EdX

Statistics 2 Part 1: Probability and Distribution Theory (edX)

Offered by The London School of Economics and Political Science, LSE,
Statistics 2 Part 1: Probability and Distribution Theory (edX)

The third in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

Statistics 2 Part 1 is a self-paced course from LSE which aims to develop your knowledge of elementary statistical theory, particularly relating to the concepts, methods and techniques of measurement and hypothesis testing that were introduced in Statistics 1, Parts 1 and 2. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 1, Probability and Distribution Theory, covers the following topics:

  • Probability theory I
  • Probability theory II
  • Random variables
  • Common distributions of random variables
  • Multivariate random variables

There is an emphasis on topics that relate to econometrics, finance and quantitative social science. Concepts and methods that provide the foundations for more specialised undergraduate-level courses in statistics and econometrics are introduced.
This course is part of the Statistics Fundamentals MicroBachelors Program.

Prerequisites:
Statistics 2 part 1 assumes no prior knowledge of statistics. Although there are no formal prerequisites for this course, it is strongly recommended to study the LSE statistics courses in order, given the cumulative nature of the subject matter. Statistics 1, Parts 1 and 2, provide a solid foundation for this course.

What you'll learn
By the end of this course, you will:

  • Have developed key ideas from Statistics 1 that are accessible to a student with a moderate mathematical competence
  • apply and be competent users of standard statistical operators
  • be able to recall a variety of well-known distributions and their respective moments

Syllabus

  • Probability theory I
  • Probability theory II
  • Random variables
  • Common distributions of random variables
  • Multivariate random variables
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Algèbre Linéaire (Partie 2) (edX) EdX
École Polytechnique Fédérale de Lausanne,EPFLx

Algèbre Linéaire (Partie 2) (edX)

Engineering Sci: Mathematics

Un MOOC francophone d'algèbre linéaire accessible à tous, enseigné de manière rigoureuse et ne nécessitant aucun prérequis. Vous voulez apprendre l'algèbre linéaire, un précieux outil complémentaire à vos connaissances acquises durant vos études en économie, ingénierie, physique, ou statistique? Ou simplement pour la beauté de la matière? Alors ce cours est fait pour vous!

Self Paced
Self-Paced
Math Linear Algebra Algèbre Linéaire
Details
Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD (edX) EdX
Georgia Institute of Technology,GTx

Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD (edX)

Sci: Mathematics

This course takes you through roughly five weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. In the first part of this course you will explore methods to compute an approximate solution to an inconsistent system of equations that have no solutions. Our overall approach is to center our algorithms on the concept of distance.

Self Paced
Self-Paced
Math Linear Algebra Mathematics
Details
Data Science: Inference and Modeling (edX) EdX
HarvardX,Harvard University

Data Science: Inference and Modeling (edX)

Statistics & Data Analysis Data Science

Learn inference and modeling, two of the most widely used statistical tools in data analysis. Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting.

Self Paced
Self-Paced
Statistics Inference Modeling
Details
Aplicaciones de la Teoría de Grafos a la vida real (I) (edX) EdX
Universitat Politècnica de València,UPValenciaX

Aplicaciones de la Teoría de Grafos a la vida real (I) (edX)

Sci: Mathematics

Aprenderemos a modelizar problemas del mundo real mediante su representación con grafos y a resolverlos mediante sus algoritmos asociados. Este curso trata la Teoría de Grafos desde el punto de vista de la modelización, lo que nos permitirá con posterioridad resolver muchos problemas de diversa índole. Presentaremos ejemplos de los distintos problemas en un contexto real, analizaremos la representación de éstos mediante grafos y veremos los algoritmos necesarios para resolverlos.

Self Paced
Self-Paced
Math Graph Theory Mathematics
Details
Probability - The Science of Uncertainty and Data (edX) EdX
MIT,MITx

Probability - The Science of Uncertainty and Data (edX)

Sci: Mathematics Statistics & Data Analysis

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference. The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Sep 1st 2026
13-24 Weeks
Math Statistics Probability
Details
MathTrackX: Probability (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Probability (edX)

Sci: Mathematics

Understand probability and how it manifests in the world around us. This course introduces probability and how it manifests in the world around us. Beginning with discrete random variables, together with their uses in modelling random processes involving chance and variation, you will start to uncover the framework for statistical inference.

Self Paced
Self-Paced
Math Probability Mathematics
Details
Introductory Statistics : Sample Survey and Instruments for Statistical Inference (edX) EdX
Seoul National University,SNUx

Introductory Statistics : Sample Survey and Instruments for Statistical Inference (edX)

Economics & Finance Social Sciences

The purpose of this course is to introduce basic concepts of sample surveys and to teach statistical inference process using real-life examples. In this course, you will learn about sample surveys with the concepts of samples and populations. In addition, we will discuss possible problems(bias) of the surveys based on practical examples and concept of probability errors in sampling.

Self Paced
Self-Paced
Statistics Probability Statistical Inference
Details
Data Science: Probability (edX) EdX
HarvardX,Harvard University

Data Science: Probability (edX)

Statistics & Data Analysis Data Science

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008. In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

Self Paced
Self-Paced
Probability Monte Carlo Method Data Science
Details
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Statistics & Data Analysis Data Science

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
Probability Machine Learning Regression
Details
Cours préparatoire: Fonctions trigonométriques, logarithmiques et exponentielles (edX) EdX
École Polytechnique Fédérale de Lausanne,EPFLx

Cours préparatoire: Fonctions trigonométriques, logarithmiques et exponentielles (edX)

Sci: Mathematics

Ce cours donne les connaissances fondamentales liées aux fonctions trigonométriques, logarithmiques et exponentielles. Le cours propose une approche très détaillée et précise des notions fondamentales liées aux fonctions trigonométriques, logarithmiques et exponentielles.

Self Paced
Self-Paced
Math Trigonometry Mathematics
Details