How to get started with data visualization

Um excelente texto sobre como se tornar um bom artista de visualizações de dados

Sometimes, I get emails asking me “how to get started in data visualization.” Maybe you’re one of the people wondering about that. Maybe you found a book full of data vis in a bookstore, followed somebody who makes data vis on social media, noticed that you love playing with the “charts” feature in Excel, or just really like these data visualizations that newsrooms are creating.

And now you want to create them yourself but don’t know where to get started.

This article tries to give some very practical advice on your next steps. If you ask ten people for that advice, you’ll likely get ten different answers. This is mine:

1 Find out what you like about data visualizations
Don’t worry too much about data or tools
Start to visualize data
Get better at visualizing data

It’s based on the experiences I’ve had and what’s worked for me. I hope some of these ideas will help you to find your own path.

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Lotteries, Covid, and Communicating Risk

Uma boa tentativa de explicar o conceito de risco de forma simples

Wayne Oldford

May 1, 2022

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Two sides of the same coin?

A few years ago, I was the “go to guy” at the University of Waterloo, asked to speak to local media, whenever a lottery jackpot got stupendously large (and the news cycle got exceedingly slow). My purpose was to relate to their audience the size of the chance of winning in a way that was quick yet comprehensible, which I did with some success on local radio and television stations.

Inevitably, though, the next day I would hear back of listener disappointment – that some of the fun of purchasing a ticket had been removed. Joy came from anticipating winning the prize and my exposition killed that for many, by them having gained an appreciation of the chance of actually winning.

I felt a little bit bad about this. I wanted people to understand the probabilities but I didn’t want to be a kill joy.

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Linux Commands Cheat Sheet

Uma boa cábula dos comandos Linux principais.

Hitesh J Last Updated : 12/13/2021

There are hundreds – possibly thousands –  commands available in Linux. Remembering every command is not possible and it can be quite daunting for a novice user. The good news is that you don’t need to remember each command. Only a very small subset of those commands are used on a day-to-day basis.

This cheat sheet offers a set of commands that you can use for quick reference. I have prepared this Linux Commands Cheat Sheet as quick reference for both experienced and basic users.

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Data Visualization In Excel Using Python

Um pequeno tutorial para utilizar ExcelWriter em Python

Using ExcelWriter for Creating Visualizations in Excel by Python Code

Himanshu Sharma4 days ago·3 min read

Excel is widely used for data analysis and has a lot of functionalities for analyzing, manipulating, visualizing, etc. Using excel should be one of the main skills required for a Data Analyst, Product Analyst, and Business Analyst. It helps in understanding the data and how we can use it for generating useful insights.

Python is also widely used for Data Analysis purposes and also overcomes the drawbacks of Excel. With a little knowledge of Python, we can enhance our Data Analysis skills and also generate more useful insights.

Combining both Excel and Python can be fruitful and makes Data Analysis more interesting. ExcelWriter is used for performing different Excel operations using Python. It provides a wide variety of functionalities like creating an Excel, writing data into an Excel sheet, creating visualizations in Excel, etc.

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A Better Way for Data Preprocessing: Pandas Pipe

Um pequeno tutorial sobre como começar a usar o pipe

Efficient, organized, and elegant.

Soner YıldırımJul 31·4 min read

Real-life data is usually messy. It requires a lot of preprocessing to be ready for use. Pandas being one of the most-widely used data analysis and manipulation libraries offers several functions to preprocess the raw data.

In this article, we will focus on one particular function that organizes multiple preprocessing operations into a single one: the pipe function.

When it comes to software tools and packages, I learn best by working through examples. I keep this in mind when creating content. I will do the same in this article.

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A Beginner’s Guide to learn web scraping with python!

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Boa descrição de web scraping com Python

Web Scraping with Python

Imagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. 

In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics:

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Extracting Seasonality and Trend from Data: Decomposition Using R

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Uma excelente descrição da decomposição clássica com Python e R.

Time series decomposition works by splitting a time series into three components: seasonality, trends and random fluctiation. To show how this works, we will study the decompose( ) and STL( ) functions in the R language.

Understanding Decomposition

Decompose One Time Series into Multiple Series

Time series decomposition is a mathematical procedure which transforms a time series into multiple different time series. The original time series is often split into 3 component series:

  • Seasonal: Patterns that repeat with a fixed period of time. For example, a website might receive more visits during weekends; this would produce data with a seasonality of 7 days.
  • Trend: The underlying trend of the metrics. A website increasing in popularity should show a general trend that goes up.
  • Random: Also call “noise”, “irregular” or “remainder,” this is the residuals of the original time series after the seasonal and trend series are removed.

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The 5 Computer Vision Techniques

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Boa introdução ao tema da visão por computador

The 5 Computer Vision Techniques That Will Change How You See The World

Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science). As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.

So what is Computer Vision?

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Free Hadoop Tutorial: Master BigData

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BigData is the latest buzzword in the IT Industry. Apache’s Hadoop is a leading Big Data platform used by IT giants Yahoo, Facebook & Google. This course is geared to make a Hadoop Expert.

What should I know?


This is an absolute beginner guide to Hadoop. But knowledge of 1) Java 2) Linux will help

Syllabus

Tutorial Introduction to BIG DATA: Types, Characteristics & Benefits
Tutorial Hadoop Tutorial: Features, Components, Cluster & Topology
Tutorial Hadoop Setup Tutorial – Installation & Configuration
Tutorial HDFS Tutorial: Read & Write Commands using Java API
Tutorial What is MapReduce? How it Works – Hadoop MapReduce Tutorial
Tutorial Hadoop & Mapreduce Examples: Create your First Program
Tutorial Hadoop MapReduce Tutorial: Counters & Joins with Example
Tutorial What is Sqoop? What is FLUME – Hadoop Tutorial
Tutorial Sqoop vs Flume vs HDFS in Hadoop
Tutorial Create Your First FLUME Program – Beginner’s Tutorial
Tutorial Hadoop PIG Tutorial: Introduction, Installation & Example
Tutorial Learn OOZIE in 5 Minutes – Hadoop Tutorial
Tutorial Big Data Testing: Functional & Performance
Tutorial Hadoop & MapReduce Interview Questions & Answers

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Controlling for test variables

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alguns apontamentos sobre testes com vars de controlo

3.2  Three (or more) variables

[Page last updated 3 August 2016]

Introducing a third variable. Controlling for test variables. Elaboration.

Logical model is X Y . T (the effect of X on Y controlling for T) where:

Y = Dependent variable
X = Independent variable
T = Test variable(s)

3.2.1 Elaboration

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