Statistics Online Courses & Certifications
Statistics is a branch of mathematics that involves collecting, organizing, analyzing, interpreting, presenting, and drawing conclusions from data. It plays a crucial role in scientific research, decision-making, and understanding the world around us. The primary goal of statistics is to provide methods and tools for dealing with uncertainty and variability in data.
Statistics is widely used in various fields, including scientific research, social sciences, economics, finance, engineering, medicine, environmental studies, and business analytics. It provides essential tools for making data-driven decisions, testing hypotheses, and understanding the underlying patterns and relationships in data.
By applying statistical methods correctly, researchers and decision-makers can gain insights, make informed choices, and improve processes and outcomes based on evidence and analysis rather than intuition or guesswork.
This course is a great introduction to both fundamental statistics concepts and the R programming language. R is used by professionals in the Data Analysis and Data Science fields as part of their daily work.
Data scientists use math as well as coding to create and understand analytics. Whether you want to understand the language of analytics, produce your own analyses, or even build the skills to do machine learning, this Skill Path targets the fundamental math you will need. Learn probability, statistics, linear algebra, and calculus as they are applied to real-world data analysis!
A goal of many analytics projects is to answer “how much did factor x affect the outcome?”, otherwise known as the treatment effect. But it can be very hard to do that with real data because it’s often incomplete. This course will introduce you to two techniques to fix that problem: Regression Discontinuity Design and Instrumental Variables.
Histograms are more than just bar charts. This course introduces you to the concepts of bins and breaks, and describing distributions visually while giving you hands on practice in creating your own histograms.
Data scientists use statistics to produce analyses, recommendations, and even machine learning models. In this Skill Path, you will use Python to summarize datasets, investigate correlations, run hypothesis tests, and build regression models.
Boxplots are the most compact way to visually represent descriptive statistics about a variable. Learn how to make them in this course.
Use the Potential Outcomes Framework to estimate what we cannot measure.
Learn the fundamentals of probability and how to quantify and visualize uncertainty.