Packt | Exploratory Data Analysis with Pandas and Python 3.x [FCO]
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Files
| 01.The Course Overview.mp4 | 43.8 MB | ||
| 02.Basic Statistical Measures.mp4 | 77 MB | ||
| 03.Variance and Standard Deviation.mp4 | 8.1 MB | ||
| 04.Visualizing Statistical Measures.mp4 | 25.6 MB | ||
| 05.Calculating Percentiles.mp4 | 9.4 MB | ||
| 06.Quartiles and Box Plots.mp4 | 15.3 MB | ||
| 07.Finding Missing Values.mp4 | 35.8 MB | ||
| 08.Dealing with Missing Values.mp4 | 11.3 MB | ||
| 09.Hands-on with Dealing with Missing Values.mp4 | 54.9 MB | ||
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| 10.Case Study - Missing Data in Titanic Dataset.mp4 | 49.8 MB | ||
| 11.What are Outliers.mp4 | 9 MB | ||
| 12.Using Z-scores to Find Outliers.mp4 | 17 MB | ||
| 13.Modified Z-scores.mp4 | 17.5 MB | ||
| 14.Using IQR to Detect Outliers.mp4 | 24.6 MB | ||
| 15.Types of Variables.mp4 | 77.6 MB | ||
| 16.Introduction to Univariate Analysis.mp4 | 18.6 MB | ||
| 17.Skewness and Kurtosis.mp4 | 7.5 MB | ||
| 18.Univariate Analysis over Olympics Dataset.mp4 | 50.9 MB | ||
| 19.Introduction to Bivariate Analysis.mp4 | 4 MB | ||
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| 20.Correlation Coefficient.mp4 | 8.2 MB | ||
| 21.Scatter Plots and Heatmaps.mp4 | 49.7 MB | ||
| 22.Bivariate Analysis - Titanic Dataset.mp4 | 32.1 MB | ||
| 23.Bivariate Analysis - Video Game Sales.mp4 | 91.2 MB | ||
| 24.Introduction to Multivariate Analysis.mp4 | 5.2 MB | ||
| 25.Multivariate Analysis over Titanic Dataset.mp4 | 38.4 MB | ||
| 26.Multivariate Analysis over Pokemon Dataset.mp4 | 80.6 MB | ||
| 27.Simpson’s Paradox.mp4 | 8.1 MB | ||
| 28.Correlation Is Not Causation.mp4 | 22.2 MB | ||
| 29.Wine Data Analysis - Initial Setup.mp4 | 71 MB | ||
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| code_37350.zip | 2.1 MB | ||
| ▲ 39 total files | |||
Description
By : Mohammed Kashif
Released : 29 Apr 2019 (New Release!)
Torrent Contains : 39 Files, 9 Folders
Course Source : https://www.packtpub.com/application-development/exploratory-data-analysis-pandas-and-python-3x-video
Analyze and visualize your data and make it speak for itself
Video Details
ISBN 9781789959116
Course Length 5 hours 4 minutes
Table of Contents
• Getting Set Up
• Graphical Interfaces, Event-Driven Programs, and Reactive Programming
• Networking, Microservices, and Asynchronous Programming
• Fighting Back Against Bugs, Using Type Annotation, and Unit Testing
• Parallel Concurrency
• Decorators and Non-Type Annotations
• Descriptors and Context Managers
• Distributing Your Software
Learn
• Improve your understanding of descriptive statistics and apply them over a dataset.
• Learn how to deal with missing data and outliers to resolve data inconsistencies.
• Explore various visualization techniques for bivariate and multivariate analysis.
• Enhance your programming skills and master data exploration and visualization in Python.
• Learn multidimensional analysis and reduction techniques.
• Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding
About
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on course shows non-programmers how to process information that’s initially too messy or difficult to access. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently.
This course will take you from Python basics to explore many different types of data. Throughout the course, you will be working with real-world datasets to retrieve insights from data. You'll be exposed to different kinds of data structure and data-related problems. You'll learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
All the code files and related files are placed on the GitHub at this link https://github.com/PacktPublishing/Exploratory-Data-Analysis-with-Pandas-and-Python-3.x
Style and Approach
This is a hands-on course, with each section taking you one step nearer to exploring datasets. Each section is accompanied by visualizations and theory describing the relevant process and finally a case study, where we apply what we have learned in that section to a real-world dataset.
Features:
• Build a solid foundation in data analytics and apply them to real-world datasets
• Each section explores one key measure for exploring any dataset and includes a case study to reinforce the topics you have learned.
• Master the various Data exploration and visualization packages in Python and apply your knowledge to any real-world dataset.
Author
Mohammed Kashif
Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he was working as a Python developer at Qualcomm. He completed his Master's degree in computer science from IIIT Delhi, with specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the higher-education industry.
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