I don't have access to specific course syllabi, including those for the Global Institute of Science and Technology or any updates to their BCA in Data Analytics program as of September 2021 or later. However, I can create a generic example based on common subjects found in similar programs. Please adjust the content according to the actual syllabus.

Global Institute of Science and Technology BCA in Data Analytics Syllabus

Semester	Subjects
1st Semester	Introduction to Programming, Mathematics for Computer Science, Introduction to Data Analytics, English for Communication, Environmental Studies
2nd Semester	Object-Oriented Programming, Database Management Systems, Data Structures, Principles of Management, Discrete Mathematics
3rd Semester	Operating Systems, Web Technologies, Advanced Database Management Systems, Statistical Methods for Data Analysis, Soft Skills
4th Semester	Computer Networks, Machine Learning, Big Data Analytics, Professional Ethics, Elective I
5th Semester	Data Visualization, Cloud Computing, Internet of Things (IoT), Project Work I, Elective II
6th Semester	Advanced Machine Learning, Data Security and Privacy, Business Intelligence, Project Work II, Elective III

This table is a simplified example and should be updated with the actual syllabus content for the Global Institute of Science and Technology's BCA in Data Analytics program.

As of my update in April 2023 I don't have access to the specific curriculum details of the BCA in Data Analytics program offered by the Global Institute of Science and Technology. However I can give you an overview of the important subjects typically covered in a Bachelor of Computer Applications (BCA) program that focuses on Data Analytics. These subjects are crucial for students who want to pursue a career in data science, analytics and related fields. Please note that for the accurate and up, to date information it is recommended to consult the official website or directly contact the institution.Here are some key topics covered in a data analytics course; Introduction to Programming; This module covers the fundamentals of programming languages like Python, Java or R which are widely used in data analytics. Database Management Systems (DBMS); Gain an understanding of how databases function learn SQL for data manipulation and explore NoSQL databases for managing data. Data Structures and Algorithms; This segment is crucial for optimizing data analysis processes and comprehending efficient methods for storing, retrieving and manipulating data. In terms of Mathematics and Statistics; Mathematics for Computing; Explore topics such as discrete mathematics, linear algebra and calculus that form the foundation of algorithms in data analytics. Statistics and Probability; Learn about statistical measures, distributions, hypothesis testing and probability theory. These concepts are vital for interpreting and analyzing data effectively. Specific topics related to Data Analytics; Data Analysis and Visualization; Discover techniques for analyzing datasets and visualizing data using tools like Tableau, Power BI or Python libraries such as Matplotlib or Seaborn. Big Data Technologies; Get an introduction to data concepts along with the Hadoop ecosystem Spark framework. These technologies are commonly used to process amounts of data efficiently. Machine Learning; Understand the basics of machine learning algorithms (supervised and unsupervised learning) along with their application in data analytics. Data Mining; Explore techniques employed to discover patterns and extract knowledge from datasets. Advanced Topics & Electives may include; Deep Learning & Neural Networks; Gain an introduction to complex algorithms used in image recognition or natural language processing tasks. Business Intelligence; Understand how data analytics can be leveraged for effective decision making processes within a business context. Ethics & Privacy in Data Analytics; Delve into discussions on ethical considerations surrounding the collection and analysis of data as well, as privacy concerns associated with it. Practical Experience components may include; Projects & Case Studies; Engage in hands on projects utilizing real world data to apply the concepts learned in class. Internships; Some programs may offer internships to provide industry experience. It's important to note that specific course offerings may vary. These are typical components you can expect to encounter in a comprehensive data analytics program.