โน๏ธ This semester also includes an Open Elective, Industrial Training / Seminar and SODECA as per RTU's teaching scheme. See the official PDF for the complete scheme.
Introduction: Objective, scope and outcome of the course.
1 hr
Unit 2
Introduction to Big Data: Big data features and challenges, Problems with Traditional Large-Scale System, Sources of Big Data, 3 Vโs of Big Data, Types of Data. Working with Big Data: Google File System. Hadoop Distributed File System (HDFS) - Building blocks of Hadoop (Namenode. Data node. Secondary Namenode. Job Tracker. Task Tracker), Introducing and Configuring Hadoop cluster (Local. Pseudo- distributed mode, Fully Distributed mode). Configuring XML files.
10 hrs
Unit 3
Writing MapReduce Programs: A Weather Dataset. Understanding Hadoop API for MapReduce Framework (Old and New). Basic programs of Hadoop MapReduce: Driver code. Mapper code, Reducer code. Record Reader, Combiner, Partitioner.
8 hrs
Unit 4
Hadoop I/O: The Writable Interface. Writable Comparable and comparators. Writable Classes: Writable wrappers for Java primitives. Text. Bytes Writable. Null Writable, Object Writable and Generic Writable. Writable collections. Implementing a Custom Writable: Implementing a Raw Comparator for speed, Custom comparators.
8 hrs
Unit 5
Pig: Hadoop Programming Made Easier Admiring the Pig Architecture, Going with the Pig Latin Application Flow. Working through the ABCs of Pig Latin. Evaluating Local and Distributed Modes of Running Pig Scripts, Checking out the Pig Script Interfaces, Scripting with Pig Latin.
7 hrs
Unit 6
Applying Structure to Hadoop Data with Hive: Saying Hello to Hive, Seeing How the Hive is Put Together, Getting Started with Apache Hive. Examining the Hive Clients. Working with Hive Data Types. Creating and Managing Databases and Tables, Seeing How the Hive Data Manipulation Language Works, Querying and Analyzing Data.
Implement the following Data structures in Java i) Linked Lists ii) Stacks iii) Queues iv) Set v) Map
Perform setting up and Installing Hadoop in its three operating modes: Standalone, Pseudodistributed, Fully distributed.
Implement the following file management tasks in Hadoop: ๏ท Adding files and directories ๏ท Retrieving files ๏ท Deleting files Hint: A typical Hadoop workflow creates data files (such as log files) elsewhere and copies them into HDFS using one of the above command line utilities.
Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
Write a Map Reduce program that mines weather data. Weather sensors collecting data everyhour at many locations across the globe gather a large volume of log data, which is a goodcandidate for analysis with MapReduce, since it is semi structured and record-oriented.
Implement Matrix Multiplication with Hadoop Map Reduce
Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter your data.
Install and Run Hive then use Hive to create, alter, and drop databases, tables, views, functions, and indexes.
Solve some real life big data problems. REFERENCE BOOKS 1 Ted Malaska and Jonathan Seidman "Hadoop Application Architectures" 2 Nathan Marz and James Warren"Big Data: Principles and best practices of scalable realtime data systems" 3 John W. Foreman "Data Smart: Using Data Science to Transform Information into Insight" IV Year- VII & VIII Semester: B. Tech. (Artificial Intelligence and Data Science)
Basic R Syntax and Data Structures: Explore basic arithmetic ope rations and variable assignment, Practice creating and manipulating vectors, matrices, and lists, Perform indexing and slicing on data structures.
Data Import and Cleaning: Import data from a CSV file, Excel spreadsheet, or JSON file. Clean the dataset by handling missing values and outliers.
Data Visualization using ggplot2: Create scatter plots, bar charts, and histograms using ggplot2. Customize plot aesthetics like titles, labels, and colors.
Exploratory Data Analysis: Calculate summary statistics (mean, median, standard deviation). Create box plots and violin plots to visualize distribution.
Hypothesis Testing: Perform t-tests and chi-square tests on relevant datasets. Interpret p-values and draw conclusions.
Clustering and Dimensionality Reduction: Apply k-means clustering to segment data. Perform principal component analysis (PCA) for dimensionality reduction.
Machine Learning Exploration: Apply a basic machine learning algorithm (e.g., decision tree) on a dataset. Evaluate model performance and visualize results.
Linear Regression and Visualization: Fit a linear regression model to a dataset. Visualize the regression line along with the data points.
Web Scraping and Data Collection: Use libraries like rvest to scrape data from websites. Collect and clean data from multiple sources for analysis. REFERENCE BOOKS 1 Hadley Wickham, Garrett Grolemund, "R for Data Science", O'Reilly Media 2 Norman Matloff, "The Art of R Programming" No Starch Press 3 Garrett Grolemund "Hands-On Programming with R",O'Reilly Media IV Year- VII & VIII Semester: B. Tech. (Artificial Intelligence and Data Science)
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About the RTU 7th Sem AI & DS syllabus
This page shows the official Rajasthan Technical University (RTU), Kota syllabus for B.Tech 7th semester Artificial Intelligence & Data Science (AI&DS), taken directly from RTU's published PDF: every subject with its code, credits, marks and unit-wise topics. Use it to plan your preparation unit by unit, then practise with the RTU 7th Sem previous year papers and notes.
Frequently asked questions
What subjects are in the RTU 7th Sem AI & DS syllabus?
The theory subjects are Big Data Analytics (7AID4-01). Labs: Big Data Analytics Lab, R. Programming Lab.
Where can I download the official RTU 7th Sem AI & DS syllabus PDF?
Use the "Download official PDF" button on this page. It is the syllabus document published by Rajasthan Technical University (RTU), Kota.
Which session is this syllabus for?
The official document says it is effective from session 2020-21 for students admitted in 2020-21 onwards. RTU revises syllabi from time to time, so also check rtu.ac.in for notices.