Course

Syllabus

INTRODUCTION TO DATA WRANGLING: What Is Data Wrangling? – Importance of Data Wrangling -How is Data Wrangling performed? – Tasks of Data Wrangling-Data Wrangling Tools-Introduction to Python-Python Basics-Data Meant to Be Read by Machines-CSV Data-JSON Data-XML Data.

WORKING WITH EXCEL FILES AND PDFS: Installing Python Packages-Parsing Excel Files-Parsing Excel Files -Getting Started with Parsing-PDFs and Problem Solving in Python-Programmatic Approaches to PDF Parsing-Converting PDF to Text-Parsing PDFs Using Pdf Miner-Acquiring and Storing Data-Databases: A Brief Introduction-Relational Databases: MySQL and PostgreSQL-Non-Relational Databases: NoSQL-When to Use a Simple File-Alternative Data Storage.

DATA CLEANUP: Why Clean Data? – Data Clean up Basics-Identifying Values for Data Clean Up-Formatting Data-Finding Outliers and Bad Data-Finding Duplicates-Fuzzy Matching-RegEx Matching-Normalizing and Standardizing the Data-Saving the Data-Determining Suitable Data Clean Up-Scripting the Cleanup-Testing with New Data

DATA EXPLORATION AND ANALYSIS: Exploring Data-Importing Data-Exploring Table Functions-Joining Numerous Datasets-Identifying Correlations-Identifying Outliers-Creating Groupings-Analyzing Data-Separating and Focusing the Data-Presenting Data-Visualizing the Data-Charts-Time-Related Data-Maps-Interactives-Words-Images, Video, and Illustrations-Presentation Tools-Publishing the Data-Open Source Platforms.

WEB SCRAPING: What to Scrape and How-Analyzing a Web Page-Network/Timeline-Interacting with JavaScript-In-Depth Analysis of a Page-Getting Pages-Reading a Web Page-Reading a Web Page with LXML-XPath-Advanced Web Scraping-Browser-Based Parsing-Screen Reading with Selenium-Screen Reading with Ghost. Py-Spidering the Web-Building a Spider with Scrapy-Crawling Whole Websites with Scrapy

Text Books / References

Text Books

1. Jacqueline Kazil & Katharine Jarmul,” Data Wrangling with Python”, O’Reilly Media, Inc, 2016.

References:

1. Dr. Tirthajyoti Sarkar, Shubhadeep,” Data Wrangling with Python: Creating actionable data from raw sources”, Packt Publishing Ltd,2019.
2. Stefanie Molin,” Hands-On Data Analysis with Pandas”, Packt Publishing Ltd,2019
3. Allan Visochek,” Practical Data Wrangling”, Packt Publishing Ltd,2017.

This course equips students with essential skills in data wrangling, analysis, and visualization using Python. Students will learn to handle diverse data formats, clean and prepare data, work with databases, and extract insights. The course also covers advanced topics like web scraping, PDF parsing, and automation, providing practical solutions to real-world data challenges.

Course Outcomes: After successful completion of the course, students will be able to

1. CO1: Teaches data wrangling, Python basics, and handling CSV, JSON, and XML for effective data analysis.
2. CO2: Learn to parse Excel and PDF files, convert formats, and understand relational, non-relational, and alternative data storage options for effective data management.
3. CO3: Learn to clean, format, normalize, and script data cleanup for accurate, standardized     datasets.
4. CO4: Learn to import, analyze, visualize, and present data using charts, maps, and interactive tools.
5. CO5: Learn web scraping techniques using LXML, XPath, Selenium, and Scrapy for efficient data extraction and website crawling.

CO-PO Mapping: