BCA in Data Analytics at Shaheed Bhagat Singh State Technical Campus - Course, Fees, Admission, Seats, Syllabus

Semester	Subjects
Semester 1	Introduction to Data Analytics, Mathematics for Data Science, Programming Fundamentals, English Communication, Environmental Studies
Semester 2	Database Management Systems, Data Structures and Algorithms, Statistical Methods for Data Analysis, Business Communication, Foreign Language
Semester 3	Operating Systems, Object-Oriented Programming, Data Visualization, Discrete Mathematics, Management Process
Semester 4	Machine Learning, Big Data Analytics, Web Technologies, Professional Ethics, Elective I
Semester 5	Deep Learning, Cloud Computing for Data Analytics, Project Work I, Elective II, Elective III
Semester 6	Capstone Project, Internship, Elective IV, Elective V

Please note, the subjects listed in each semester are hypothetical and for illustrative purposes only. For accurate and up-to-date syllabus information, you should contact Shaheed Bhagat Singh State Technical Campus directly or visit their official website.

As of my recent update in April 2023 the specific details of the BCA program in Data Analytics at Shaheed Bhagat Singh State Technical Campus (SBSSTC) may change over time to adapt to technological advancements and industry needs. However I can give you an overview of the important subjects typically covered in a BCA program with a specialization in Data Analytics. These subjects aim to provide students with the skills and knowledge in the field of data analysis and information management; **Introduction to Data Analytics**; This module focuses on understanding the fundamentals of data analysis, including its significance in todays world, different types of data and the process involved in data analytics. **Programming Fundamentals**; In this module students learn programming languages like Python and R that are crucial for data analysis. The focus is on techniques, for manipulating, visualizing and analyzing data. **Database Management Systems (DBMS)**; This module covers topics related to managing databases.In this course we will cover the basics of Database Management Systems (DBMS) including SQL (Structured Query Language) and NoSQL databases. Our focus will be on understanding how to store, retrieve and manage data effectively. We'll begin by exploring Data Structures and Algorithms. This will involve learning about data structures such as arrays, lists, stacks, queues, trees and graphs. Additionally we'll dive into algorithms that help us process and analyze data efficiently. The next topic on our agenda is Statistics for Data Science. We'll delve into methods and probability theory that are crucial for interpreting data accurately and making informed decisions. Topics covered will include statistics, inferential statistics, hypothesis testing and regression analysis. Data Visualization is another aspect we'll explore. We'll learn techniques and tools for representing complex data in a way that makes it more accessible and understandable. This includes understanding visualization tools like Tableau, Power BI or Matplotlib in Python. Machine Learning algorithms will also be covered in this course. We'll introduce learning techniques as well as unsupervised learning approaches. Topics such as decision trees, methods, regression analysis and neural networks will be explored to understand their applications in data analytics. Furthermore we'll touch upon Big Data Technologies to understand the challenges associated with handling volumes of data. An introduction to technologies like Hadoop Spark along with data processing frameworks will be provided. Lastly but not least important is the field of Data Mining where we discover meaningful patterns correlations insights, from datasets using various techniques tools available. In this course we will cover subjects such as association rule learning, classification and clustering. We will also delve into the topic of ethics and privacy, in data analytics. This includes discussing the considerations and privacy concerns related to data collection, analysis and reporting. Additionally we will focus on understanding laws and regulations regarding data protection. Lastly students will have the opportunity to showcase their knowledge and skills through a capstone project where they can solve real world data analytics problems.