• Attempt any five questions.
• All questions carry equal marks.
Explain Data Indexing. Discuss different indexing techniques and their importance in efficient data retrieval.
Explain the importance of Measures of Dispersion in statistical analysis and decision making.
Explain the concept of Sampling Distribution. Discuss its characteristics, importance and applications in statistical analysis.
Explain different methods of Data Categorization.
Discuss the role of Maximum Likelihood Estimation in Machine Learning and Data Analytics.
Explain Bayesian Networks. Discuss their structure and working.
Write the Short Note: (i) Principal Component Analysis (PCA) (ii) Statistical Hypothesis Testing.
Explain the concept of Data Wrangling. Discuss its importance in Data Analytics.
Describe different sources of data used in Data Analytics.
Define Data Cleaning. Explain various types of data quality problems and discuss suitable methods to resolve them.
Explain the process of designing an effective dashboard. Discuss various components and design principles used in dashboard development.
Define Multivariate Data Exploration. Explain its objectives and significance in complex data analysis.
Compare CSV, JSON, XML, and Parquet file formats with suitable examples.
Explain the architecture and working of Hadoop Distributed File System (HDFS). Discuss its advantages and limitations.
Analyze the role of Plotly in Business Intelligence applications. Discuss why interactive dashboards are preferred over traditional charts.
Explain how Power BI supports real-time analytics and interactive reporting.
