BCA in Data Analytics at Medhavi Skills University - Course, Fees, Admission, Seats, Syllabus

| **Attribute** | **Detail** | | | | | Duration of the Course | The course lasts for a period of 3 years | | Level of the Course | This course is designed for students | | Tuition Fees | Unfortunately specific information regarding the fees is not available | | Method of Study | It is likely that this course includes a combination of both offline learning but it is important to confirm the exact mode with the institution | | Type of Institution | Medhavi Skills University is a specialized university focusing on skill development | Please keep in mind that tuition fees and the exact method of study can vary and are subject to updates by the institution. It would be advisable to contact Medhavi Skills University for the most up, to date information.

As of my update in April 2023 it may not be directly possible to access specific course outlines for the BCA program in Data Analytics at Medhavi Skills University. However I can provide you with an overview of the key subjects typically covered in a Bachelor of Computer Applications (BCA) program specializing in Data Analytics. These subjects aim to equip students with the skills and knowledge to effectively analyze data and make well informed decisions. Here's a broad outline of the areas of study; 1. Introduction to Data Analytics; This module focuses on understanding the fundamentals of data analysis, including its significance in todays world various types of data and the overall process involved. 2. Programming Foundations; In this module students will learn programming languages like Python and R that are vital for data analysis. This includes gaining an understanding of syntax, control structures, data structures and basic algorithms. 3. Database Management Systems (DBMS); This module delves into comprehending how databases function, covering topics such as databases utilizing SQL for data manipulation purposes and exploring NoSQL databases for handling unstructured data. 4. Data. Data Mining; This module explores techniques, for storing volumes of data efficiently while also focusing on extracting valuable information or identifying patterns from this stored data. 5. **Statistics for Data Science**; Develop an understanding of statistics encompassing descriptive statistics, inferential statistics, probability distributions, hypothesis testing and regression analysis. 6. **Machine Learning**; Get introduced to machine learning algorithms covering supervised and unsupervised learning, decision trees, clustering, neural networks and deep learning. 7. **Big Data Technologies**; Gain an understanding of the tools and technologies utilized for handling data such as Hadoop, Spark and their associated ecosystems. 8. **Data Visualization**; Learn how to visually represent data using tools like Tableau or Power BI or Python libraries such as Matplotlib and Seaborn. This helps in making data more comprehensible and actionable. 9. **Business Intelligence**; Discover the application of data analytics, in business contexts to support decision making. This includes utilizing dashboards and reporting tools effectively. 10. **Ethics and Data Privacy**; Explore the considerations involved in data analytics regarding data protection laws, privacy regulations and responsible usage of data. 11. **Capstone Project**; Most programs culminate with a capstone project or internship that enables students to apply their knowledge to real world data analytics challenges. Staying up to date with the developments and advancements in data analytics is crucial. This includes being aware of emerging trends, like AI powered analytics and predictive analytics.