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Master’s Degree Programme (MCA) – Data Science and Big Data Solved Assignment Answer | MCS 226 | 2021-2022

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Free MCS 226 Solved Assignment Answer | Data Science and Big Data | For July 2021 – October 2022

IGNOU MCS 226 Solved Assignment, This assignment is the curriculum of the Master of Computer Applications (MCA) program. For other courses, students check our website categories section to find their courses and assignments over there.

MCS 226 Data Science and Big Data Solved Assignment 2021-2022

IGNOU MCS 226 Data Science and Big Data assignment are 100 marks. There is only two sections – A & B. Students have to answer all the questions. Download IGNOU MCS 226 Assignment free without any registration, Students can easily download the IGNOU assignment question paper from the official website of the university. They are not required to pay any fees or charges for this.

Title NameMCS 226 Solved Assignment 2021-2022 | Data Science and Big Data
UniversityIGNOU
Service TypeSolved Assignment (Soft Copy/Q&A form)
CourseMCA
LanguageEnglish
SemesterSession: July 2021 – January 2022
Short NameMCS 226
Assignment CodeMCS-226(III)/Asst/TMA/July2021- October 2022
ProductAssignment of MCS 226 | 2021-2022
Submission DateValid from 1st July 2021 to 31st October 2022
Assignment PdfDownload Now

How to get Data Science and Big Data, MCS 226 Solved Assignment?

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The answer of MCS 226 | Data Science and Big Data | Solved Assignment 2021-2022:

Important Notes:

1. Submit your assignments to the Coordinator of your Study Centre on or before the due date.
2. Assignment submission before due dates is compulsory to become eligible for appearing in corresponding Term End Examinations. For further details, please refer to Programme Guide of Master of Computer Applications (MCA _NEW).
3. To become eligible for appearing the Term End Practical Examination for the lab courses, it is essential to fulfill the minimum attendance requirements as well as submission of assignments (on or before the due date). For further details, please refer to the Programme Guide of Master of Computer Applications (MCA _NEW).
4. The viva voce is compulsory for the assignments. For any course, if a student submitted the assignment and not attended the viva-voce, then the assignment is treated as not successfully completed and would be marked as ZERO.

This assignment has sixteen questions of 5 Marks each, answer all questions. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the Programme Guide for the format of presentation.

1. What is data science? What are its applications? Define the terms Descriptive, Exploratory and Predictive in the context of data analysis. What is the difference between Causal inference and prediction?

2. Explain the following with the help of an example in the context of statistics and Probability:
Conditional Probability, Bayes Theorem, Normal distribution, Central limit theorem and Statistical Hypothesis

3. Explain the concept of data pre-processing, data extraction, data cleaning, data curation and data integration with the help of an example of each.

4. A class has 25 students. Create a data set of marks of the students in Mathematics out of a maximum of 50 marks. Make the histogram and box plot for this data. Can you draw scatter plots using this data? Give reasons in support of your answer.

5. Explain Big data and its characteristics. How is Big data different to relational data? Explain with the help of an example. Define the characteristics of HDFS. Explain purpose of name node, data node and job tracker in this context.

6. What is Map-Reduce programming? Explain the map phase, shuffling and sorting and reduce phase with the help of an example of word counting problem.

7. Explain the features of Apache SPARK, HIVE and HBASE.

8. What are NoSQL databases? How are they different from relational database management system? List the features of any four types of NoSQL databases.

9. Explain the Jaccard similarity of sets with the help of an example. What are the ways of finding similarity between two documents? Also, define the term collaborative filtering.

10. What is a data stream? How is it different to relational data? List the issues and challenges of handling data streams. What is the role of bloom filter?

11. Explain the role of link analysis. Explain a page ranking algorithm with the help of an example. What is link spam? Explain the role of hubs and authorities for finding page rank.

12. Explain the process and issues of the following:
Advertising on web, Recommendation system, Mining of social networks.

13. Write program using R for the following tasks:
(i) Computation of income tax of a vector of size 10, consisting of total annual income of 10 different person. The tax computation should be 10%, if annual income is below 5 lakhs and 20% if it is above 2 lakhs.
(ii) Matrix addition, subtraction and multiplication
(iii) Finding inverse of a matrix

14. Create a sample data of the marks of 20 students in five different subjects using MSExcel. How can you use this data for programming in R? Write programs using R programming language to create four different plots using this data.

15. Write program using R to demonstrate any one of the following: chi-square testing or linear regression or logistic regression. You may choose any sample data.

16. Write steps about how R programming language can be used for performing the following analysis task: (i) classification (ii) clustering (iii) finding association rules

Wrapped Up

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