Top 5 Key Statistics Concepts for Data Science Interviews: P-value, Confidence Interval, Power, Errors
In this video, we will discuss the top 5 statistics concepts that are essential for Data Science interviews. I will provide insights on P-value, Confidence Interval, Power, and Errors.

Emma Ding
65.0K views โข Feb 3, 2021

About this video
Top 5 Statistics Concepts in Data Science Interviews
In this video, we will talk about the top 5 statistics concepts in Data Science interviews. I will show you how to explain those concept to both technical and non-technical audiences.
Typos
10:09 "hull" hypothesis should be "null" hypothesis
๐ขGet all my free data science interview resources
https://www.emmading.com/resources
๐ก Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet
๐ Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet
๐ฃ Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet
๐ต Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist
โ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching
// Comment
Got any questions? Something to add?
Write a comment below to chat.
// Let's connect on LinkedIn:
https://www.linkedin.com/in/emmading001/
====================
Contents of this video:
====================
0:00 Intro
1:27 Structure your answer for technical audience
2:08 Structure your answer for non-technical audience
3:04 Power, Type I error, Type II error (for technical audience)
5:15 Power, Type I error, Type II error (for non-technical audience)
6:17 Confidence interval (for technical audience)
8:33 Confidence interval (for non-technical audience)
9:20 P value (for technical audience)
11:29 P value (for non-technical audience)
In this video, we will talk about the top 5 statistics concepts in Data Science interviews. I will show you how to explain those concept to both technical and non-technical audiences.
Typos
10:09 "hull" hypothesis should be "null" hypothesis
๐ขGet all my free data science interview resources
https://www.emmading.com/resources
๐ก Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet
๐ Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet
๐ฃ Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet
๐ต Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist
โ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching
// Comment
Got any questions? Something to add?
Write a comment below to chat.
// Let's connect on LinkedIn:
https://www.linkedin.com/in/emmading001/
====================
Contents of this video:
====================
0:00 Intro
1:27 Structure your answer for technical audience
2:08 Structure your answer for non-technical audience
3:04 Power, Type I error, Type II error (for technical audience)
5:15 Power, Type I error, Type II error (for non-technical audience)
6:17 Confidence interval (for technical audience)
8:33 Confidence interval (for non-technical audience)
9:20 P value (for technical audience)
11:29 P value (for non-technical audience)
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Video Information
Views
65.0K
Likes
1.9K
Duration
13:11
Published
Feb 3, 2021
User Reviews
4.7
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