Big Data Analytics

Syllabus Revised in R2016

R16 IT Sem 8 ITC801 Big Data Analytics - Syllabus [ PDF ]
R16 COMP Sem 7 CSDLO7032 Big Data Analytics - Syllabus [ PDF ]

IT Syllabus Revised in R2012

Syllabus to view/download click here.
BEIT Sem 7 and 8 Examination Scheme details click here.
Question Bank Booklet (All Chapters) click here.
Guidelines for Writing Lab Experiments click here.

Course Materials (PPTs/PDFs/Handouts)

Ch1 [3 Hrs]: Introduction to Big Data [ PDF ]
Ch2 [2 Hrs]: Introduction to Hadoop [ PDF ]
Ch3 [4 Hrs]: NoSQL [ PDF ]
- Installation Procedure [ MongoDB Install ]
- Introduction to MongoDB [ PDF ]
Ch4 [6 Hrs]: MapReduce and the New Software tack [ PDF ]
Ch5 [3 Hrs]: Finding Similar Items [ PDF ]
- Solved Example [ e.g Edit Distance ]
Ch6 [6 Hrs]: Mining Data Streams [ PDF ]
- Solved Example [ e.g Flajolet Martin Algorithm ]
- Solved Example [ e.g Bloom Filter ]
Ch7 [5 Hrs]: Link Analysis [ PDF ]
- Solved Example PageRank [ e.g LaTeX PDF ]
- Solved Example [ PageRank ]
- Solved Example TPSR [ e.g TSPR ]
- Solved Example HITS [ e.g HITS ]
Ch8 [5 Hrs]: Frequent Itemsets [ PDF ]
Ch9 [5 Hrs]
- Classification [ PDF ]
- Clustering [ PDF ]
- Model Evaluation [PDF]
Ch10 [4 Hrs]: Recommendation Systems [ PDF ]
- Solved Example [ e.g TF-IDF ]
Ch11 [5 Hrs]: Mining Social-Network Graphs [ PDF ]

Course Examination and Evaluation

- 2018-19:
  - Assignments: Assignment #1 [Question ] [Solution] and Assignment #2 [Question ]
  - Internal Assessment: Class Test #1 [Paper ] [Solution] and Class Test #2 [Paper] [Solution]
  - Study Camp / Prelim Examination: Prelim [Paper ] [Solution ]
  - Lab Experiments: Experiment List [ Sheet ] and Lab Manual [PDF]
  - Theory University May 2019 Examination [Paper][Solution]
- 2017-18:
  - Assignments: Assignment #1 [Question ] [Solution] and Assignment #2 [Question ] [Solution]
  - Internal Assessment: Class Test #1 [Paper ] [Solution] and Class Test #2 [Paper] [Solution]
  - Study Camp / Prelim Examination: Prelim [Paper ] [Solution ]
  - Lab Experiments: Experiment List [ Sheet ] and Lab Manual [PDF]
  - Theory University Dec 2018 Examination [Paper][Ans]
- 2016-17:
  - Assignments: Assignment #1 [Question ] [Solution] and Assignment #2 [Question ] [Solution ]
  - Internal Assessment: Class Test #1 [Paper ] [Solution ] and Class Test #2 [Paper ] [Solution ]
  - Study Camp: Camp #1 [Paper ] [Solution ] and Camp #2 [Paper ] [Solution ]
  - Lab Experiments: Experiment List [ Sheet ] and Lab Manual [PDF]
  - Theory Examination [ Paper ] [ Solution ]
- 2015-16:
  - Assignments: Assignment #1 [Question ] [Solution] and Assignment #2 [Question ] [Solution]
  - Internal Assessment: Class Test #1 [Paper] [Solution] and Class Test #2 [Paper] [Solution]
  - Study Camp: Camp #1 [Paper ] [Solution ] and Camp #2 [Paper ] [Solution ]
  - Lab Experiments: Experiment List [ Sheet ] and Lab Manual [PDF]
  - Theory Examination [Que][Ans]

Course Lab Experiments

Guidelines for Writing Lab Experiments click here.

- Lab 1 [Ch1]: To draw and explain Hadoop Architecture and Ecosystem with the help of a case study using WorkCount example. To define and install Hadoop.
  - Introduction
  - Hadoop Installation Steps
- Lab 2 [Ch2]:To implement the following file management tasks in Hadoop System (HDFS): Adding files and directories, Retrieving files, Deleting files
  - Hadoop - HDFS UI Ports and Nodes
  - Hadoop_Lab - helloHDFS
- Lab 3 [Ch2]:To run a basic Word Count MapReduce program to understand MapReduce Paradigm: To count words in a given file, To view the output file, and To calculate execution time.
- Lab 4 [Ch3]: To perform NoSQL database using mongodb to create, update and insert.
- Lab 5 [Ch5]: To study and implement basic functions and commands in R Programming.
- Lab 6 [Ch5]: To finding similar documents with Cosine Similarity in R.
- Lab 7 [Ch6]: To implement Bloom Filters for filter on Stream Data in C++/java.
- Lab 8 [Ch6]: To implement Flajolet-Martin Algorithm for counting distinct elements in Stream Data.
- Lab 9 [Ch8]: To implement clustering program using R programming.
- Lab 10 [Ch1]: To build WordCloud, a text mining method using R for easy to understand and visualization than a table data.
- Lab 11 [Ch5 and Ch10]: To find Term Frequency and Inverse Document Frequency (tf-idf) Matrix for Recommendation Systems and Plot TF Using R used.
- Lab 12 [Ch7]: