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TORRENT DETAILS
[FreeCoursesOnline.Me] [Packt] Data Wrangling With Python 3.x [FCO]
TORRENT SUMMARY
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Learn the data life cycle—from acquisition to processing to analysis—in Python
Video Details
ISBN 9781789956597
Course Length 3 hour 35 minutes
Table of Contents
• GATHERING AND PARSING DATA
• WORKING WITH DATA FROM EXCEL AND PDF FILES
• STORING DATA IN PERSISTENT STORAGE
• CLEANING STRUCTURED DATA
• MORE DATA CLEANING AND TRANSFORMATION
• PERFORMING STATISTICAL ANALYSIS
• LET THE VISUALIZATIONS TELL THE STORY
Video Description
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.
All codes and supporting files are placed on GitHub at this link: https://github.com/PacktPublishing/-Data-Wrangling-with-Python-3.x
Style and Approach
This hands-on course demonstrates concepts via slides, to make sure they're explained in simple ways. Throughout the course, we will be using datasets downloaded from the UCI Machine Learning Repository and various sources on the public web for conceptual practical intuition.
In every section, we will be looking into the theoretical concepts related to the section and then jump on practical examples using the number one IDE for Data Science i.e. Spyder IDE.
Each line of code will be explained in detail and the output will be instantly shown in the variable explorer of the IDE.
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.
Authors
Jamshaid Sohail
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.
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FILE LIST
Filename
Size
01.Gathering and Parsing Data/0101.The Course Overview.mp4
30.8 MB
01.Gathering and Parsing Data/0102.Installing Anaconda Navigator on WindowsLinux.mp4
53.5 MB
01.Gathering and Parsing Data/0103.Importing and Parsing CSV in Python.mp4
28.5 MB
01.Gathering and Parsing Data/0104.Importing and Parsing JSON in Python.mp4
22.8 MB
01.Gathering and Parsing Data/0105.Scraping Data from Public Web – Part 1.mp4
20.9 MB
01.Gathering and Parsing Data/0106.Scraping Data from Public Web – Part 2.mp4
49.4 MB
02.Working with Data from Excel and PDF Files/0201.Importing and Parsing Excel Files – Part 1.mp4
20.3 MB
02.Working with Data from Excel and PDF Files/0202.Importing and Parsing Excel Files – Part 2.mp4
205.7 MB
02.Working with Data from Excel and PDF Files/0203.Manipulating PDF Files in Python – Part 1.mp4
125.3 MB
02.Working with Data from Excel and PDF Files/0204.Manipulating PDF Files in Python – Part 2.mp4
126.7 MB
03.Storing Data in Persistent Storage/0301.Difference between Relational and Non-Relational Databases.mp4
14.7 MB
03.Storing Data in Persistent Storage/0302.Storing Data in SQLite Databases.mp4
33.4 MB
03.Storing Data in Persistent Storage/0303.Storing Data in MongoDB.mp4
25.2 MB
03.Storing Data in Persistent Storage/0304.Storing Data in Elasticsearch.mp4
27.5 MB
03.Storing Data in Persistent Storage/0305.Comparative Study of Databases for Storage.mp4
5 MB
04.Cleaning Structured Data/0401.The Most Important Step in Data Analysis.mp4
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