The final part 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.
Statistics 2 Part 2 is a comprehensive course designed by the London School of Economics (LSE) focused on advancing your understanding of elementary statistical theory. This course delves deeper into measurement techniques, methods, and hypothesis testing concepts that were introduced in previous modules. It's suitable for either standalone study or as an installment in the LSE's MicroBachelors program in Statistics Fundamentals.
This intermediate-level course does not require previous knowledge in statistics, although prior completion of LSE's Statistics 1 and the first part of Statistics 2 is highly recommended to ensure a solid foundational understanding and to cope with the cumulative nature of the content covered.
This course is ideal for individuals interested in econometrics, finance, and quantitative social sciences, and those looking to build a foundational knowledge in statistics for pursuing more specialized courses.
Skills acquired from this course can be applied in various real-world settings such as in economic analysis, financial modeling, and social science research, where rigorous quantitative analysis and statistics are essential.
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'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.
