Statistics

Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R. Modern finance is the science of decision making in an uncertain world, and its language is mathematics. As part of the MicroMasters® Program in Finance, [...]

This course is meant to be a direct continuation of "Statistics and Data Analysis with Excel, Part 1." Therefore, it is not recommended to take Part 2 unless you've also taken Part 1. Building on the topics learned in Part 1 of the course (probability, probability mass and density [...]

Statistical analysis is an indispensable aspect of data analysis because it allows us to collect, review and analyse data to draw valuable conclusions in various industries. This is why the market for statisticians is projected to grow in the future. If you want to build your statistics and probability [...]

This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.

This is primarily aimed at first- and second-year undergraduates interested in psychology, statistics, data analysis, and research methods along with high school students and professionals with similar interests.

Once we have generated data, we need to answer the research question by performing an appropriate statistical analysis. Engineers and business professionals need to know which test or tests to use. Through this class, you will be able to perform one sample tests for comparison to historical data. You [...]

"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students [...]

"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages [...]

Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll [...]