data analytics lifecycle
swaroopa
CSE409A_Assignment.docx
Faculty of Engineering and Technology |
|||
Ramaiah University of Applied Sciences |
|||
Department |
Computer Science and Engineering |
Programme |
B. Tech |
Semester/Batch |
8th /2016 |
||
Course Code |
CSC409A |
Course Title |
Data Analytics |
Course Leader |
Mohan Kumar K N |
Assignment - 01 |
|||||
Register No |
|
Name of Student |
|
||
Sections |
|
Marking Scheme |
Marks |
||
|
|
|
|
Max Marks |
First Examiner Marks |
Moderator Marks |
Part-A |
|
||||
|
|
A 1.1 |
Data Analytics and its applications |
04 |
|
|
|
|
A 1.2 |
Real world use cases –industries, technologies |
04 |
|
|
|
|
A 1.3 |
Barriers to adoption – technical and non technical |
04 |
|
|
|
|
A 1.4 |
future of analytics – best guess |
04 |
|
|
|
|
A1.5 |
Justification and stance taken |
04 |
|
|
|
|
|
Part-A Max Marks |
20 |
|
|
Part B 1 |
|
||||
|
|
B 1.1 |
Phase 2 tools (any two) |
8 |
|
|
|
|
B 1.2 |
Phase 4 tools (any two) |
8 |
|
|
|
|
B 1.3 |
kinds of use scenarios |
4 |
|
|
|
|
|
Part-B 1 Max Marks |
20 |
|
|
Part B 2 |
|
||||
|
|
B2.1 |
Introduction to the recommended method(s) |
06 |
|
|
|
|
B2.2 |
Suggestion and selection the attributes |
08 |
|
|
|
|
B2.3 |
Justification |
06 |
|
|
|
|
|
Part-B 2 Max Marks |
20 |
|
|
Part B 3 |
|
||||
|
|
B3.1 |
Recommend relevant solution |
08 |
|
|
|
|
B3.2 |
Issues with information retrieval |
04 |
|
|
|
|
B3.3 |
justification |
08 |
|
|
|
|
|
|
|
|
|
|
|
|
Part-B 3 Max Marks |
20 |
|
|
Part B 4 |
|
||||
|
|
B4.1 |
Introduction to big data platform |
04 |
|
|
|
|
B4.2 |
Problem solving approach |
04 |
|
|
|
|
B4.3 |
Design and implementation |
04 |
|
|
|
|
B4.4 |
Performance analysis |
04 |
|
|
|
|
|
Part-B 4 Max Marks |
20 |
|
|
|
|
Total Assignment Marks |
100 |
|
|
|
|
|
|||||
|
Course Marks Tabulation |
|||||
|
Component- CET B Assignment |
First Examiner |
Remarks |
Second Examiner |
Remarks |
|
|
A |
|
|
|
|
|
|
B.1 |
|
|
|
|
|
|
B.2 |
|
|
|
|
|
|
B.3 |
|
|
|
|
|
|
B.4 |
|
|
|
|
|
|
Marks (Max 50 ) |
|
|
|
|
|
|
Marks (out of 25 ) |
|
|
|
|
|
|
Signature of First Examiner Signature of Second Examiner |
Please note:
1. Documental evidence for all the components/parts of the assessment such as the reports, photographs, laboratory exam / tool tests are required to be attached to the assignment report in a proper order.
1. The First Examiner is required to mark the comments in RED ink and the Second Examiner’s comments should be in GREEN ink.
1. The marks for all the questions of the assignment have to be written only in the Component – CET B: Assignment table.
1. If the variation between the marks awarded by the first examiner and the second examiner lies within +/- 3 marks, then the marks allotted by the first examiner is considered to be final. If the variation is more than +/- 3 marks then both the examiners should resolve the issue in consultation with the Chairman BoE.
Assignment 1
Instructions to students:
1. The assignment consists of 5 questions: Part A – 1 Question, Part B- 4 Questions.
2. A maximum mark is 100.
3. The assignment has to be neatly word processed as per the prescribed format.
4. The maximum number of pages should be restricted to 25.
5. Restrict your report for Part-A to 5 pages only.
6. Restrict your report for Part-B to a maximum of 20 pages.
7. The printed assignment must be submitted to the course leader.
8. Submission Date: 31 / 03/2020
9. Submission after the due date is not permitted.
10. IMPORTANT: It is essential that all the sources used in preparation of the assignment must be suitably referenced in the text.
11. Marks will be awarded only to the sections and subsections clearly indicated as per the problem statement/exercise/question
Preamble:
The course is intended to teach the design, development, analysis and evaluation of Data Analytics applications. Employing appropriate techniques, methods and technology in various domains of computing is discussed. Data mining algorithms, tuning them for a given application and actionable interpretations are emphasized. It helps to solve practical applications with data analysis, turning business intelligence into real-world outcomes. Students are trained to analyses, visualize and interpret the data and associated implicit insights.
PART – A 20 Marks
Data Analytics is the science of analyzing data to convert information into useful knowledge. This helps to understand the world better and in many contexts enable to make better decisions. Technological advances and associated changes in daily life have produced a rapidly expanding new content/data/information sources. Although many opportunities exist, big data and data analytic technologies also present many challenges such as understanding data, quality of data, security and real time integration. Most of the organizations are facing the imbalance on data analysts and the amount of data being produced.
Debate on the statement “Data deluge in information and starving for knowledge in Data Analytics”
Your debate should include:
A1.1 Introduction to Data Analytics and its applications
A1.2 Illustration with real world examples
A1.3 Discussion on the barriers for adoption
A1.4 Discussion of the future of analytics
A1.5 Stance taken and justification
PART – B 80 Marks
B.1 20 Marks
Data analytics lifecycle defines analytics process and best practices spanning from discovery to project completion. Consider data preparation and model building phases of data analytics lifecycle and select relevant tools for each phase and defend with suitable example. Perform the following:
B1.1 Discuss data preparation phase tools
B1.2 Discuss model building phase tools
B1.3 Justify with suitable scenarios
B.2 20 Marks
A data science team is working on a book recommendation problem. The books are available in different categories. If a customer buys a book he should be recommended other books and categories of books of his preference:
B2.1 Model different method(s) to address the above issue.
B2.2 Identify suitable attributes.
B2.3 Justify your solution by comparison.
A certain company ‘A’ wants to market its new product. Manual marketing is time consuming and a costly process. The model should spread the product information like virus (viral marketing).
B3.1 Recommend a solution
B3.2 Discuss issues
B3.3 Justification
B.4 20 Marks
Inverted index: Inverted Index is mapping of text in the document. Mainly used in search engines, it provides faster lookup on text searches. The output file must contain a list of all words also the number of times it occurs. The Map method can read the input file and output (word, filename) as the key-value pair. Reducer method can use a hash map of (filename, count) to count the occurrences of each filename for a particular word key. Solve the problem using Big data (Hadoop \R).
Your report should include:
B4.1 Introduction to Big data platform
B4.2 Problem solving approach
B4.3 Design and implementation
B4.4 Performance analysis
❧❧❧
Detailed Marking Scheme
❧❧❧
