MSEI-021 Introduction to Information Security | Quick Readable Notes For IGNOU Exam 
MSEI-022 Network Security | Quick Readable Notes For IGNOU Exam 
MSEI-023 Cyber Security | Quick Readable Notes For IGNOU Exam 
MSE-024 Policy, Standards and Laws | Quick Readable Notes For IGNOU Exam 
MSEI-025 Application and Business Security Developments | Quick Readable Notes For IGNOU Exam 
MSEI-026 BCP, DR Planning and Audit | Quick Readable Notes For IGNOU Exam 
MSEI-027 Digital Forensics | Quick Readable Notes For IGNOU Exam 
MSE-029 Cyber Attack: use of Technology in Cyberspace | Quick Readable Notes For IGNOU Exam 
MSE-030 Cloud and Infrastructure Security | Quick Readable Notes For IGNOU Exam 
MSE-031 Cyber Security using Python | Quick Readable Notes For IGNOU Exam 
MSE-033 Web Application Testing and Audit | Quick Readable Notes For IGNOU Exam 
MSE-034 E-commerce and Cyberspace | Quick Readable Notes For IGNOU Exam 
MSE-035 IoT Security | Quick Readable Notes For IGNOU Exam 
MSE-036 Latest Trends in Information and Cyber Security | Quick Readable Notes For IGNOU Exam MCS-226 Data Science and Big Data | Quick...
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| Category | : MASTER‘S DEGREE PROGRAMMES |
| Sub Category | : Master of Science (Information Security) (MSCIS) |
| Products Code | : MSCIS-MSEI-S3-5.59 |
| HSN Code | : 490110 |
| Language | : English |
| Author | : BMAP EDUSERVICES PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 100-130 |
| Weight | : 157gms |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
MCS-226 Data Science and Big Data is a specialized course designed for students pursuing the Master of Computer Science (MCS) degree. This course provides a comprehensive exploration of data science methodologies and big data analytics, focusing on techniques for handling and analyzing large-scale datasets. Topics include data preprocessing, exploratory data analysis, predictive modeling, machine learning algorithms (e.g., regression, classification, clustering), big data technologies (e.g., Hadoop, Spark), data visualization, and ethical considerations in data science. Case studies on real-world applications of big data and practical exercises enhance students' ability to apply data science techniques to solve complex problems.
Key benefits of this course include:
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