B.C.A - Data Analytics

Overview 

The program aims to impart to eligible candidates a combined study of business with data analytics and quantitative systems. The curriculum is further supplemented by a progression of analytical modules, giving a solid range of abilities in data analysis and statistics.

In present-day business, key leaders consistently utilize the deductions of information investigation to advise strategic decisions. The program has been designed to equip aspiring managers and entrepreneurs with a crucial understanding of statistical principles, in order to grasp the implications and limitations of the results presented to them.

Such candidates would ideally possess vital analytical skills to operate in the discipline of Data Analytics, and strong theoretical foundations and practical skills of data science sector.

The subject covers specialized, technical and business areas. The curriculum is oriented towards:

Adding to the students’ knowledge of powerful techniques used in finance, marketing, and operations.
Building students’ relational abilities as business investigators invest a lot of energy communicating with clients, customers, management and engineers.
Developing analytical skills in students, since the job involves data analysis, documents, user input surveys, and workflow.
Equipping students with the requisite tools and techniques that drive solutions to real world business problems.
Enabling students to analyze and provide solutions on data visualization techniques effectively.
Preparing students for offering cutting-edge solutions to organization’s cyclical issues with data driven management decisions.
Providing advanced insight to student about leading analytic practices, design, and iterative learning and development cycles, which are the most significant part of business core deliverables.
Preparing students to build data-fueled products that assist customers and improve business outcomes.

Students pursuing a BCA with specialization in Data Analytics gain a competitive advantage by acquiring extensive expertise in the multifaceted field of data. This specialization provides comprehensive knowledge and hands-on experience in data warehousing, modeling, mining, analytics, and visualization. It equips students to work with various types of data, and cultivates their ability to design products, fostering skills relevant to software and game development. Enroll in this program and meet the rising demand for professionals with specialized data analytics skills across various industries.

Admission Process

Most institutes offering the course admit students based on performance in a relevant entrance test, often followed by a round of personal interview, wherein their general aptitude for the course is tested. Admission process generally varies across colleges.

A few institutes also provide direct admission based on the candidate’s performance at the 10+2 level of education.

Listed below are some of the top entrance exams conducted in the country for admission to the course:

IPU CET BCA
KIITEE BCA
LUCSAT BCA
PESSAT.

Eligibility

B.C.A. in Data Analytics
Listed below are the minimum criteria which candidates interested in pursuing the course are required to fulfil, in order to be eligible to apply for the course:

Successful completion of 10+2 level of education in any stream, preferably Science, completed from a recognized university.
A minimum aggregate score of 50% (45% for SC/ST candidates) at the level of graduation.

Career Opportunity

Data Scientist

Health Care Analyst

Statistician

Business Analyst

Data Analyst

Data Scientist

Market Research Analyst

Technical Team Leader

Semester I

English for Professionals
Mathematics for Data Scientists - I
Communication skills
Computer Architecture & Organization
Programming in C
Operating System
Programming in C Lab
Operating System Lab


Semester II

Mathematics for Data Scientists -- II
Object Oriented Programming using Java
Data Structures and Algorithms
Probability & Statistics - I
Excel for Data Scientists (Tool Based)
Introduction to Data Science
Object Oriented Programming using Java Lab
Data Structures and Algorithms Lab


Semester III

Reasoning and Thinking
Software Engineering
Database Management Systems
Probability & Statistics - II
Computer Networks
Scientific Programming Using R (Tool Based)
Database Management Systems Lab
Software Engineering Lab

Semester IV

Inferential Statistics
Data Manipulation Using PL / SQL Programming
Big Data Analytics (Tool Based)
Machine Learning Algorithms - I (Tool Based)
Exploratory Data Analysis (Tool Based)
Business Communication and Presentation Skills / Professional Ethics
Inferential Statistics Lab
Data Manipulation Using PL / SQL Programming Lab

Semester V

Times Series Analysis
Cloud Computing
Machine Learning Algorithms - II (Tool Based)
Data Visualization (Tool Based)
Elective – I
Elective – II
Times Series Analysis Lab
Cloud Computing Lab
Elective – III (Any One)
Data Science Project Management
Data Science Industry Use Cases
Advance in Data Science

Semester VI

Elective - III
Project and Viva-Voce
List of Electives:
Elective – I (Any One)
Internet of Things
Artificial Intelligence
Neural Networks
Cloud Computing Lab NA
Elective – III (Any One)
Data Science Project Management
Data Science Industry Use Cases
Advance in Data Science