Data Wrangling with Python

Data Wrangling with Python, available at $39.99, has an average rating of 3.3, with 41 lectures, 8 quizzes, based on 28 reviews, and has 121 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. It is particularly useful for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert.

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Summary

Title: Data Wrangling with Python

Price: $39.99

Average Rating: 3.3

Number of Lectures: 41

Number of Quizzes: 8

Number of Published Lectures: 41

Number of Published Quizzes: 8

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert.

Target Audiences

Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert.

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.

About the Author

Samik Sen is currently working with R on Machine Learning. He has done his Ph.D. in Theoretical Physics. He has Tutored Classes for High-Performance Computing postgraduates and Lecturer at International Conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.

Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in Artificial Intelligence and Machine learning from Stanford and MIT.

Shubhadeep Roychowdhury works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying the state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics. He holds a master’s degree in computer science from West Bengal University Of Technology and certifications in machine learning from Stanford.

Course Curriculum

Chapter 1: Introduction to Data Wrangling with Python

Lecture 1: Course Overview

Lecture 2: Lesson Overview

Lecture 3: Importance of Data Wrangling

Lecture 4: Sets

Lecture 5: Tuples and Strings

Lecture 6: Lesson Summary

Chapter 2: Advanced Data Structures and File Handling

Lecture 1: Lesson Overview

Lecture 2: Advanced Data Structures

Lecture 3: Basic File Operations in Python

Lecture 4: Lesson Summary

Chapter 3: Introduction to NumPy, Pandas, and Matplotlib

Lecture 1: Lesson Overview

Lecture 2: NumPy Arrays

Lecture 3: Pandas DataFrames

Lecture 4: Statistics and Visualization with NumPy and Pandas

Lecture 5: Using NumPy & Pandas to Calculate Basic Descriptive Statistics on the DataFrame

Lecture 6: Lesson Summary

Chapter 4: A Deep Dive into Data Wrangling with Python

Lecture 1: Lesson Overview

Lecture 2: Subsetting, Filtering, and Grouping

Lecture 3: Detecting Outliers and Handling Missing Values

Lecture 4: Concatenating, Merging, and Joining

Lecture 5: Useful Methods of Pandas

Lecture 6: Lesson Summary

Chapter 5: Getting Comfortable with Di?erent Kinds of Data Sources

Lecture 1: Lesson Overview

Lecture 2: Reading Data from Di?erent Sources

Lecture 3: Introduction to Beautiful Soup 4 and Web Page Parsing

Lecture 4: Lesson Summary

Chapter 6: Learning the Hidden Secrets of Data Wrangling

Lecture 1: Lesson Overview

Lecture 2: Advanced List Comprehension and the zip Function

Lecture 3: Data Formatting

Lecture 4: Identify and Clean Outliers

Lecture 5: Lesson Summary

Chapter 7: Advanced Web Scraping and Data Gathering

Lecture 1: Lesson Overview

Lecture 2: Introduction to Web Scraping and the BeautifulSoup Library

Lecture 3: Reading Data from XML

Lecture 4: Reading Data from an API

Lecture 5: Fundamentals of Regular Expressions (RegEx)

Lecture 6: Lesson Summary

Chapter 8: RDBMS and SQL

Lecture 1: Lesson Overview

Lecture 2: Refresher of RDBMS and SQL

Lecture 3: Using an RDBMS (MySQL/PostgreSQL/SQLite)

Lecture 4: Lesson Summary

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 8 votes

4 stars: 9 votes

5 stars: 6 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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