BCA in Data Analytics at JODHPUR NATIONAL UNIVERSITY - Course, Fees, Admission, Seats, Syllabus

Semester	Subjects
Semester 1	Introduction to Data Analytics, Mathematics for Computer Science, Programming Fundamentals, English Communication, Environmental Studies
Semester 2	Database Management Systems, Data Structures, Object-Oriented Programming, Discrete Mathematics, Principles of Management
Semester 3	Operating Systems, Web Technologies, Statistical Methods for Data Analysis, Python Programming, Minor Project I
Semester 4	Software Engineering, Machine Learning, Big Data Analytics, Elective I, Minor Project II
Semester 5	Data Visualization, Cloud Computing for Data Analytics, Elective II, Elective III, Summer Internship
Semester 6	Advanced Database Management Systems, Data Mining, Major Project, Elective IV, Elective V

Please note, the subjects listed in the table are hypothetical and may not accurately represent the actual syllabus of Jodhpur National University's BCA in Data Analytics program. For the most accurate and up-to-date syllabus, please refer to the official university website or contact the university directly.

As of my recent update in April 2023 the specific details of the BCA in Data Analytics program at Jodhpur National University may change over time to ensure that the curriculum aligns with advancements in technology and industry requirements. However I can provide you with an overview of the important subjects typically covered in a BCA program with a focus on Data Analytics. These subjects aim to equip students with a combination of computer science, statistical analysis, analytical skills and soft skills needed for analyzing and managing volumes of data. It is advisable to refer to the curriculum of the university or directly contact them for the most accurate and up, to date information.Here are some key topics within computer science and data analytics; 1. Introduction to Programming; This covers the basics of programming languages like Python, Java or R which are commonly used for data analytics. 2. Database Management Systems (DBMS); It involves understanding how databases function using SQL for data manipulation and exploring NoSQL databases for data. 3. Data Structures and Algorithms; This is knowledge for optimizing data analysis processes and understanding efficient ways to store, retrieve and manipulate data. Moving on to topics in data analytics; 4. Statistics for Data Science; This includes both basic and advanced statistical methods crucial for effective data analysis. Topics include probability, regression, hypothesis testing and inferential statistics. 5. Data Visualization; Here we explore techniques and tools for presenting data in formats using software such as Tableau, Power BI or Python libraries like Matplotlib or Seaborn. 6. Machine Learning; This field introduces machine learning algorithms (both supervised and unsupervised) networks and their applications in the realm of data analytics. 7. Big Data Technologies; Understanding technologies like Hadoop and Spark that enable processing datasets beyond what traditional database systems can handle is crucial, in this field. Lastly we have some applied skills well; 8. Business Intelligence; This covers techniques and tools used to convert data into actionable intelligence that informs strategic business decisions. 9. Data Mining; The process of discovering patterns and extracting knowledge from amounts of available information plays a significant role in this area. The subjects covered in this course encompass aspects such as preparing data categorizing information grouping similar data points identifying patterns and detecting irregularities. **. Privacy, in Data Analytics**; Gaining an understanding of the ethical considerations and privacy regulations associated with handling and analyzing data. **Project Management and Communication**; Developing skills to effectively manage data analytics projects, including planning, execution and conveying findings to technical individuals.