EdX

Statistics 1 Part 1: Introductory statistics, probability and estimation (edX)

Offered by The London School of Economics and Political Science, LSE,
Statistics 1 Part 1: Introductory statistics, probability and estimation (edX)

The first 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 1 Part 1 is a self-paced course from LSE which aims to introduce you to and develop your understanding of essential statistical concepts, methods and techniques, emphasising the applications of these methods. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals or the LSE MicroBachelors program in Mathematics and Statistics Fundamentals.
Part 1, Introductory Statistics, Probability and Estimation, covers the following topics:

  • Mathematical revision and the nature of statistics
  • Data visualisation and descriptive statistics
  • Probability theory
  • The normal distribution and ideas of sampling
  • Point and interval estimation

Statistics 1 Part 1 forms part of a series of courses which focuses on the application of statistical methods in management, economics and the social sciences. During this course, you will focus on the interpretation of tables and results, and how to approach statistical problems effectively.
This course is part of the Mathematics and Statistics Fundamentals MicroBachelors Program and the Statistics Fundamentals MicroBachelors Program.

Prerequisites:
Statistics 1 Part 1 assumes no prior knowledge of statistics. Essential statistical concepts are introduced and are further built on in Statistics 1 Part 2 (and also in Statistics 2, Parts 1 and 2). It is strongly recommeded to study the LSE statistics courses in order (progressing to Statistics 1, Part 2) given the cumulative nature of the subject matter.

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

  • be familiar with some key ideas of statistics that are accessible to a student with a moderate mathematical competence
  • be able to routinely apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels
  • have a grounding in probability theory

Syllabus

  • Mathematical revision and the nature of statistics
  • Data visualisation and descriptive statistics
  • Probability theory
  • The normal distribution and ideas of sampling
  • Point and interval estimation
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Statistics (edX) EdX
Delft University of Technology,DelftX

Statistics (edX)

Statistics & Data Analysis

This course provides an overview of bachelor-level statistics. You will review the concepts of descriptive and inferential statistics. You will use the statistical software package R on real data to gain insight in these topics. A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.

Self Paced
Self-Paced
Statistics Bootstrap Linear Regression
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
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
Estadística Aplicada a los Negocios (edX) EdX
Galileo University,GalileoX

Estadística Aplicada a los Negocios (edX)

Statistics & Data Analysis

Aprende las principales herramientas y técnicas de la estadística descriptiva y la estadística inferencial para analizar e interpretar datos desde la perspectiva de negocios facilitando la toma de decisiones. Este curso proporciona una introducción al análisis de datos en base a las principales herramientas estadísticas, enfocándose en la estadística descriptiva y la estadística inferencial.

Self Paced
Self-Paced
Statistics Econometrics Data Analysis
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
External Debt Statistics (edX) EdX
International Monetary Fund - IMF,IMFx

External Debt Statistics (edX)

Economics & Finance

This course, presented by the Statistics Department, is intended to provide participants with a thorough understanding of the international standards for the compilation of EDS presented in the 2013 EDS: Guide for Compilers and Users (2013 EDS Guide). This course is for those interested in learning the fundamentals of compiling international accounts - specifically compilation of external debt statistics (EDS) and/or international investment position (IIP) statistics. It is a basic-level course laying the foundations.

Self Paced
Self-Paced
Statistics Debt External Debt Statistics
Details
Statistics and R (edX) EdX
HarvardX,Harvard University

Statistics and R (edX)

Sci: Biology & Life Sciences Statistics & Data Analysis

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Self Paced
Self-Paced
Statistics Data Analysis Statistical Inference
Details
Advanced statistical physics (edX) EdX
École Polytechnique Fédérale de Lausanne,EPFLx

Advanced statistical physics (edX)

Sci: Physics

We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications. This course covers non-equilibrium statistical processes and the treatment of fluctuation dissipation relations by Einstein, Boltzmann and Kubo. Moreover, the fundamentals of Markov processes, stochastic differential and Fokker Planck equations, mesoscopic master equation, etc will be treated in detail. Prior knowledge of statistical physics is highly recommended but not required.

Self Paced
Self-Paced
Physics Statistics Statistical Methods
Details
MathTrackX: Statistics (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Statistics (edX)

Sci: Mathematics

Understand fundamental concepts relating to statistical inference and how they can be applied to solve real world problems. This course will build on probability and random variable knowledge gained from previous courses in the MathTrackX XSeries with the study of statistical inference, one of the most important parts of statistics.

Self Paced
Self-Paced
Math Statistics Statistical Inference
Details