
KL Online MCA in Data Science

Course Overview
KL University’s Online MCA in Data Science is crafted for professionals and graduates who want to specialize in data-driven technologies. With an emphasis on Python, statistics, machine learning, big data, and analytics tools, this program provides you with the technical edge to thrive in data-first careers. The curriculum includes real-world datasets, business case projects, and job-aligned mentoring—delivered 100% online with flexible schedules.
Course Highlights
UGC-Entitled & NAAC-Accredited: Widely recognized across public/private jobs and higher education.
Data-Driven Curriculum: Learn Python, R, SQL, ML, Big Data, Tableau, Power BI, and more.
Project-Based Learning: Hands-on labs and case simulations using real datasets from industry.
Career-Aligned Training: Built for roles in analytics, business intelligence, ML, and data engineering.
Career Mentorship via Course Connect: Resume feedback, project portfolio review, interview prep, and curated job leads.
100% Online Format: Learn anytime, anywhere—perfect for working professionals and recent graduates.
Peer Networking: Join a growing community of data learners, mentors, and hiring partners.
Affordable with EMI Options: Accessible pricing without compromising on academic rigour.
Course
Benefits
CourseConnect Benefits
What additional you get from CourseConnect
Mock Interview➔
Resume Building➔
Alumni Network➔
Career Guidance➔
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Eligibility
- »
Graduates in any discipline are eligible for this course.

Course
Fees


Total 4 semester
INR 16,375 / Semester
*Fees may vary kindly check the actual fee with our counsellor
Full course fee
INR 65,500
*Inclusive of all taxes
EMI starting at
INR 2,729 / Month
*Terms & Conditions apply
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Course Syllabus
| S.No | Credit | Subject |
|---|---|---|
| 1 | Professional Communication Skills | |
| 2 | Computer Networks and Communications | |
| 3 | Data Structures and Algorithms | |
| 4 | Operating Systems Concepts | |
| 5 | Database Systems |
| S.No | Credit | Subject |
|---|---|---|
| 1 | Machine Learning | |
| 2 | Exploratory Data Analysis | |
| 3 | Applied Machine Learning | |
| 4 | Statistics for Data Science | |
| 5 | Graph and Web Analytics |






















