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

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
Statistical Inference and Modeling for High-throughput Experiments (edX) EdX
HarvardX,Harvard University

Statistical Inference and Modeling for High-throughput Experiments (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

A focus on the techniques commonly used to perform statistical inference on high throughput data. In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation.

Self Paced
Self-Paced
Statistics Modeling Statistical Inference
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
Bases Matemáticas: Derivadas (edX) EdX
Universitat Politècnica de València,UPValenciaX

Bases Matemáticas: Derivadas (edX)

Sci: Mathematics

Curso básico sobre funciones y sus derivadas, incluyendo sus aplicaciones a la resolución de problemas. Este curso se concibe como una revisión de los conceptos básicos del cálculo diferencial, necesarios para los primeros cursos de aquellos estudios universitarior en los que se imparte matemáticas.

Self Paced
Self-Paced
Math Mathematics Functions
Details
Linear Algebra II: Matrix Algebra (edX) EdX
Georgia Institute of Technology,GTx

Linear Algebra II: Matrix Algebra (edX)

Sci: Mathematics

This course takes you through roughly three weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. Your ability to apply the concepts that we introduced in our previous course is enhanced when you can perform algebraic operations with matrices. At the start of this class, you will see how we can apply the Invertible Matrix Theorem to describe how a square matrix might be used to solve linear equations.

Self Paced
Self-Paced
Math Algebra Equations
Details
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Statistics & Data Analysis CS: Programming

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced
Programming Python Statistics
Details
Calculus Applied! (edX) EdX
HarvardX,Harvard University

Calculus Applied! (edX)

Sci: Biology & Life Sciences Sci: Mathematics

Apply tools of single-variable calculus to create and analyze mathematical models used by real practitioners in social, life, and physical sciences. In this course, we go beyond the calculus textbook, working with practitioners in social, life and physical sciences to understand how calculus and mathematical models play a role in their work.

Self Paced
Self-Paced
Math Calculus Mathematics
Details
BioStatistics (edX) EdX
DoaneX,Doane University

BioStatistics (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

This college-level, credit-eligible Biostatistics course will teach you the skills required for success in future analytical studies in biology. In this undergraduate-level biostatistics course, the learners will be introduced to the use of statistics and study designs in biology. Upon successful completion of this course, learners will be able to design experimental, quasi-experimental and observational studies that will meet regulatory guidelines; collect, analyze, and interpret data using appropriate statistical tools.

Self Paced
Self-Paced
Biology Statistics Biostatistics
Details
Maths Essentials (edX) EdX
Imperial College Business School,Imperial College London

Maths Essentials (edX)

Management & Leadership Sci: Mathematics

Discover and acquire the fundamental maths skills that you will need to use while studying an MBA program, from algebra to differentiation and geometric series. Planning to study for an MBA but unsure of your basic maths skills? All MBA programs, and in business generally require some maths, particularly on quantitative subjects such as Accounting, Economics and Finance.

Self Paced
Self-Paced
Math Calculus Decision Making
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