MCS-226 English Medium Solved Assignments 2024-25

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MCS-226 English Medium Solved Assignments 2024-25 Available

Q1: What is Exploratory Data Analysis (EDA) and why is it important in the data science workflow? What are the key components of the data science process?
Q2: Discuss the implications of hypothesis testing results in decision-making. Provide examples of real-world situations where statistical hypothesis testing is commonly used.
Q3: What is data preprocessing, and why is it a crucial step in the data science workflow? Why is it important to identify and handle outliers in a dataset during data preprocessing?
Q4: Discuss the significance of the three Vs (Volume, Velocity, Variety) in the context of big data. Provide examples of each of the three Vs in real-world scenarios. How does MapReduce facilitate parallel processing of large datasets? Explain the functionality of the Map function in the MapReduce paradigm with the help of an example.
Q5: Explain the purpose of Apache Hive in the Hadoop ecosystem. How does Spark address limitations of the traditional MapReduce model?
Q6: Define NoSQL databases and explain the primary motivations behind their development. Provide examples of scenarios where each type of NoSQL database is suitable.
Q7: How does collaborative filtering contribute to enhancing user experience and engagement in recommendation systems? Provide examples of industries or platforms where collaborative filtering is widely used.
Q8: Define what a Data Stream Bloom Filter is and explain its primary purpose in data stream processing. Introduce the Flajolet-Martin Algorithm and its role in estimating the cardinality of a data stream.
Q9: Describe the role of link analysis in the PageRank algorithm. How are links between web pages interpreted in the context of PageRank?
Q10: Explain the concept of decision trees in classification. Provide an example of building and visualizing a decision tree using R. How can K-means clustering be applied to a dataset in R?