BCA in Data Science at PDM University - Course, Fees, Admission, Seats, Syllabus

I don't have real-time access to the internet or specific databases, including the current syllabus for PDM University's BCA in Data Science program. However, I can create a generic example of what such a table might look like based on a typical BCA in Data Science curriculum. Please note that the actual syllabus may vary, and you should consult PDM University's official resources for the most accurate and up-to-date information.

Semester	Subjects
Semester 1	Introduction to Data Science, Mathematics for Data Science, Programming Fundamentals, English Communication, Environmental Studies
Semester 2	Database Management Systems, Data Structures, Object-Oriented Programming, Probability and Statistics, Digital Principles and Systems Design
Semester 3	Operating Systems, Algorithms for Data Science, Python Programming, Linear Algebra, Business Communication
Semester 4	Machine Learning, Big Data Analytics, Web Technologies, Discrete Mathematics, Financial Accounting
Semester 5	Deep Learning, Data Visualization, Cloud Computing for Data Science, Elective I, Project Work I
Semester 6	Artificial Intelligence, Natural Language Processing, Elective II, Elective III, Project Work II

This table is a simplified representation and includes generic subjects that might be part of a BCA in Data Science program. Electives and specific course titles can vary significantly between institutions. For the most accurate syllabus, please visit PDM University's official website or contact their academic department directly.

As of my update in April 2023 the specific details regarding the courses offered in the BCA in Data Science program at PDM University may vary depending on the academic year and any curriculum updates. However I can provide you with an overview of the important subjects typically covered in a Bachelor of Computer Applications (BCA) program with a specialization in Data Science. These topics aim to equip students with the skills and knowledge required for a career in data science and analytics. Please keep in mind that for the accurate and up to date information it is advisable to refer to PDM Universitys official curriculum or directly get in touch, with the university.Here are some of the topics that are typically covered in a BCA course focused on Data Science; **Introduction to Data Science**; An overview of data science, its significance and how it is applied across different industries. **Foundations of Programming**; Introduction to programming languages for data science, such as Python and R. **Mathematics for Data Science**; Covering fundamental mathematical concepts like linear algebra, calculus and statistics that form the basis for understanding data science algorithms. **Data. Algorithms**; Understanding different data structures (e.g., arrays lists, trees, graphs) and algorithms that are critical for manipulating and analyzing data. **Database Management Systems (DBMS)**; Basics of DBMS including SQL and NoSQL databases, well as concepts related to data modeling and database design. **Data Preprocessing and Analysis**; Techniques for cleaning, transforming and analyzing data in order to extract insights. **Machine Learning**; Introduction to machine learning algorithms such as supervised learning, unsupervised learning, regression analysis, classification techniques, clustering methods, and neural networks. **Big Data Technologies**; Overview of data concepts along with popular technologies like Hadoop and Spark used for processing large datasets efficiently. **Data Visualization**; Exploring techniques and tools used in visualizing data. Examples include Tableau, Power BI and Python libraries like Matplotlib or Seaborn. **Data Mining**; Understanding the process of extracting patterns and knowledge from amounts of information. **Ethics & Data Privacy**; Discussions around considerations data privacy laws, and responsible use of collected information. **Capstone Project** ; A hands on project where students can apply the concepts and techniques on real world problems in the field of Data Science. **Emerging Trends in Data Science** ; Introduction to the trends and technologies in the field of data science, including artificial intelligence (AI) Internet of Things (IoT) and blockchain technology for data management. Remember, the specific modules covered and their depth may vary from one course, to another.