BCA in Data Science at VIT Vellore - Course, Fees, Admission, Seats, Syllabus

Based on my recent update in April 2023 the specific course structures for the BCA in Data Science at Vellore Institute of Technology (VIT) may vary as academic institutions frequently revise their curricula to keep up with technological advancements and industry demands. However I can provide an overview of important topics typically covered in a BCA in Data Science program at VIT or similar institutions; **Introduction to Data Science**; This module focuses on understanding the fundamental concepts of data science, its significance and its application across various fields. **Programming Fundamentals**; Here students learn programming languages for data science such as Python and R. The emphasis is on mastering syntax, data structures and basic algorithms. **Mathematics for Data Science**; This module covers mathematical concepts including linear algebra, calculus and statistics that are essential, for comprehending data science algorithms. **Database Management Systems (DBMS)**;To gain proficiency in database systems both SQL and NoSQL databases should be thoroughly understood. Efficient data management and retrieval techniques are aspects of this understanding. Data analysis and visualization play a role in the field. The ability to extract insights from datasets and present them visually using tools like Matplotlib, Seaborn or Tableau is an essential skill to acquire. Machine learning is another topic covered in this course. You will be introduced to machine learning algorithms, including supervised and unsupervised learning, regression, classification, clustering, as well as neural networks. Big data technologies are explored extensively. Technologies such as Hadoop, Spark and their respective ecosystems will be studied for processing and analysis of large datasets. Data mining techniques will also be covered in depth; these involve discovering patterns, anomalies and associations within datasets. Ethics and data privacy considerations are a part of the curriculum. Understanding concerns surrounding data handling and analysis is important; knowledge of existing data protection laws along with privacy concerns is equally emphasized. Applied data science projects form a part of the coursework. You will have hands on experience applying your acquired skills to real world problems involving data collection, cleaning, analysis and presentation. Deep learning concepts will also be introduced during the course. Convolutional Neural Networks (CNNs) well as Recurrent Neural Networks (RNNs) architectures are covered under this topic. Cloud computing for data science basics will also be discussed extensively using platforms such as AWS(Amazon Web Services) Azure or Google Cloud Services along with their services related to storage of amounts of data, alongside computation power required for analytics purposes The course also delves into business intelligence & analytics by exploring how effective utilization of gathered insights can inform decision making processes within organizations which further drives business strategies & performance metrics Please note that while this provides a general overview of the course content topics mentioned earlier there may be additional more specific topics covered in the actual curriculum. To ensure you have the precise and current information