BCA in Data Science at SIKKIM MANIPAL INSTITUTE OF TECHNOLOGY - Course, Fees, Admission, Seats, Syllabus

| **Attribute** | **Details** | | | | | Course Duration | The course takes 3 years to complete. | | Course Level | This is a course. | | Tuition Fees | The tuition fees may vary,. It's advisable to check the official website for the latest information. | | Mode of Study | The course is available for full time study. | | Institute Type | It is an institute. | Please note that the tuition fees for the BCA in Data Science program at Sikkim Manipal Institute of Technology can vary depending on factors such as the year of study and scholarship availability. To get the accurate and up, to date information it is recommended to visit the official website or directly contact the institute.

The Data Science program offered at Sikkim Manipal Institute of Technology (SMIT) is specifically designed for students pursuing a Bachelor of Computer Applications (BCA) degree. It aims to provide a foundation in computer applications while emphasizing the importance and application of data science in various fields. The curriculum includes topics such as Introduction to Data Science, Programming Fundamentals using languages like Python and R, Mathematics for Data Science encompassing linear algebra, calculus and statistics Database Management Systems covering SQL and NoSQL databases for handling large scale unstructured data, as well as Data Structures and Algorithms to enhance efficient data processing and analysis. Please note that the specific curriculum may undergo changes over time. These fundamental areas are typically considered crucial for a BCA program with specialization, in Data Science. Here are some of the topics covered in this course; 1. **Data. Visualization**; Learn techniques and tools for analyzing datasets extracting insights and visualizing data using popular tools such as Tableau, Power BI or Python libraries like Matplotlib and Seaborn. 2. **Machine Learning Fundamentals**; Gain an understanding of various machine learning algorithms including supervised, unsupervised and reinforcement learning. Topics will include regression, classification, clustering, decision trees and neural networks. 3. **Big Data Technologies**; Get introduced to the concepts of data and learn about technologies like Hadoop and Spark along with their ecosystems. These tools enable processing and analysis of volumes of data. 4. **Data Mining Techniques**; Explore techniques and tools used for discovering patterns, anomalies and associations within datasets. 5. **Cloud Computing for Data Science**; Understand how cloud platforms like AWS, Azure or Google Cloud can be leveraged for storing, processing and analyzing data in the cloud. 6. **Ethics and Data Privacy Considerations**; Discuss considerations related to data science projects such as responsible use of data in accordance with data privacy laws like GDPR (General Data Protection Regulation). 7. **Capstone Project Experience**; Apply the knowledge gained throughout the course, to real world data science problems through a capstone project. These topics provide an overview of what you can expect to learn in this course. Soft skills and effective communication play a role in data science due to the collaborative nature of the work and the need to convey findings to stakeholders. Additionally staying updated on emerging trends in the field is essential, for data scientists.