MCS-226: Data Science & Big Data – Assignment Answer Index
This post serves as a master index for all answers to the IGNOU MAEC course assignment MCS-226: Data Science & Big Data for the academic year 2024–2025. Each question listed below is linked to its detailed answer. All answers are written in easy-to-understand language and contain over 600 words, suitable for 10th-grade reading level and above.
- 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?
- Discuss the implications of hypothesis testing results in decision-making. Provide examples of real-world situations where statistical hypothesis testing is commonly used.
- 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?
- 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.
- Explain the purpose of Apache Hive in the Hadoop ecosystem. How does Spark address limitations of the traditional MapReduce model?
- Define NoSQL databases and explain the primary motivations behind their development. Provide examples of scenarios where each type of NoSQL database is suitable.
- 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.
- 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.
- Describe the role of link analysis in the PageRank algorithm. How are links between web pages interpreted in the context of PageRank?
- 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?
📘 Category: MCS-226
🏷️ Tags: IGNOU, MAEC, Data Science, Big Data, Assignments 2024-25
✍️ Updated: September 2025