Data science relies on several important aspects of mathematics. In this course, you'll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems.
Foundations of Data Science
This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.
This lesson serves a primer on elementary algebra.
This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science.
In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.
This talk gives a primer on calculus, emphasizing its role in data science.
This lesson clarifies how calculus relates to optimization in a data science context.
This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.
This lesson serves as a primer on the fundamental concepts underlying probability.
This talk introduces Bayes' theorem, which describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
This lesson recaps why math, in a number of ways, is extremely useful in data science.
This lesson provides an introduction to the lessons in this course that deal with statistics and why they are useful for data science.
In this lesson, users will learn about the importance of exploratory analysis, as well as how statistics enables one to become familiar with and understand one's data.
This lesson goes over graphical data exploration, including motivations for its use as well as practical examples of visualizing data distributions.
In this lesson, users learn about exploratory statistics, and are introduced to various methods for numerical data exploration.
This lesson overview some simple descriptions of statistical data.
This lesson provides instruction on how to infer results from incomplete data.
This lesson covers the basics of hypothesis testing.
This lesson provides instruction on finding parameter values, computing confidence levels, and other various statistical methods employed in data investigation.
In this lesson, statistical methods and tools are described for estimating parameters in your dataset.
This lesson covers how to measure the correspondece between data and model.
In this lesson, you will learn the concepts behind choosing useful variables, as well as various analyses and tools to do so.
This lesson goes over some of the common problems in statistical modeling.
This lesson continues describing some of the common problems in statistical modelling, particularly when it comes to model validation.
You don't have to be a wizard to do statistics!