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Data Wrangling with Python

  • Course Code: Data Analysis / BI - Data Wrangling with Python
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Data Wrangling with Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. 

Working in a hands-on learning environment, led by our Data Wrangling with Python expert instructor, students will learn about and explore: 

  • Focus on the basics of data wrangling 
  • Study various ways to extract the most out of your data in less time 
  • Boost your learning curve with bonus topics like random data generation and data integrity checks 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use and manipulate complex and simple data structures 
  • Harness the full potential of DataFrames and numpy.array at run time 
  • Perform web scraping with BeautifulSoup4 and html5lib 
  • Execute advanced string search and manipulation with RegEX 
  • Handle outliers and perform data imputation with Pandas 
  • Use descriptive statistics and plotting techniques 
  • Practice data wrangling and modeling using data generation techniques 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  
  • experienced with spreadsheet software who know the basics of Python. 

Course Agenda / Topics 

  1. Introduction to Data Wrangling with Python 
  • Introduction to Data Wrangling with Python 
  • Introduction 
  • Python for Data Wrangling 
  • Lists, Sets, Strings, Tuples, and Dictionaries 
  1. Advanced Data Structures and File Handling 
  • Advanced Data Structures and File Handling 
  • Introduction 
  • Advanced Data Structures 
  • Basic File Operations in Python 
  1. Introduction to NumPy, Pandas, and Matplotlib 
  • Introduction to NumPy, Pandas, and Matplotlib 
  • Introduction 
  • NumPy Arrays 
  • Pandas DataFrames 
  • Statistics and Visualization with NumPy and Pandas 
  1. A Deep Dive into Data Wrangling with Python 
  • A Deep Dive into Data Wrangling with Python 
  • Introduction 
  • Subsetting, Filtering, and Grouping 
  • Detecting Outliers and Handling Missing Values 
  • Concatenating, Merging, and Joining 
  • Useful Methods of Pandas 
  1. Getting Comfortable with Different Kinds of Data Sources 
  • Getting Comfortable with Different Kinds of Data Sources 
  • Introduction 
  • Reading Data from Different Text-Based (and Non-Text-Based) Sources 
  • Introduction to Beautiful Soup 4 and Web Page Parsing 
  1. Learning the Hidden Secrets of Data Wrangling 
  • Learning the Hidden Secrets of Data Wrangling 
  • Introduction 
  • Advanced List Comprehension and the zip Function 
  • Data Formatting 
  • Identify and Clean Outliers 
  • Activity 8: Handling Outliers and Missing Data 
  1. Advanced Web Scraping and Data Gathering 
  • Advanced Web Scraping and Data Gathering 
  • Introduction 
  • The Basics of Web Scraping and the Beautiful Soup Library 
  • Reading Data from XML 
  • Reading Data from an API 
  • Fundamentals of Regular Expressions (RegEx) 
  1. RDBMS and SQL 
  • RDBMS and SQL 
  • Introduction 
  • Refresher of RDBMS and SQL 
  • Using an RDBMS (MySQL/PostgreSQL/SQLite) 
  1. Application of Data Wrangling in Real Life 
  • Application of Data Wrangling in Real Life 
  • Introduction 
  • Applying Your Knowledge to a Real-life Data Wrangling Task 
  • Activity 12: Data Wrangling Task – Fixing UN Data 
  • Activity 13: Data Wrangling Task – Cleaning GDP Data 
  • Activity 14: Data Wrangling Task – Merging UN Data and GDP Data 
  • Activity 15: Data Wrangling Task – Connecting the New Data to the Database 
  • An Extension to Data Wrangling 
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