Data Wrangling with Python 3.x

Data Wrangling with Python 3.x, available at $29.99, has an average rating of 3.45, with 38 lectures, based on 14 reviews, and has 116 subscribers.

You will learn about Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset. Retrieving data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape. Learn about the amazing data storage places in an industry which are being highly optimized. Perform statistical analysis using in-built Python libraries. Hacks, tips, and techniques that will be invaluable throughout your Data Science career. This course is ideal for individuals who are This course is for Python developers, data analysts, and IT professionals who are keen to explore data analytics/insights to enrich their current personal or professional projects. It is particularly useful for This course is for Python developers, data analysts, and IT professionals who are keen to explore data analytics/insights to enrich their current personal or professional projects.

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Summary

Title: Data Wrangling with Python 3.x

Price: $29.99

Average Rating: 3.45

Number of Lectures: 38

Number of Published Lectures: 38

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset.

Retrieving data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape.

Learn about the amazing data storage places in an industry which are being highly optimized.

Perform statistical analysis using in-built Python libraries.

Hacks, tips, and techniques that will be invaluable throughout your Data Science career.

Who Should Attend

This course is for Python developers, data analysts, and IT professionals who are keen to explore data analytics/insights to enrich their current personal or professional projects.

Target Audiences

This course is for Python developers, data analysts, and IT professionals who are keen to explore data analytics/insights to enrich their current personal or professional projects.

You might be working in an organization, or have your own business, where data is being generated continuously (structured or unstructured) and you are looking to develop your skillset so you can jump into the field of Data Science. This hands-on guide shows programmers how to process information.

In this course, you will gather data, prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and more! This course will equip us with the tools and technologies, also we need to analyze the datasets using Python so that we can confidently jump into the field and enhance our skill set. The best part of this course is the takeaway code templates generated using the real-life dataset.

Towards the end of the course, we will build an intuitive understanding of all the aspects available in Python for Data Wrangling.

About the Author

Jamshaid Sohail is a Data Scientist who is highly passionate about Data Science, Machine learning, Deep Learning, big data, and other related fields. He spends his free time learning more about the field and learning to use its emerging tools and technologies. He is always looking for new ways to share his knowledge with other people and add value to other people’s lives. He has also attended Cambridge University for a summer course in Computer Science where he studied under great professors and would like to impart this knowledge to others. He has extensive experience as a Data Scientist in a US-based company. In short, he would be extremely delighted to educate and share knowledge with, other people.

Course Curriculum

Chapter 1: Gathering and Parsing Data

Lecture 1: The Course Overview

Lecture 2: Installing Anaconda Navigator on Windows/Linux

Lecture 3: Importing and Parsing CSV in Python

Lecture 4: Importing and Parsing JSON in Python

Lecture 5: Scraping Data from Public Web – Part 1

Lecture 6: Scraping Data from Public Web – Part 2

Chapter 2: Working with Data from Excel and PDF Files

Lecture 1: Importing and Parsing Excel Files – Part 1

Lecture 2: Importing and Parsing Excel Files – Part 2

Lecture 3: Manipulating PDF Files in Python – Part 1

Lecture 4: Manipulating PDF Files in Python – Part 2

Chapter 3: Storing Data in Persistent Storage

Lecture 1: Difference between Relational and Non-Relational Databases

Lecture 2: Storing Data in SQLite Databases

Lecture 3: Storing Data in MongoDB

Lecture 4: Storing Data in Elasticsearch

Lecture 5: Comparative Study of Databases for Storage

Chapter 4: Cleaning Structured Data

Lecture 1: The Most Important Step in Data Analysis

Lecture 2: Viewing/Inspecting DataFrames

Lecture 3: Renaming/Adding/Removing the DataFrame Columns

Lecture 4: Dropping Duplicate Rows

Lecture 5: Indexing DataFrame to Retrieve Specific Columns and Rows

Lecture 6: Merging/Concatenating/Joining DataFrames

Lecture 7: Dealing with Missing Values

Chapter 5: More Data Cleaning and Transformation

Lecture 1: Filtering and Sorting of DataFrame

Lecture 2: Encoding/Mapping Existing Values – Part 1

Lecture 3: Encoding/Mapping Existing Values – Part 2

Lecture 4: Rescale/Standardize Column Values

Lecture 5: Common Cleaning Operations

Lecture 6: Exporting Datasets for Future Use

Chapter 6: Performing Statistical Analysis

Lecture 1: Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib)

Lecture 2: Types of Column Names/Features/Attributes in Structured Data

Lecture 3: Split-Apply-Combine (Performing Group By Operation)

Lecture 4: Descriptive Statistics Using Python – Part 1

Lecture 5: Descriptive Statistics Using Python – Part 2

Chapter 7: Let the Visualizations Tell the Story

Lecture 1: Using Visualizations

Lecture 2: Cool Visualization of Real-World Datasets of World Population Evolution

Lecture 3: Visualizations in Python – Part 1

Lecture 4: Visualizations in Python – Part 2

Lecture 5: Exploring an Online Visualization Tool (RAWGraphs)

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 3 votes

4 stars: 3 votes

5 stars: 5 votes

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