering

  • Mathematics and Statistical Methods for Data Science

  • Data Science Fundamentals
    Machine Learning: Principles and Techniques

  • Software Engineering and Development Practices

  • Research Methodologies in Computational Sciences

  • Professional Skills and Communication
     

Year 2

  • Advanced Data Science and Machine Learning

  • Big Data and Cloud Computing

  • Specialized Data Science Applications

  • Industry Integration and Practical Learning

  • Research and Capstone Project

  • Professional Development

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

Candidates falling under any of the below mentioned categories must apply under the International Student Category:

  • Foreign citizens,
  • PIO card holders and
  • OCI (Dual Citizens)

International students coming from Non-English speaking countries should:

  • Produce evidence of passing the qualifying examination in English medium or
  • Have IELTS 6.0 with no sub-score below 5.5 or TOEFL (paper) 550, TOEFL (computer) of 213 or TOEFL (IBT) of 79 scores

Candidates without the above pre-qualifications will have to enroll either for

  • Intensive Certificate course in English Language (Full Time) conducted from March to May each year or
  • One Semester Certificate course in English Language (Part Time) conducted after regular class hours from June to December.

Note: The International Student category fee structure is binding for the full duration of the programme and cannot be transferred /changed in between.

Candidates from the above listed categories having pursued Indian Educational qualification and who may have applied under the Indian States Category will have to pay the International Student Category Fee. The decision of the Admission committee is final.

Candidates seeking admission through International Student category (Foreign Nationals/PIO/OCI) will have a separate application process, with the following options to apply for any programme at Christ University.
 

  • Direct mode Application
  • Online Application form

Email ID for any clarifications: isc.admission@christuniversity.in

For other details: https://christuniversity.in/international-student-category

 

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

Students who fall under any of the following classifications, at the time of application may apply under NRI Student category and be liable to pay the fees applicable to the category for the entire duration of the course.

  • NRI defined under the Indian Income Tax Law
  • Either of the parents is outside India (except Nepal) on Work Permit / Resident Permit.
  • Indian citizen financed by any Institution /  agency outside India, even if parents are Residents of India.
  • Indian Citizen who has pursued studies for qualifying examination in any foreign / Indian syllabus outside India.
  • Indian citizen pursued studies for qualifying examination in foreign syllabi in India.

Note: If only condition 5 is satisfied, and not conditions 1 to 4 above,NRI student category fee will be applicable only for the first year.

Email ID for any clarifications: isc.admission@christuniversity.in

For other details: https://christuniversity.in/international-student-category

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

For any queries at any given time during the application and admission process, you may contact us through the following Email ID’s:

Pune Lavasa Campus

CHRIST (Deemed to be University),

Christ University Road, 30 Valor Court,

PO Dasve Lavasa, Mulshi, Pune - 412112, Maharashtra

 1800-123-2009
  admission.lavasa@christuniversity.in

 

 

Between: Monday to Friday: 09:00 AM to 05:00 PM, Saturday: 09:00 AM to 04:00 PM
(Office remains closed on Sundays, government holidays and any special events)

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

Candidates must have completed an undergraduate degree, as mentioned below from any recognized university in India or abroad (recognized by UGC/AIU/AICTE).

  1. Undergraduate Degree in Science (BSc or BS) in any one of the following subjects as major or minor:

    1. Computer Science

    2. Data Science

    3. Data Analytics

    4. Information Technology
      (OR)

  2. Undergraduate candidates from other domains of Science having UG Degree must have studied at least one computer science course.
    (OR)

  3. Bachelor of Computer Applications
    (OR)

  4. BE/BTech in any domain

Candidates appearing for their final degree examinations in March-May 2025 are eligible to apply. Such applicants must have secured 50% or above aggregate in all semesters/years of undergraduate examinations conducted so far.

  1. Foster Advanced Problem-Solving and Analytical Thinking
    Equip students with the ability to analyse complex problems in computing and data science, apply relevant algorithms, and develop efficient solutions using advanced computational techniques.

  2. Enhance Expertise in Data Science and Machine Learning
    Provide students with deep knowledge and hands-on experience in data analytics, machine learning, and artificial intelligence, enabling them to tackle real-world challenges with data-driven insights.

  3. Develop High-Level Research and Innovation Skills
    Promote critical thinking and research capabilities, allowing students to contribute to cutting-edge projects and innovations in the fields of data science, cloud computing, and big data analytics.

  4. Strengthen Communication and Leadership Abilities
    Cultivate the skills necessary to present technical concepts, research results, and data-driven strategies effectively to both technical and non-technical audiences, while leading cross-functional teams.

  5. Equip Students with Industry-Relevant Digital Tools
    Ensure that students are proficient in the latest software, programming languages, and technologies such as Python, R, TensorFlow, Hadoop, and cloud computing platforms, preparing them for careers in high-demand industries.

  6. Promote Ethical Leadership in Technology and Data Science
    Encourage responsible decision-making by instilling a strong understanding of the ethical implications of AI, machine learning, and data management, guiding students to act responsibly in their professional careers.


 

  • PO1. Understand and apply fundamental principles, concepts and methods in critical areas of science and multidisciplinary fields.

  • PO2. Demonstrate problem-solving, analytical and logical skills to provide solutions for scientific requirements.

  • PO3. Develop critical thinking with a scientific temper.

  • PO4. Communicate the subject effectively.

  • PO5. Understand the importance and judicious use of technology for the sustainable growth of humanity in synergy with nature.

  • PO6. Understand professional, ethical, and social responsibilities.

  • PO7. Enhance research culture and uphold scientific integrity and objectivity.

  • PO8. Engage in continuous reflective learning in the context of technological and scientific advancements.

PSO1: Advanced Analytical Skills :Apply advanced data science techniques, including machine learning, data mining, and statistical analysis, to solve complex problems across various domains.

PSO2: Interdisciplinary Problem Solving :Integrate computer science and data science methodologies to design and implement solutions in interdisciplinary research and industry applications.

PSO3: Proficiency in Modern Tools: Gain expertise in using data science tools and programming languages like Python, R, and big data platforms for effective data analysis and visualisation.

PSO4: Ethical and Responsible Computing :Understand and commit to ethical practices, data privacy, and responsible technology use in data science applications.

PSO5: Research and Innovation : Engage in research-based projects using scientific methods, critical thinking, and data-driven insights to contribute to advancements in the field.

PSO6: Leadership and Collaboration : Develop the ability to lead and collaborate in diverse, interdisciplinary teams, managing projects and driving data-centric strategies effectively.



 

The MSc programme is a two-year, full-time course, divided into four semesters. The first year focuses on building a strong foundation in core subjects of Computer Science, while the second year delves deeper into specialised areas of Data Science and Machine Learning. Core subjects are covered in the first two semesters, while specialised topics and elective subjects start from the third semester.

The curriculum is designed to provide a comprehensive understanding of advanced data analytics, big data technologies, machine learning, artificial intelligence, and cloud computing. Students are encouraged to integrate theoretical knowledge with real-world applications through industry projects, research papers, and a capstone project.

Key Activities:

  • Live Industry Projects: Collaboration with companies to solve real-world business problems.

  • Summer Internship: Industry exposure and hands-on learning experience.

  • Research & Development: Students work on research projects that contribute to cutting-edge developments in Data Science.

  • Workshops and Seminars: Frequent interactions with professionals to discuss the latest technological trends in AI, ML, and Data Science.

  • Guest Lectures: Industry leaders and subject-matter experts engage with students to share knowledge and career advice.

  • Industry Tie-ups: Strong collaborations with tech companies for internships, workshops, and job placements.

The MSc course plan is designed to bridge the gap between academia and industry by offering practical exposure alongside theoretical understanding, preparing graduates to meet the dynamic challenges of the data science and computer science fields.


 

The teachers of Computer Science Department give the students special counselling and remedial sessions for the benefit of their overall development. It focuses on personal as well as academic guidance for growth and competence. Every mentor is allotted with 2-3 students. The teachers extend their support for student’s needs both in academic as well as personal space. During these sessions students clear their doubts, share their problems and develop a bond with the mentor which in turn help and benefit the individuals. This is considered as an essential practise of the programme as it goes beyond the classroom dynamics helping them to grow and develop into mature human beings.

CHRIST

(Deemed to be University)

Christ University Road, 30 Valor Court
At Post: Dasve Lavasa,Taluka: Mulshi
Pune 412112, Maharashtra.

Tel: 1800-123-2009

Email: mail.lavasa@christuniversity.in

Web: http://www.lavasa.christuniversity.in

Vision

EXCELLENCE AND SERVICE

Mission

CHRIST (Deemed to be University) is a nurturing ground for an individual's holistic development to make effective contribution to the society in a dynamic environment.

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