MCA Specialization in Data Science | Career Point University, Kota

CPUEST for B.Tech/ BCA/ BBA/ MBA/ MCA

  • Eligibility: Only for students seeking admission to B.Tech, BCA, BBA,  MBA, MCA.
  • Mode of Test - Proctored Online
    1. At Center (CP Tower)
    2. At Home
  • Test Date
    1. Scheduled Dates at Center 
    2. On Demand
    3. Schedule Dates

      Phase

      Saturday

      Sunday

      Time

      Phase-1 

      12 April

      13 April


      Slot1: 10:00 am to 11:30 am

      Slot 2: 04:00 pm to 05:30 pm

      19 April

      20 April

      26 April

      27 April

      Phase-2

      03 May

      04 May




      10:00 am to 11:30 am

      04:00 pm to 05:30 pm

      10 May

      11 May

      17 May

      18 May

      24 May

      25 May

      31 May

       

      Phase-3

       

      01 June




      10:00 am to 11:30 am

      04:00 pm to 05:30 pm

      07 June

      08 June

      14 June

      15 June

      21 June

      22 June

      28 June

      29 June

  • Test Pattern

    Test For

    Syllabus

    Duration

    Marking Pattern

    Max Marks

    B.Tech

      PCM

    90 Mins

     

    180

    BCA

    Aptitude Test

    60 Mins

     

    120

    MCA

    Aptitude Test

    60 Mins

     

    120

    BBA

    Aptitude Test

    60 Mins

     

    120

    MBA

    Aptitude Test

    60 Mins

     

    120

  • Scholarship: 10% based on performance in CPU-EST

Website/News paper
Scholarship:  up to 50% based on performance in qualifying exam / Scholarship Tests

Scholarship Offering Guidelines for Team Leads and Counsellors:

  • Those who are getting scholarship on the basis of past academic performance should not be advised to appear for scholarship unless it is required for conversion with higher scholarship.

  • Those who are not getting any scholarship may be advised to appear for scholarship to offer some discount in fee and create urgency for taking admission.

MCA Specialization In Data Science

Other Specialization:

Overview

Master of Computer Application (MCA) program with a specialization in Data Science is a cutting-edge course designed to equip students with advanced skills in both computer science and data analytics. Spanning two years, the MCA with Data Science specialization curriculum covers core subjects such as Advanced Programming, Algorithms, Data Structures, Database Management Systems, and Software Engineering, along with specialized modules focusing on data science techniques, machine learning, statistical analysis, big data technologies, and data visualization. Students gain hands-on experience through practical projects, internships, and industry collaborations, applying data science concepts to real-world scenarios and honing their analytical and problem-solving abilities. Our experienced faculty, modern infrastructure, and industry partnerships ensure that students receive a comprehensive education aligned with current industry trends and demands. Graduates of our MCA program with a specialization in Data Science are well-prepared for roles such as Data Scientist, Machine Learning Engineer, Big Data Analyst, Data Engineer, and Business Intelligence Consultant in various sectors including IT, finance, healthcare, and e-commerce. We also provide career counseling, networking opportunities, and placement support to help students succeed in their chosen career paths within the rapidly evolving field of data science and analytics.

University's Accomplishments and Impact

3000+

Placements So Far

66 Lakhs

Highest Package

30

Startups

130

Patent Published

1250+

Research Publication

905

National & Int'l Journal

Trust of more than, 10,000 Students

Scope of MCA Specialization In Data Science

According to US Bureau of Labor Statistics, The employment outlook for computer and information technology occupations is exceptionally promising, with a projected growth rate significantly higher than the average across all occupations from 2022 to 2032. On average, approximately 377,500 job openings are anticipated each year within these fields. This surge is attributed to both the expansion of employment opportunities and the imperative to fill positions vacated by workers departing these occupations permanently. The robust projection underscores the ongoing demand for skilled professionals in the computer and information technology sectors over the next decade.

Course Structure MCA Specialization In Data Science

Our B.Tech program in Artificial Intelligence and Machine Learning (AI & ML) combines theoretical and practical knowledge, offering a curriculum that covers programming, data structures, machine learning, deep learning, natural language processing, and big data analytics. The program provides a strong foundation in mathematics and statistics, along with specialized AI & ML topics.

The following table outlines the comprehensive course structure of the B.Tech AI & ML program. It is organized into core, elective, interdisciplinary, and skill enhancement components. Each category specifies the type of courses, their descriptions, and the corresponding credits, ensuring a well-rounded academic experience with a balance of foundational knowledge, research exposure, and professional ethics:

Category Short Name Description Credits
Departmental Core DC Discipline Specific Core Courses (DSC) 35
Departmental Core DC Project / Dissertation / Field Study / Survey 6
Departmental Core DC Seminar 2
Departmental Core DC Internship / on Job Experience 8
Departmental Core DC Research Credit Course 0
Departmental Elective DE Discipline Specific Elective Courses(DSE) 36
Program Linked Core PLC Interdeciplinary 2
Generic Elective GE Open courses/Generic Elective (GE) 0
University Core UC Ability Enhancement Courses (AEC) 2
University Core UC Skill Enhancement Courses (SEC) 0
Value Added Courses common for all UG (VAC) – Non Graded
University Core UC NSS/NCC 2
University Core UC Envionmental Science 2
University Core UC Human Values and Professional Ethics 2
This structure ensures students gain technical expertise, interdisciplinary perspectives, and ethical and environmental awareness, preparing them for dynamic careers in AI and ML.
S.No. Course Code Course L T P Total Credit Category Semester
1 CAL811 Software Engineering 3 0 0 3 DC 1st Sem
2 CAL817 Operating System 3 0 0 3 DC 1st Sem
3 CAL813 Advance Database Management System 3 0 2 4 DC 1st Sem
4 CAL812 Internet and Java Programming 3 0 0 3 DC 1st Sem
5 CAL815 Web Technology 3 0 2 4 DC 1st Sem
6 CAP801 Java Lab 0 0 2 1 DC 1st Sem
7 CAL814 Departmental Elective-1 3 0 2 4 DE 1st Sem
8 CAL818 Departmental Elective-2 3 0 0 3 DE 1st Sem
9 EGL805 Business Writing and Professional Communication 2 0 0 2 PLC 1st Sem
S.No. Course Code Course L T P Total Credit Category Semester
1 CAL851 Programming with Python 3 0 2 4 DC 2nd Sem
2 CAL857 Data Communication & Computer Networks 3 0 0 3 DC 2nd Sem
3 CAL853 Ethical Hacking 3 0 0 3 DC 2nd Sem
4 CAL855 Unix and Shell Programming 3 0 2 4 DC 2nd Sem
5 Departmental Elective-3 3 0 0 3 DE 2nd Sem
6 Departmental Elective-4 3 0 2 4 DE 2nd Sem
7 Departmental Elective-5 3 0 0 3 DE 2nd Sem
8 Departmental Elective-6 2 0 2 3 DE 2nd Sem
9 CAD851 Minor project 0 0 4 2 DC 2nd Sem
S.No. Course Code Course L T P Total Credit Category Semester
1 CAL872 Cryptography and Network Security 3 0 0 3 DC 3rd Sem
2 CAL889 Advanced Java programming 3 0 2 4 DC 3rd Sem
3 Departmental Elective-7 2 0 2 3 DE 3rd Sem
4 Departmental Elective-8 2 0 2 3 DE 3rd Sem
5 Departmental Elective-9 3 0 0 3 DE 3rd Sem
6 Departmental Elective-10 3 0 0 3 DE 3rd Sem
7 SML886 Professional Ethics 2 0 0 2 PLC 3rd Sem
8 CAD840 Seminar – Research Paper 0 2 0 2 DC 3rd Sem
S.No. Course Code Course L T P Total Credit Category Semester
1 CAD895 Internship and Project Work 0 0 12 6 DC 4th Sem
2 CAD897 Dissertation 0 0 8 4 DC 4th Sem
Course Code Course L T P Credit
CAL814 Open Source Technologies 3 0 2 4
CAL818 Cloud Computing 3 0 0 3
CAL854 Artificial Intelligence 3 0 0 3
CAL852 ASP .Net with C# 3 0 2 4
CAL856 Analysis and Design of Algorithm 3 0 0 3
CAL859 R Programming for Data Analysis 2 0 2 3
CAL873 Application Development with Android 2 0 2 3
CAL875 Digital Marketing Analytics and SEO 2 0 2 3
CAL874 Cluster and Grid Computing 3 0 0 3
CAP878 Cyber Crime and Digital Forensics 3 0 0 3

Department
at a Glance

Exclusive
Labs

Your Department in a Nutshell

Admission Criteria

Fee Details

Course Fee
Admission Fee (one time)
5000/- (at the time of admission)
Tuition Fee
37000/- Per Semester
Examination Fee
3000/- Per Semester
Development Fee
3000/- Per Semester
Caution Money (one time)
3000/- (Refundable)

Scholarship Criteria

Based on performance in qualifying exam

% in Qualifying Class [1] Scholarship on Tuition Fees
Above 90% 20%
85% – 89.99% 10%
75% – 84.99% 5%
  • Early Admission Benefits Upto Rs. 3000/- Admission Taken Before 15th July 2024
  • Scholarship in Tuition fee (for all 4 semesters): up to 20% based on academic performance.

Why Join MCA Specialization In Data Science?

Joining an MCA specialization in Data Science offers numerous compelling reasons:

What makes Department
of MCA Specialization In Data Science unique?

The Department of MCA Specialization in Data Science stands out due to several unique characteristics:

Placements

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Frequently Asked Questions

MCA Specialization In Data Science at Career Point University

What is the duration of the MCA Specialization in Data Science program at Career Point University?

The MCA Specialization in Data Science program at Career Point University typically spans over two years, divided into four semesters.

What are the eligibility criteria for admission to the MCA Specialization in Data Science program?

The eligibility criteria for admission to the MCA Specialization in Data Science program at Career Point University generally include completing a bachelor’s degree in computer applications, computer science, or a related field from a recognized university with a minimum specified percentage as per university guidelines.

What subjects are covered in the MCA Specialization in Data Science curriculum?

The curriculum of the MCA Specialization in Data Science program at Career Point University covers a broad range of subjects including statistics, machine learning, big data technologies, data visualization, database management, and programming languages such as Python and R.

Are there any specialization options available within the MCA Specialization in Data Science program?

Career Point University may offer elective courses or specialization tracks within the MCA Specialization in Data Science program, allowing students to focus on specific areas such as machine learning, data engineering, business analytics, or natural language processing.

Does the MCA Specialization in Data Science program include practical training and hands-on experience?

Yes, the MCA Specialization in Data Science program at Career Point University emphasizes practical training, hands-on experience, and project work. Students have the opportunity to work on real-world data science projects, analyze datasets, develop machine learning models, and gain practical exposure to data science tools and technologies.

What are the career prospects for graduates of the MCA Specialization in Data Science program?

Graduates of the MCA Specialization in Data Science program at Career Point University can pursue various career opportunities in the field of data science, including roles such as data scientist, machine learning engineer, data analyst, business intelligence analyst, data engineer, and big data architect.

Is there any support available for internships and job placements?

Career Point University provides support for internships, job placements, and career counseling services to help students explore internship opportunities, develop job search skills, prepare for interviews, and secure employment in reputable organizations within the data science industry.

Are there opportunities for research and innovation within the MCA Specialization in Data Science program?

Yes, Career Point University encourages research and innovation within the MCA Specialization in Data Science program. Students have opportunities to engage in research projects, collaborate with faculty members on cutting-edge research initiatives, and contribute to advancements in the field of data science.

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