BCA in Data Science at Satyug Darshan Institute of Engineering & Technology - Course, Fees, Admission, Seats, Syllabus

Sure, here's a simple HTML table representation for a BCA in Data Science course at Satyug Darshan Institute of Engineering & Technology, structured semester-wise. This table is a basic example and might not reflect the actual course structure of the institute.

Semester	Subjects
Semester 1	Introduction to Data Science, Mathematics for Computer Science, Programming Fundamentals, English Communication, Environmental Studies
Semester 2	Database Management Systems, Data Structures, Discrete Mathematics, Principles of Management, Python Programming
Semester 3	Operating Systems, Web Technologies, Statistical Methods for Data Science, Object-Oriented Programming, Minor Project I
Semester 4	Machine Learning, Big Data Analytics, Computer Networks, Professional Ethics, Minor Project II
Semester 5	Deep Learning, Cloud Computing, Elective I, Elective II, Summer Internship
Semester 6	Capstone Project, Elective III, Elective IV, Industrial Training

This table includes a header row for the semester and subjects, followed by rows for each semester detailing the subjects. Note that the actual subjects and structure may vary based on the institute's curriculum updates. Electives would also have specific titles based on the options provided by the institute.

As of my recent check in back in April 2023 the precise course specifics for the BCA program in Data Science at Satyug Darshan Institute of Engineering & Technology may undergo changes periodically to align with the dynamic nature of the data science domain. Nonetheless I can offer you an overview of key subjects usually included in a BCA program with a focus, on Data Science. These core subjects are fundamental. Are expected to be part of such a program; **Introduction to Data Science**; Grasping the fundamentals of data science, its significance and its applications across industries. **Foundations of Programming**; Acquiring knowledge of programming languages for data science, such as Python and R with a focus on data manipulation, visualization and analysis techniques. **Mathematics for Data Science**; Covering mathematical concepts like statistics, probability, linear algebra and calculus that form the basis for understanding data science algorithms. **Data. Algorithms**; Understanding how to efficiently organize, manage and process data using different data structures and algorithms. **Database Management Systems**; Learning about database administration, including SQL for retrieving data and NoSQL databases for handling scale unstructured data. **Data Preprocessing and Cleansing**; Methods for preparing data for analysis by addressing missing values, outliers and transforming the data. **Data Visualization**; Exploring techniques and tools to represent data in order to extract insights using libraries like Matplotlib, Seaborn as well as tools such as Tableau. **Machine Learning**; Introduction, to machine learning algorithms encompassing supervised learning unsupervised learning methods including regression analysis classifications clustering techniques and neural networks.