<h2>Introduction to Instrumental Variables</h2><p>This chapter will introduce you to Instrumental Variables (IV) analysis, a tricky but powerful method to find causality through indirect inference</p><h2>Instrumental Variables in Practice</h2><p>This chapter covers more details about Instrumental Variables Analysis, and allows you to practice instrumental variables for yourself</p><h2>Regression Discontinuity Design</h2><p>This chapter introduces you to Regression Discontinuity Design (RDD), which is often considered a more flexible relative of Instrumental Variables</p><h2>The Local Average Treatment Effect of IV</h2><p>This chapter explains the treatment effect we are estimating with the instrumental variables method - LATE, or the Local Average Treatment Effect</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-instrumental-variables-rdd

A look at using Instrumental Variables and Regression Discontinuity to estimate causal effects

Causal Inference with R - Instrumental Variables & RDD

<h2>Regressions 1: Introduction to Regression As Causality</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 2: Using Regression to Estimate Causal Effects</h2><p>This chapter will introduce you to using regression analysis to find causal effects</p><h2>Regressions 3: Introduction to Matching Methods</h2><p>This chapter will introduce you to using matching methods to find causal effects</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-regression

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc. 

Causal Inference with R - Regression

<h2>Introduction</h2><p>We introduce how experiments help us find causal connections, and take a quick look at controlled experiments.</p><h2>Randomized Experiments & Statistical Inference</h2><p>These videos and exercises introduce randomized experiments, and explain why we need statistical inference in experiments.</p><h2>Practice with Published Experiments - The Oregon Health Insurance Experiment</h2><p>These videos and exercises offer practice in skills used in analyzing experimental data.</p><h2>Common Issues in Experiments</h2><p>These videos and exercises discuss common issues in carrying out experiments.</p><h2>Dealing with Noncompliance in Experiments</h2><p>These videos and exercises discuss the issue of noncompliance in experiments.</p><h2>Long-term Average Treatment Effects</h2><p>These videos and exercises discuss how to compute and interpret long-term causal effects.</p><h2>Natural Experiments</h2><p>These videos and exercises introduce natural experiments, sometimes called quasi-experiments.</p><h2>Bounds Analysis & Putting It All Together</h2><p>This chapter discusses how to form bounds on causal effects in the presence of experimental noncompliance, and we wrap up the course with a bigger exercise to review what we have learned.</p>

https://www.datacamp.com/community/open-courses/causal-inference-with-r-experiments

Welcome to the Causal Inference with R – Experiments, the 2nd of 7 courses on causal inference concepts and methods created by Duke University with support from eBay, Inc.

Causal Inference with R - Experiments

<h2>Conceptos básicos de Python</h2><p>Una introducción a los conceptos básicos de Python. Aprende como utilizar Python de forma interactiva y a tráves de una consola. Crea tus primeras variables y familiarícese con los tipos de datos básicos en Python.</p><h2>Listas de Python</h2><p>Aprende a almacenar, acceder y manipular datos en una lista: el primer paso hacia un trabajo eficiente con gran cantidad de datos.</p><h2>Funciones y paquetes</h2><p>Para aprovechar el código que los desarrolladores de Python han escrito, aprenderás sobre funciones, métodos y paquetes. ¡Esto te ayudará a reducir la cantidad de código que necesitas para resolver problemas!</p><h2>NumPy</h2><p>NumPy es un poderoso paquete de Python para hacer ciencia de datos eficientemente. Aprende como trabajar con arrays de NumPy, una alternativa más rápida y poderosa a las listas, y toma tus primeros pasos en la exploración de datos.</p>

https://www.datacamp.com/community/open-courses/introduccion-a-python

Aprenderás acerca de poderosas maneras de almacenar y manipular datos, así como también geniales herramientas de ciencia de datos para comenzar tus propios análisis. 

Introducción a Python

<h2>Introduction to Bayesian thinking</h2><p>This chapter introduces the idea of discrete probability models and Bayesian learning. You'll express your opinion about plausible models by defining a prior probability distribution, you'll observe new information, and then, you'll update your opinion about the models by applying Bayes' theorem.</p><h2>Learning about a binomial probability</h2><p>This chapter describes learning about a population proportion using discrete and continuous models. You'll use a beta curve to represent prior opinion about the proportion, take a sample and observe the number of successes and failures, and construct a beta posterior curve that combines both the information in the prior and in the sample. You'll then use the beta posterior curve to draw inferences about the population proportion.</p><h2>Learning about a normal mean</h2><p>This chapter introduces Bayesian learning about a population mean. You'll sample from a normal population with an unknown mean and a known standard deviation,  construct a normal prior to reflect your opinion about the location of the mean before sampling, and see that the posterior distribution also has a normal form with updated values of the mean and standard deviation. You'll also get more practice  drawing inferences from the posterior distribution, only this time, about a population mean.</p><h2>Bayesian comparisons</h2><p>Suppose you're interested in comparing proportions from two populations. You take a random sample from each population and you want to learn about the difference in proportions. This chapter will illustrate the use of discrete and continuous priors to do this kind of inference. You'll use a Bayesian regression approach to learn about a mean or the difference in means when the sampling standard deviation is unknown.</p>

https://www.datacamp.com/community/open-courses/beginning-bayes-in-r

Discover Bayesian statistics with R: represent your beliefs with the language of probability, collect some data and update your beliefs based on the evidence contained in the data.

Beginning Bayes in R

<h2>建立與探索資料框</h2><p>在學習資料框的整理技巧之前，我們得先在 R 語言的工作環境中建立出可供我們練習的資料框才行，在本章我們首先會複習在 R 語言導論中學過的內容，像是建立一個資料框以及一些快速探索資料框的好用函數，一場爭奪 One Piece 的海上冒險故事！</p><h2>欄與列的相關技巧</h2><p>實務上我們很常會有新增變數、刪除變數或者篩選觀測值 ... 等的需求，在資料框的結構中，其實就是針對欄或者列進行整理，我們將在本章節學習這些技巧與概念，一場爭奪 One Piece 的海上冒險故事！</p><h2>生成衍生變數</h2><p>從資料庫查詢得到的結果或者既有資料的變數有時候並不能滿足我們的分析需求，這時我們會需要生成衍生變數，可能是將類別型變數重新歸類、將數值型變數歸類為類別型變數或者針對數值型變數作計算 ... 等，我們將在本章節學習這些技巧與概念，一場爭奪 One Piece 的海上冒險故事！</p><h2>欄位聚合與資料轉置</h2><p>分析資料框常需要對某些欄位進行摘要統計，可能是求取總和或者平均值，讓你對變數的分佈更為清楚；有時候甚至你需要依據某個類別變數，分別計算不同類別的摘要統計，一如你在 Excel 中使用樞紐分析表一般；有經驗的資料科學家還必須熟悉長資料框與寬資料框的互相轉換，視需求靈活調整資料結構。我們將在本章節學習這些技巧與概念，一場爭奪 One Piece 的海上冒險故事！</p><h2>聯結資料框</h2><p>在一個資料分析專案中，資料可能散落在多個資料框中，因此對於資料框的聯結你必須要有清晰的認知，假如你使用過關聯式資料庫進行資料查詢，你在這章節的練習中會覺得駕輕就熟；假如你沒有使用過也不要緊，我們將在本章節學習這些技巧與概念，一場爭奪 One Piece 的海上冒險故事！</p>

https://www.datacamp.com/community/open-courses/資料框整理技巧

在 R 語言導論中我們介紹了一種資料類型叫資料框，實務中我們面對的多數資料分析專案都是將原始資料讀為一個資料框再開始進行後續工作。

資料框整理技巧

<h2>Retrieving Data From The Database</h2><p>This chapter provides a brief introduction to working with relational databases in a marketing context. You'll learn about their structure,  how to talk about them using database lingo, and how to begin an  analysis by using simple SQL commands to select and summarize columns  from database tables.</p><h2>Filter Data And Limit Your Output</h2><p>In this chapter, you'll learn how to filter tables for rows that satisfy your criteria of interest with the help of basic comparison operators and combinations of multiple criteria.</p><h2>What's Next?</h2><p>Besides selecting columns and filtering rows, there is much more that you can do with SQL! This chapter will present you with some examples that will motivate you to continue to develop this newly gained skill!</p>

https://www.datacamp.com/community/open-courses/sql-tutorial-for-marketers

The best marketers and growth hackers are data-driven. Learn how to use Structured Query Language (SQL) to query a database containing bank clients and marketing campaigns data.

SQL Tutorial for Marketers

<h2>Introduction</h2><p>Introduction to data.world</p><h2>Working with datasets</h2><p>Working with data.world datasets</p><h2>Working with multiple datasets</h2><p>Working with multiple datasets</p><h2>Querying with data.world</h2><p>Querying with data.world</p><h2>Wrap up & next steps</h2><p>Setup your own data.world account and apply what you've learned and more!</p>

https://www.datacamp.com/community/open-courses/intro-to-dataworld-in-python

data.world allows data people to discover and share cool data. In this course, you'll learn to use the data.world Python SDK to pull data and metadata into your environment.

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