BCA in Data Analytics at Sri Sai University - Course, Fees, Admission, Seats, Syllabus

Year 1	Year 2	Year 3
Introduction to Programming, Mathematics for Computing, Basics of Data Analytics, Communication Skills, Computer Organization and Architecture.	Object-Oriented Programming, Database Management Systems, Operating Systems, Data Structures and Algorithms, Statistical Methods for Data Analysis.	Big Data Analytics, Machine Learning, Data Visualization, Cloud Computing for Data Analytics, Project Work (Data Analytics).

Please note, the specific courses and their arrangement might vary slightly based on the latest curriculum updates by Sri Sai University. Always refer to the official university website or contact the university directly for the most accurate and current information.

As of my recent update in April 2023 the specific details of the BCA in Data Analytics program at Sri Sai University may change over time to align with curriculum updates and industry requirements. However I can give you an overview of the key subjects usually included in a BCA program that focuses on Data Analytics. These subjects aim to provide students with the skills and knowledge, in the field of data analytics within the context of computer applications.Here are some important topics in computer science that you should know; **Introduction to Programming**; This covers the basics of programming languages like Python, Java or C++. **Database Management Systems**; It focuses on understanding SQL, NoSQL, database design and implementation. **Data Structures and Algorithms**; This is about concepts that help with efficient data manipulation and storage. **Computer Networks**; It covers the basics of network structure and operations. **Operating Systems**; This topic dives into how operating systems work, including processes, memory management and file systems. Now lets move on to topics in data analytics; **Introduction to Data Analytics**; This provides an overview of data analytics, its importance and various applications. **Statistics for Data Science**; Here you learn advanced statistical methods used for data analysis. **Data Visualization**; Techniques for visualizing data using tools like Tableau, Power BI or Python libraries such as Matplotlib or Seaborn are covered here. **Big Data Technologies**; You'll be introduced to the concept of data along with technologies like Hadoop and Spark used for processing large datasets. **Machine Learning**; Basics of machine learning algorithms used for modeling and analysis will be explained. **Data Mining**; Techniques for discovering patterns and knowledge from amounts of data fall under this category. **Data Warehousing**; Concepts related to data warehousing, ETL processes (extract transform load) and OLAP ( analytical processing) will be discussed. Lastly there are elective topics you can explore; **Advanced Database Management;** This delves into NoSQL databases database optimization techniques and advanced SQL queries. **Cloud Computing for Data Analytics;** Learn how cloud platforms like AWS,Azure and Google Cloud are utilized in the field of data analytics. **Business Intelligence;** Discover techniquesand tools that help convert data into business insights. **Ethics and Privacy in Data Analytics;** Understand the considerations and privacy concerns involved in handling and analyzing data. To gain exposure it is recommended to engage in hands on activities such, as projects, internships or real world applications that bring these concepts to life. *Real life. Practical Examples*; Gain hands on experience by working on real world projects and studying case studies to apply the knowledge you have learned. *Internships*; Get practical work experience, in companies to gain firsthand knowledge and skills.