<h2>The Basics of R</h2><p>This lab is about teaching enough R to start using it for statistical analyses.</p><h2>Introduction to R continued</h2><p>This lab continues with an introduction to R</p><h2>Comparing two groups</h2><p>The current lab treats differences between two independent groups, paired groups and proportions</p><h2>Categorical Association</h2><p>Investigating associations between categorical variables.</p><h2>Simple Regression</h2><p>Predicting a response variable one predictor variable.</p><h2>Multiple Regression</h2><p>Predicting a response variable with more than one predictor variable.</p><h2>Quantitative associations: ANOVA</h2><p>This lab treats the statistical methodology of the analysis of variance</p><h2>Nonparametric tests</h2><p>This lab will treat testing methods that can be employed when the assumptions of other methods are not met</p>

https://www.datacamp.com/community/open-courses/inferential-statistics

This course complements the course on Inferential Statistics at Coursera.

Inferential Statistics

<h2>Tidy Data in Python</h2><p>It is often said that data scientists spend only 20% of their time analyzing their data, and 80% of time cleaning it. Indeed, maintaining a tidy, easy-to-use dataset is crucial in our age of big data. In the paper Tidy Data, veteran statistician Hadley Wickham gives definitions of tidy and messy data so that all data scientists can keep their work organized. In this mini-course, you'll learn to transform messy datasets into tidy datasets using the pandas package in python. Let's get started!</p>

https://www.datacamp.com/community/open-courses/tidy-data-in-python-mini-course

Most of the world's data aren't sorted cleanly nor organized. In this course, you'll learn about the standards for tidy data and you'll create tidy data sets from messy ones.

Tidy Data in Python Mini-Course

<h2>Introduction to Python for Data Analysis</h2><p>This chapter will get you started with Python for Data Analysis. We will cover the reasons to learn Data Science using Python, provide an overview of the Python ecosystem and get you to write your first code in Python!</p><h2>Python Libraries and data structures</h2><p>In this chapter, we will introduce some of the most common data structures in Python to you and take you through some of the libraries we commonly use in data analysis.</p><h2>Exploratory analysis in Python using Pandas</h2><p>We start with the first step of data analysis - the exploratory data analysis.</p><h2>Data Munging in Python using Pandas</h2><p>Pandas is at the heart of data analysis in Python. This chapter gets you started with Data Munging in Python using Pandas</p><h2>Building a Predictive model in Python</h2><p>We build our predictive models and make submissions to the AV DataHack platform in this section.</p><h2>Expert advice to improve model performance</h2><p>This chapter will help to understand the approach of data science experts, "How they do approach a challenge?", "How to select a right algorithm?", "How to combine outputs of multiple algorithms?" and "How to select the right value of model parameter also known as parameter tuning?".</p>

https://www.datacamp.com/community/open-courses/introduction-to-python-machine-learning-with-analytics-vidhya-hackathons

This course introduces you to data science concepts, data exploration, and preparation in Python to prepare you to participate in machine learning competitions on Analytics Vidhya.

Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)

<h2>基本介紹</h2><p>本章節是我們與 R 的第一次接觸，我們將學習如何使用 R 做數學運算以及如何宣告變數，也會學習 R 的基本資料類別，讓我們趕快開始吧！</p><h2>向量</h2><p>在第二章的課程中，我們要去拉斯維加斯一趟學習如何在 R 使用向量分析博弈的結果！完成本章節後，我們不僅會變成技巧高超的賭客，還將學會如何宣告向量、命名向量、從向量選取元素以及比較不同的向量。</p><h2>矩陣</h2><p>在本章節我們將學習如何在 R 使用矩陣，包括宣告以及運算，我們將要使用矩陣來分析星際大戰的票房記錄，願原力與我們同在！</p><h2>因素向量</h2><p>資料常會有特定幾個類別，例如性別是男或女。在 R 語言，類別型的資料以因素向量的方式儲存，因素向量對資料分析是很重要的，讓我們好好學習如何建立、篩選與比較因素向量！</p><h2>資料框</h2><p>實務上我們面對的多數資料都是儲存為資料框的格式，學完本章節之後，我們將可以熟練地建立資料框、選擇資料框中有興趣的部分以及根據某一個變數排序資料框。</p><h2>清單</h2><p>清單與向量最大的不同是清單可以儲存不同類型的資料格式，就像我們的工作項目清單一樣，本章節我們將學會如何建立、命名以及選擇清單中的元素。</p>

https://www.datacamp.com/community/open-courses/r-語言導論

我們將在 R 語言導論學習這個熱門的開源軟體，包含因素向量、清單與資料框等資料類別。

R 語言導論

<h2>Importing data and star review modification</h2><p>Weighting star reviews based on users who review more restaurants</p><h2>Generating authentic star reviews</h2><p>Modifying restaurant reviews to only the native Indian users</p>

https://www.datacamp.com/community/open-courses/r-yelp-and-the-search-for-good-indian-food

Are all Yelp restaurant reviews created equal? You will learn the techniques to “cut through the noise” and deliver useful metrics for finding a great place to eat on Friday night!

R, Yelp and the Search for Good Indian Food

<h2>Introduction to Course and RStudio</h2><p>This chapter contains videos that walks you through what a programming language is, and how to install RStudio on your computer.</p><h2>Getting Started With Data Structures in R</h2><p>In this chapter, you'll be introduced to some basic functions and data structures.</p><h2>Installing Packages, Importing and Cleaning Data</h2><p>In this chapter, you'll be introduced to some of the most popular packages in R, learn how to import your dataset, and some techniques to clean your dataset!</p><h2>Data Visualization Techniques</h2><p>In this chapter, you'll learn about the syntax to make data visualizations using some of R's most popular graphics packages.</p><h2>Data Manipulation</h2><p>In this chapter, you'll learn how to manipulate your data in order to not only make better graphs, but also making your data graphable in the first place.</p><h2>Final Project</h2><p>In this last chapter, you will combine everything you've learned in this course to work on a final project using the volcanoes dataset.</p>

https://www.datacamp.com/community/open-courses/r-for-the-intimidated

This course will introduce you to the very basics of R, from running simple functions to manipulating data sets and creating graphs.

R for the Intimidated

<h2>Getting Started with Python</h2><p>In this chapter we will go trough the essential steps that you will need to take before beginning to build predictive models.</p><h2>Predicting with Decision Trees</h2><p>After making your first predictions in the previous chapter, it's time to bring you to the next level. In chapter 2 you</p><h2>Improving your predictions through Random Forests</h2><p>What techniques can you use to improve your predictions even more? One possible way is by making use of the machine learning method Random Forest. Namely, a forest is just a collection of trees...</p>

https://www.datacamp.com/community/open-courses/kaggle-python-tutorial-on-machine-learning

Always wanted to compete in a Kaggle competition but not sure you have the right skill set? In this course, you'll learn to predict survival rate for Kaggle's Titanic competition.

Kaggle Python Tutorial on Machine Learning

<h2>Analyzing the 2013 American Community Survey</h2><p>In this first chapter you will use data from the <a href="https://www.kaggle.com/census/2013-american-community-survey" target="_blank" >2013 American Community Survey</a> to figure out whether it makes sense to pursue a PhD or not. The end result of this chapter will be <a href="https://www.kaggle.com/martijn/d/census/2013-american-community-survey/data-exploration-with-kaggle" target="_blank">your own Kaggle script</a> that you can add to your Kaggle account.</p><h2>Pigeons, Chopsticks, Spanish Silver and Marijuana</h2><p>Analyze pigeon racing data, discover the optimal size for your chopsticks, plot Spanish silver production in the 18th century and map the average price of marijuana in the different states of the US.</p>

https://www.datacamp.com/community/open-courses/data-exploration-with-kaggle-scripts

In this course, you'll begin learning the art and science of data exploration. You'll also become familiar with some of the most widely used visualization packages in data science.

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