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Opiniones y comentarios de aprendices correspondientes a Probability Theory, Statistics and Exploratory Data Analysis por parte de HSE University

333 calificaciones
91 reseña

Acerca del Curso

Exploration of Data Science requires certain background in probability and statistics. This online course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science. The core concept of the course is random variable — i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables. While introducing you to the theory, we'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python. This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals). This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here

Principales reseñas

4 de dic. de 2021

I liked this course, it is packed with valuable information on theoretical as well as practical aspects of probability theory.\n\nThe instructor explains everything clearly with details.

12 de oct. de 2020

Very nice approach to such a vast topic , making it more understandable. Such type of interactive lectures are advisable for courses on Calculus and Algebra from the same University.

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51 - 75 de 91 revisiones para Probability Theory, Statistics and Exploratory Data Analysis

por Julio C D M

25 de abr. de 2021

Excellent course. It's very useful and has a lot of real life examples.

por Anna J

28 de abr. de 2021

The topic is hard to grasp, but very well explained, I learned a lot.

por Ivan A T U

27 de ago. de 2021

Es un curso muy completo y muy avanzado, para mi. Gracias

por Miguel A G G

3 de ene. de 2022

Nice overall to do exploratory data analysis.

por Hervé d K

13 de ago. de 2021

Excellent Professor, I have learned a lot.


28 de jul. de 2021

thanks alot , excellent course


3 de ago. de 2021

its good course !i liked it


28 de sep. de 2020

this course is great

por thandarkyaw

24 de ago. de 2020

Thank you so much.

por Ловягин А А

4 de jul. de 2021

Great course!

por bolgum h g 2

25 de jul. de 2021

ok it's good

por Kyama V

19 de abr. de 2021

nice course


10 de ago. de 2020

best course

por Mahalingam P R

30 de ago. de 2020


por MR. J T R A

22 de may. de 2020

Very good

por Jaskeerat S

20 de may. de 2020



28 de sep. de 2020


por Swapnil S T

7 de oct. de 2020


por allabharath t 2

22 de jul. de 2021


por Tanu s

4 de may. de 2020


por ANUJA M 2

8 de ago. de 2021


por HARSHA V 9

3 de ago. de 2021



3 de ago. de 2020


por Jim F

29 de abr. de 2021

Ilya is a great teacher. He takes the time to motivate with examples, to start with the basics, and to help intuition with visualization.

The main thing I felt was missing was an introduction to common distributions (e.g. exponential, beta, gamma, ...). It seems like much data science/ML work requires this practical understanding of which are appropriate in a given situation. I would have appreciated tests like "You are watching cars pass by. Consider the expected time between one car and the next. What kind of distribution is appropriate to model this?" (An exponential distribution, I think?) Or, "You flip a coin 10 times and observe 3 heads. What is the most likely underlying probability of heads? What kind of distribution is appropriate to model this underlying true probability?" (A beta distribution, I think?)

Strangely, the course starts off challenging, but the final two weeks are comparatively obvious and easy.

Ilya shows several examples in Python, but I would have appreciated more Python tests/exercises to reinforce this.

por Majid H

27 de nov. de 2020

The course was overall ok for an intermediate level of understanding statistics in data science. I recommend watching it with higher video speeds to get used to the bad quality of the voice. It was also better if they explain important concepts like variance, covariance and correlations with higher introducing the prerequisite for every method. For example, it is not mentioned that Pearson correlation has to be applied to normally distributed data. Thin kind of details could make the course perfect