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Web Scraping in R

Given the following ordered list (using the ol element), which is stored as list_raw_html:

Introduction to Deep Learning with Keras

You’re going to build a simple neural network to get a feeling of how quickly it is to accomplish this in Keras.

Introduction to the OpenAI API

Software applications, web browser experiences, and even whole products are being built on top of the OpenAI API. In this exercise, you’ll be able to explore an application…

Introduction to Shell

An operating system like Windows, Linux, or Mac OS is a special kind of program. It controls the computer’s processor, hard drive, and network connection, but its most…

Python Data Science Toolbox (Part 1)

In the video, you learned of another standard Python datatype, strings. Recall that these represent textual data. To assign the string ‘DataCamp’ to a variable company, you…

Data Manipulation with pandas

When you get a new DataFrame to work with, the first thing you need to do is explore it and see what it contains. There are several useful methods and attributes for this.

Intermediate Python

With matplotlib, you can create a bunch of different plots in Python. The most basic plot is the line plot. A general recipe is given here.

Introduction to Python

Intermediate Regression in R

In Introduction to Regression in R, you learned to fit linear regression models with a single explanatory variable. In many cases, using only one explanatory variable limits…

Survival Analysis R

In this course, we will frequently use the GBSG2 dataset. This dataset contains information on breast cancer patients and their survival. In this exercise, we will take a…

Foundations of Probability in R

In these exercises, you’ll practice using the rbinom() function, which generates random “flips” that are either 1 (“heads”) or 0 (“tails”).

Introduction to Statistics with R

In this chapter, you’ll be working with the 2018 Food Carbon Footprint Index from nu3. The food_consumption dataset contains information about the kilograms of food consumed…

Functions in R

One way to make your code more readable is to be careful about the order you pass arguments when you call functions, and whether you pass the arguments by position or by name.

Handling Missing Data with Imputations in R

Missing data is a common problem and dealing with it appropriately is extremely important. Ignoring the missing data points or filling them incorrectly may cause the models…

Anomaly Detection in R

In this exercise, you’ll explore the river dataset which will be used throughout this chapter to illustrate the use of common anomaly detection techniques. The river data is…

Sampling in R

Sampling is an important technique in your statistical arsenal. It isn’t always appropriate though—you need to know when to use it and when to work with the whole dataset.

Introduction to advanced dimensionality reduction

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analysis

You will use the MNIST dataset in several exercises through the course. Let’s do some data exploration to gain a better understanding. Remember that the MNIST dataset…

Census data in r with tidycensus

tidycensus is an R package designed to return data from the US Census Bureau ready for use within the Tidyverse.

Modeling with tidymodels in R

The rsample package is designed to create training and test datasets. Creating a test dataset is important for estimating how a trained model will likely perform on new…

Communicating with Data in the Tidyverse

In the video, you have learned that the inner_join() function of dplyr needs to be given a “key” on which two data frames are joined. Actually, multiple keys that need to…

The reduction in weekly working hours in Europe

Looking at the development between 1996 and 2006

The International Labour Organization (ILO) has many data sets on working conditions. For example, one can look at how weekly working hours have been decreasing in many…

Writing Efficient R Code

One of the relatively easy optimizations available is to use an up-to-date version of R. In general, R is very conservative, so upgrading doesn’t break existing code.…

Scalable Data Processing in R

If you are processing all elements of two data sets, and one data set is bigger, then the bigger data set will take longer to process. However, it’s important to realize…

Multiple Linear and Logistic Regression in R

We use the lm() function to fit linear models to data. In this case, we want to understand how the price of MarioKart games sold at auction varies as a function of not only…

Network analysis in R

Here you will learn how to create an igraph ‘object’ from data stored in an edgelist. The data are friendships in a group of students. You will also learn how to make a…

String manipulation with stringr in r

Let’s get started by entering some strings in R. In t he video you saw that you use quotes to tell R to interpret something as a string. Both double quotes (“) and single…

ML with tree based models in r

Let’s get started and build our first classification tree. A classification tree is a decision tree that performs a classification (vs regression) task. You will train a…