Computer Science and Engineering (Data Science) is an interdisciplinary domain that integrates computer science, machine learning techniques, algorithms, mathematics, and domain-specific knowledge to extract valuable insights and information from data. In short, technology, algorithm development and data inference are blended together to solve complex problems analytically in Data Science.
Over the course of the program, students learn how to combine tools, statistics, and business expertise to produce value for their businesses in novel ways. The basic sciences, mathematical foundations, statistical foundations, artificial intelligence, machine learning, deep learning, and data science with its different aspects such as data collection, data visualization, processing and modeling of unstructured and large data sets are all covered in depth within the undergraduate programs of four years.
A liberal education component for all-around development, courses that spark new age skills, project-based learning, special topics (hands-on sessions on multiple topics with expert mentoring), the option for MOOC, and UG Research Project/Product Development/Internships are some of the components of the curriculum that impart 21st-century skills. The curriculum is centered on professional courses, foundation courses, liberal arts courses, and electives that aid in the development of specialized knowledge. From the first year the curriculum is also designed with a focus on design-oriented thinking, communication, collaboration, and creativity.
The Data Science graduates would be sure of the wide opportunity as, by 2020 the need for data
scientists will increase by 28%. (Refer to IBM).And also there is rough estimates of creation of
11.5 million new jobs by 2026, according to estimates from the US Bureau of Labor Statistics.
A degree in Data Science will cover the following topics:
- Basic engineering mechanics & physics.
- Data structures and algorithm design.
- Natural Language Processing & Neural Networks.
- Machine Learning & Virtualization.
- Big Data Analytics & Deep Learning.
- Builds a solid foundation in Data Science & Analytics by covering standard tools and techniques through hands-on and experimental learning.
- Through Integrated Liberal education program to gain insights into subjects like Psychology, Design Thinking, Critical Thinking & Creative Writing.
- Blended & Hybrid Learning through real-time industry projects, preparing students for evolving job roles in the chosen area of specialization.
- Offers flexibility in choosing elective courses for widening the understanding of emerging technologies and targeting towards equipping students for future skill sets and encouraging Entrepreneurship.
Vision
To be the most preferred institution for engineering & management education, research and entrepreneurship by creating professionally superior and ethically strong global manpower.
Mission:
To prepare students for professional accomplishments and responsible global citizenship while fostering continuous learning and to provide state-of-the-art education through the committed and highly skilled faculty by partnering and collaborating with industry and R&D institutes.
At the beginning of the carrier stockholder’s first responsibilities include testing new concepts, debugging, and refactoring already-existing models. They can advance themselves to the position of Senior Data Scientist or Machine Learning and AI Engineer, where they are expected to develop well-architected products, after accumulating one to three years of work experience. Senior data scientists with relevant experience and knowledge can advance to become principal data scientists, involving themselves in high-profile corporate projects.
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Engineering Graduates will be able to:
Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization for the solution of complex engineering problems.
Problem analysis: Identify, formulate, research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for public health and safety and 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: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling to complex engineering activities, with an understanding of the limitations.
The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal, and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering 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 on complex engineering activities with the engineering community and with the society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
Faculty
Dr. Sunitha B S
HOD of Data Science Department, Associate Professor
Mrs. Sheethal lobo
Assistant Professor
Facilities / Labs
Lab Conducted:- Data Structure/Python Programming
Software’s Available:- OS, Ubuntu 16.04, Python IDLE3, netbeans
Lab Carpet Area:- 75 sqm
Faculty In-Charge:- Mrs. Sheethal Lobo.
Number of systems:- 30
Contact Us
Dr. Sunitha B. S
Professor and Head,
Computer Science and Engineering (Data Science)
PES Institute of Technology and Management
NH 206, Sagar Road, Shivamogga – 577 204
Office- 8147053073
Email- hodds@pestrust.edu.in
Dr. Sunitha B. S
Professor and Head, Computer Science and Engineering (Data Science)
PES Institute of Technology and Management
NH 206, Sagar Road, Shivamogga – 577 204
Office- 8147053073
Email- hodds@pestrust.edu.in