BCA in Data Science at Tata Institute of Technology - Course, Fees, Admission, Seats, Syllabus

As of my last update in April 2023, I don't have access to real-time databases or specific course syllabi, including those for the Tata Institute of Technology's BCA in Data Science program. However, I can provide you with a general outline of what a typical BCA in Data Science syllabus might include. For the most accurate and up-to-date syllabus, please consult the official website of the Tata Institute of Technology or contact their admissions office directly. Below is a hypothetical example of what the syllabus might look like, formatted as requested in a single-line HTML table format. Note that this is a generic example and not specific to any institution:

Year	Semester	Subjects
1	1	Introduction to Programming, Mathematics for Data Science, Fundamentals of Computer and IT, English for Communication, Environmental Studies
1	2	Programming in Python, Discrete Mathematics, Database Management Systems, Principles of Data Science, Communication Skills
2	1	Data Structures and Algorithms, Probability and Statistics, Operating Systems, Object-Oriented Programming, Elective I
2	2	Machine Learning, Linear Algebra, Web Technologies, Software Engineering, Elective II
3	1	Big Data Analytics, Artificial Intelligence, Cloud Computing, Elective III, Project Work I
3	2	Deep Learning, Data Visualization, Ethical and Legal Issues in Data Science, Elective IV, Project Work II

Please remember, this table is purely illustrative and does not reflect the actual syllabus of the Tata Institute of Technology or any specific institution's BCA in Data Science program. Always refer to the official source for the most accurate information.

As of my update in April 2023 the specific details about a BCA program in Data Science from the Tata Institute of Technology may not be readily available online. The course offerings at Tata Institute of Technology can vary, at the undergraduate level. However I can share an overview of key topics typically included in a Bachelor of Computer Applications (BCA) program with a focus on Data Science. These topics aim to provide students with skills and knowledge in the field of data science and analytics; **Introduction to Data Science**; Exploring the fundamentals of data science, its significance and applications across various industries. **Programming Fundamentals**; Acquiring proficiency in programming languages for data science like Python and R emphasizing data manipulation, visualization and analysis techniques. **Mathematics for Data Science**; Delving into mathematical concepts such as statistics, probability, linear algebra and calculus that form the basis for data analysis and modeling. **Database Management Systems (DBMS)**; Understanding database management systems essential for organizing and retrieving datasets efficiently. This curriculum is designed to equip students, with the skills to excel in the dynamic field of data science.In the realm of database systems there's a focus on grasping SQL for data retrieval and NoSQL databases for managing datasets. **Data Structures and Algorithms**; Delving into data structures like arrays lists, trees and graphs along with essential algorithms for effectively storing, processing and analyzing data. **Data Visualization**; Exploring methods and tools to represent data using libraries such as Matplotlib, Seaborn, as well as tools like Tableau to convey data insights convincingly. **Machine Learning**; Introducing machine learning algorithms covering learning unsupervised learning, reinforcement learning techniques including regression, classification, clustering methods along with neural networks. **Big Data Technologies**; Understanding the array of data technologies like Hadoop, Spark along with their components designed for processing and analyzing large volumes of datasets. **Data Mining**; Unveiling techniques and procedures to uncover patterns and insights from datasets encompassing association rule learning, anomaly detection methods as well as predictive modeling approaches. **Ethics and Data Privacy**; Engaging in discussions about considerations in data science practices including aspects of data governance alongside privacy laws such, as GDPR and CCPA compliance. **Capstone Project**; A culminating project where students apply their acquired knowledge and skills to tackle real world challenges in the realm of data science often collaborating with industry partners. It's important to note that the specific curriculum details may vary significantly across educational institutions. For the precise and current details its recommended to refer to the official website.