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.
London School of Economics and Political Science (LSE)
Welcome to Statistics 1 Part 1, an engaging and comprehensive course offered by the prestigious London School of Economics and Political Science (LSE). This self-paced course is designed to introduce you to the fascinating world of statistics, providing you with a solid foundation in essential concepts, methods, and techniques. What sets this course apart is its emphasis on practical applications, making it especially valuable for those interested in management, economics, and social sciences.
The beauty of Statistics 1 Part 1 is that it assumes no prior knowledge of statistics. However, a moderate level of mathematical competence will be beneficial. It's strongly recommended to progress to Statistics 1 Part 2 after completing this course, given the cumulative nature of the subject matter.
The skills acquired in this course have wide-ranging applications in the real world. You'll be able to:
By enrolling in Statistics 1 Part 1, you're taking the first step towards mastering the language of data. Whether you're aiming to advance your career, enhance your academic performance, or simply satisfy your curiosity about the world of statistics, this course offers a comprehensive and accessible introduction to this essential field. Don't miss this opportunity to learn from one of the world's leading institutions in social sciences and start your journey into the powerful world of statistics today!
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
This course introduces the basic tools and methods of statistical analysis, which can be applied to a wide variety of situations and data encountered in the areas of business and economics. Topics discussed include descriptive statistics, probability, sampling distributions, estimation, and hypothesis testing. By the end of the semester students should be able to compute and interpret basic descriptive statistical measures, understand the basic concepts of probability, and apply techniques of statistical inference.
This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions – including the normal distribution, which in turn leads to the Central Limit Theorem. We then discuss elementary descriptive statistics and estimation methods.
Test your knowledge and ability to apply the concepts and methods from the four courses included in the LSE MicroBachelors program in Mathematics and Statistics Fundamentals and take your first step towards further study at undergraduate level or upskill in high-growth careers.
