Pupil outcome: I can use some simple statistics to better understand the values in a set of data. Key learning points: - Univariate data can be statistically analysed using measures of central tendancy. - Data can also be statistically...

Pupil outcome: I can evaluate different statistical measures to draw conclusions about a data set. Key learning points: - A statistical summary involves the mean, median, mode and range. - Each summary gives us different insights into a...

Pupil outcome: I can calculate and compare weighted means. Key learning points: - There may be cases where one value has more weight (importance). - The weighted mean takes this into account. - The percent weight given to each value is...

Pupil outcome: I can construct pie charts from data. Key learning points: - Data presented as a list can be represented as a pie chart. - Data presented in a table can be represented as a pie chart. - A larger data set requires...

Pupil outcome: I can see that the different measures of central tendency offer a summary of a set of data. Key learning points: - The mode is a useful measure of central tendency for certain data sets. - The median is a useful measure of...

Pupil outcome: I can see how certain statistical measures may change as a result of changes in the data. Key learning points: - Changing the value of one data point can affect the mode. - Changing the value of one data point can affect...

Pupil outcome: I can calculate the mean, median, mode and range from a list of data. Key learning points: - The mean, median and mode are measures of central tendency. - The range is a measure of the spread of the data. - Calculating...

Pupil outcome: I can use my understanding of 2D shapes to solve problems. Key learning points: - When problem solving, consider whether any elements are familiar from other areas of maths. - Keep the goal in mind, it is easy to get...

Pupil outcome: I can understand what the mean is measuring, how it is measuring it and calculate the mean from data presented in a list. Key learning points: - The mean can be seen as a redistribution of values such that everything is...

Pupil outcome: I can calculate the median from data presented in a frequency table and different graphs. Key learning points: - The median can be calculated from a line graph. - The median can be calculated from an ungrouped frequency...

In this math video we will be identifying measures of spread. We will understand that our statistical measures of data are mean, median, mode, range and IQR (interquartile range). We will learn that there are two categories of measure -...

Create a boosted tree model to classify stars based on important characteristics.

Explore sentiment analysis using VADER to analyze emotions in text from social media, reviews, and more.

A first look at Midjourney 5 - a state-of-the-art generative image model that can produce photorealistic humans. We breakdown new features in v5 and discus ethical concerns surrounding AI image tech.

Identify tick species using AI and CNNs to reduce risk of tick-borne diseases.

Predict future air quality levels using LSTM models for a location of your choice to mitigate air pollution impact.

Explore how machine learning can predict who survived the Titanic's sinking in this beginner AI science project

Explore how machine learning can diagnose malignant and benign breast tumors in this AI science project

Explore how machine learning can diagnose malignant and benign breast tumors in this AI science project

In this video, we dive into the powerful str_sub() function from the stringr package, a must-know tool for anyone involved in data science, text manipulation, or R programming. The stringr package in R is a comprehensive toolkit designed...

Welcome to our R Programming series, where we dive deep into the power of the Tidyverse! In this video, we'll explore the str_c() function from the stringr package, a crucial tool for text manipulation in R. Whether you're a data...

In this video, we'll explore the versatile str_replace_all() function from the stringr package, an essential tool for anyone working with text or strings in R programming. The stringr package in R is your go-to resource for handling and...

Use plotly to create interactive and 3d plots in R. Plotly integrates with ggplot2. If you're interested in data visualisation and want to create plots and graphs that tell a story with your data then plotly is a great place to start....

The stringr package in R is a comprehensive suite of tools designed to simplify string operations, whether you're parsing text, cleaning data, or preparing strings for analysis. It's a must-have for data scientists, analysts, and anyone...