Up to one month

In this 4-week self-paced Data Science course in English, you’ll dedicate 2 to 4 hours per week to build your skills. The course, designed for intermediate learners, offers the option to audit for free or obtain a Verified Certificate for $129

You will delve into essential statistical concepts, including random variables, distributions, inference using p-values and confidence intervals, exploratory data analysis, and non-parametric statistics.

Due to the diverse educational backgrounds of the students, the series is divided into seven parts. You have the flexibility to enrol in the entire series or choose individual courses that align with your interests.

Join this comprehensive 4-week data science course if you’re ready to take your data analysis skills to the next level. With a commitment of 2 to 4 hours per week, you can pace yourself through this intermediate-level program. The course is conducted in English, with video transcripts available for your convenience. You’ll delve into more advanced data science concepts, enhancing your proficiency in this field.

In this course, you’ll gain valuable insights into managing high-throughput data effectively. We’ll tackle complex topics such as the multiple comparison problem, Family Wide Error Rates, and False Discovery Rate. You’ll also learn about Error Rate Control procedures, including the essential Bonferroni Correction. These skills are crucial for data scientists and analysts aiming to maintain precision and accuracy in their work.

The course is available for free in audit mode, or you can opt for a Verified Certificate for $129.

This 4-week self-paced English course aims to introduce you to the intriguing field of data science. With a flexible commitment of 2 to 4 hours per week, you can explore and master the core concepts at your own pace. Through engaging lessons and hands-on exercises, you’ll dive into the world of data, learning how to analyze it and derive insights from it. The focus is on several techniques that are widely used in the analysis of high-dimensional data.

If you want to analyze and interpret data, this course is for you. You’ll learn about mathematical concepts such as distance and singular value decomposition (SVD) that underlie dimensionality reduction and multidimensional scaling, including their relationship to principal component analysis. In addition, we will address the complex issue of group effects in genomic data analysis and discuss detection and correction techniques. You will also look at data visualization and analysis using methods such as principal component analysis and factor analysis.