Learn basic concepts in probability and statistics, data analysis, and linear models for data science.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
Basic principles in probability, statistics, and linear modeling are important building blocks for working in data science. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge offered by this specialization.
Courses focus on principles and methods intended for applications in biostatistics, including concepts of probability, distributions and likelihood, hypothesis testing, and case-group sampling.
The specialization requires a certain level of mathematical understanding, including a basic understanding of:
The specialization includes a series of rigorous graded exams aimed at testing understanding of key concepts.
Duration: 13 hours
Rating: 4.4 out of 496 ratings
What you will learn: The course introduces the basic concepts of probability and statistics as used in data analysis. It will be given at a basic level to students with third or fourth year mathematical knowledge, including a basic understanding of calculations.
Skills you will acquire:
Duration: 11 hours
Rating: 4.3 out of 131 ratings
What you will learn: Learn basic concepts in data analysis and statistical inference, with a focus on independent samples.
Skills you will acquire:
Duration: 8 hours
Rating: 4.5 out of 187 ratings
What you will learn: The course offers an introduction to methods of inquiry from an algebraic and mathematical perspective.
Skills you will acquire:
Duration: 5 hours
Rating: 4.5 out of 95 ratings
What you will learn: The course offers in-depth training in understanding advanced linear models.
Skills you will acquire:
