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Autor Tópico: The Ultimate Pandas Bootcamp: Advanced Python Data Analysis  (Lida 233 vezes)

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The Ultimate Pandas Bootcamp: Advanced Python Data Analysis
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 319 lectures (32 hour, 3 mins) | Size: 9.63 GB
Master the powerful pandas library to analyze, manipulate and visualize data. More than 10 datasets & bonuses included!

What you'll learn

Learn everything there is to know about pandas - from absolute scratch!
Gain a deep and hands-on understanding of pandas data structures.
Transform, clean, filter, groupby, pivot, and otherwise manipulate a any dataset.
Understand related computer science topics like random-number generators, binary operators, memory pointers, and more!
Practice reading data from the web, pickles, Excel files right within pandas.
Discover and learn hundreds of methods, attributes, and techniques to manipulate data in pandas and python.

Requirements

A computer (Windows/Mac/Linux). That's all!
No prior knowledge of python is required.
No prior knowledge of pandas is required.

Description

Welcome to the best resource online for learning and mastering data analysis with pandas and python.

Over 31 hours, 10+ datasets, and 50+ skill challenges, you will gain hands-on mastery of, not only pandas 1.0.x, but also tens of computer science, statistics, and programming concepts.

We will break down, understand, and practice hundreds of methods, attributes, and techniques in pandas and python that will fundamentally change the way you work with data.

In The Ultimate Pandas Bootcamp (2020) you won't be working with outdated versions of pandas, writing repetitive commands on the same boring dataset. Instead, you'll learn pandorable and pythonic solutions to interesting, real-world data problems, while working with many diverse datasets that range from wine servings, video game sales, and SAT scores to stock prices, college salaries and more!

Data analysis is an applied science, which is why in each section, you'll stop and practice what you learn in dedicated skill challenges, followed by detailed solutions where we often consider and compare alternative solutions.

Data analysis is one of the most in-demand skill across all industries and an increasing number of roles. And python is increasingly the language of choice.

Pandas is the wonderful open-source library that is the embodiment of those trends: based on the python programming language, pandas is the de facto data analysis library in the python data science community.

----- Structure & Curriculum -----

Over more than 31 hours, we'll cover everything that pandas has to offer, from manipulating series and dataframes, to merging datasets, handling time series, aggregations, filtering, sorting and much more!

The first four sections of the bootcamp constitute the core curriculum. You'll get acquainted with series and dataframes and develop an in-depth understanding of pandas data structures.

· Series at a Glance

· Series Methods and Handling

· Introducing DataFrames

· DataFrames More In Depth

In the next eight sections, you will dive into more advanced topics and take your pandas skills to another level, learning how to work with multiple datasets, manipulate time series, visualize data, write custom functions to transform data and much more.

· Working With Multiple DataFrames

· Going MultiDimensional

· GroupBy And Aggregates

· Reshaping With Pivots

· Working With Dates And Time

· Regular Expressions And Text Manipulation

· Visualizing Data

· Data Formats And I/O

Pandas and python go hand-in-hand which is why this bootcamp also includes a full-length introduction to the python programming language, to get you up and running writing pythonic code in no time.

This is the ultimate course on one of the most-valuable skills today. I hope you commit to mastering data analysis with pandas.

See you inside!

Who this course is for:

Anyone looking to deeply understand and master pandas
Anyone interested in mastering data analysis with python

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