Statistics & Mathematics for Data Science & Data Analytics (Udemy.com)

This course is well-structured, informative, and beginner-friendly. It provides a strong foundation in statistical and mathematical concepts essential for data science and analytics. The content is easy to understand and industry-relevant. Including more Python-based exercises, real-world datasets, and hands-on projects would further enhance the learning experience. Overall, it is an excellent course for students and aspiring data professionals."

The course is well organized and provides a meaningful overview of the material in a concise, yet thorough process. Speaking as someone who knew little about statistics going in, the instructor helped me understand the basics so that I can progress to a more advanced level in the specific areas that I will need. I am very pleased with the course and recommend it without reservation.

could have used clearear notations and examples , for the one way anova im lost between samples and columns , you cant refer to them as columns you have to say what the columns are in the practical sense so i understand , also you chose a 3 by 3 table so i literally confuse the columns and rows for the degrees of freedom ... still confused and watched it 2 times :( .. but i understood something :). also in regression you use a number from the example and then symbols ( 4 + s ), either put the numbers or put only symbols you cant have it half way :( (imho)

yes in the central limit theorem..n is defined and the theres an 'm' variable on the graph...basically its pretty confusing ( coin being tossed 50 times is N or this experiment of tossing coin 50 times repeated N times...something is amiss.. I also feel that the probability concepts could have been dealt better...many of the explanations are done taking for granted that the terms/concepts are clear.I had to refer to other resources to understand it better.

Really nice course. Nikolai speaks clearly and uses simple and straight forward examples. One thing that would be helpful is having more resources to download. Many times I had to pause the video to write down the main formulas etc by hand. I have a background in finance, so I didn't have many unfamiliar terms, don't know if that would be the case for complete beginners.

Started Study Data Science only by code and libraries but there was no info about what was actually performed. With the help of this course, I actually figured out what Data Science and ML is all about what is Distribution all about and how I can actually perform a test and realize visual data, analyze, and draw conclusions. Even helped me figure out stuff I use in Finance and Technical Analysis Thank you man you got a 5* from me.

Dear Nikolai Schuler, This was a valuable course on Statistics and other important mathematics required for Data Science. I have taken Bootcamps on Data Science but I never had the mathematical intuition of how these calculations were done. But now because of you, I am able to understand and even visualize the working of the Machine Learning algorithms. I am from India, and I am grateful for people like you who provide us with such quality education. I am very happy with this course as It covers almost all the important topics. I have the entire course but I will go through the two-way ANOVA again because the calculations are a bit complex. so I need to do it again for my mind to understand. Apart from that, I am very satisfied with the course. Thank you.

This course was amazing. I highly recommend this course for any one looking to build a statistics and math foundation. Even if you have no prior knowledge. Nikolai is a great instructor and created a very well structured course. The tests at the end of the sections really helped to better understand the material. I also really enjoyed the resources that are included. I look forward to seeing what other content Nikolai creates!

Concise, clear and step-wisely presented. It is quite magical how the author can elaborate the important concept concisely without over-complicating things; starting from ground zero, the lecture gradually connected one to another. Although the course-conciseness is what I rate highly, I hope in the future the instructor would consider expanding the course materials (some mixed-models, GLM, repeated measures ANOVA, feature selection of linear model will not be harmful right?). I feel better intuititively for the statistical and machine learning concept after the course. Since those has became less scary now (thanks to the instructor), I think am going to dive into some coding. Wishing my journey continues well.

Since this is a math focused tutorial and involves no coding , most people coming here are for the math and concept . However this tutorial just provides concept theoretically - concept should be elaborated by more examples . That was my initial view - however explanation of important topics like ML algorithm is excellent and detailed solved example of ANOVA type 1 and type 2 is included . Hence it deserves an above 4 rating.