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

The BCA program in Data Science at JK Lakshmipat University is designed to provide students with the skills and knowledge to thrive in the ever changing field of data science. While the specific curriculum may vary there are key areas and topics that are typically important in a BCA program focused on Data Science; 1. **Introduction to Data Science**; Gain an understanding of the fundamentals of data science, its significance and its applications across various industries. 2. **Programming Basics**; Learn programming languages like Python and R which play a crucial role in manipulating analyzing and applying machine learning to data. 3. **Mathematics for Data Science**; Cover mathematical concepts such as linear algebra, calculus and statistics that serve as building blocks for comprehending data science algorithms. 4. **Database Management Systems (DBMS)**; Develop an understanding of how databases are structured master SQL for efficient database querying and explore NoSQL databases used for handling unstructured data. 5. **Data Structures and Algorithms**; Explore data structures (arrays, lists, stacks, queues, trees, graphs) along with algorithms designed to process and analyze data effectively. 6. **Data Visualization**; Acquire techniques and tools (, like Matplotlib, Seaborn, Tableau) for representing data in order to effectively communicate findings. 7.**Machine Learning**; This section covers the concepts of machine learning, such as supervised and unsupervised learning algorithms, decision trees, clustering techniques, regression analysis and neural networks. 8. **Big Data Technologies**; In this module we will introduce you to the concepts of big data and explore the ecosystem of Hadoop, Spark and other technologies used for processing and analyzing large datasets effectively. 9. **Data Mining**; Here you will learn about techniques and processes that enable us to discover patterns and extract valuable knowledge from vast amounts of data. 10. **Statistics and Probability**; Understanding methods and probability theory is essential for effective data analysis and making meaningful inferences. 11. **Ethics and Data Privacy**; This module focuses on the considerations surrounding data science practices. We will also delve into data protection laws and privacy issues that need to be addressed while working with data. 12. **Capstone Project**; The capstone project provides an opportunity for students to apply their acquired knowledge in solving real world data science problems. Often done in collaboration with industry partners it allows students to gain experience. 13. **Cloud. Storage**; Here we will cover the basics of cloud services like AWS, Azure Google Cloud for storing data as well as performing computations efficiently. 14. **Deep. Artificial Intelligence**; This section introduces topics in AI including deep learning techniques such as neural networks, convolutional neural networks (CNNs) as well, as natural language processing (NLP) methods. 15. **Business. Analytics**; Gaining insights, into how businesses utilize data analysis.