Скачать или смотреть Interview Preparation For R | R Programing Language | R | Ivy Pro School

This video will help you to get some idea on how you can prepare for a "R" interview. Follow the questions that have been mentioned in the video for effective outcome.

Common R/ ML Interview Questions are as follows:

0:00 Introduction
2:38 How can we list all the variables in R?
2:55 How can we remove the existing variables?
3:27 What are Reserved Keywords and how can we identify them?
4:25 What are different Data Structures in R?
5:08 Difference between a Vector and a List
6:10 Difference Between as.integer and floor function
7:09 What is the role of sapply() function?
8:22 How to generate multiple plots in a window?
9:30 How to install a package in R?
10:41 What is the purpose of rbind() function in R?
11:57 What is Null Hypothesis?
12:35 What are the different types of Alternative Hypothesis?
13:23 What are the measures of Central Tendency for Continuous variables?
14:21 What is Multicollinearity and how to check it?
16:45 Difference between two sample t-test and paired t-test?
18:02 Difference between SRSWR and SRSWOR?
19:23 What is ANOVA?
21:19 What are Dummy variables and reference category?
23:16 What will R choose as a reference category by default?
24:14 What is Correlation?
25:03 In Linear regression, which criteria is taken into account to find optimal values of intercept and slope?
26:07 What is the ideal value of SSE?
26:46 Difference between R squared and Adjusted R squared?
27:59 How to calculate accuracy of a Linear Regression Model?
30:11 What is PCA used for?
31:34 Why is Normalization important before applying PCA?
32:38 What is Entropy used for?
33:49 When can we have the maximum Information gain?
34:55 Difference between Logistic and Linear Regression
36:28 What are Type 1 and Type 2 errors in case of Logistic Regression?
37:11 What are within and between clusters sum of squares?
39:27 How can we break observed Time Series into different components?
41:02 What is a Stationary Time Series?
41:27 What is AR in ARIMA and how to derive it?

#interviewtips #interviewpreparation #interviewhacks #interviewquestions #rprogrammingforbeginners #rprogramming

❓Interested in a Data Science or Data Engineering career? Check out the Ivy Pro School’s top ranked Nascomm, Govt. of India and IBM accredited Data Science and Data Engineering courses: https://ivyproschool.com/

❓Why should you learn Data Science today?    • Future Of Analytics & Data Science | Learn...  

❓Are you a Fresher? Watch this video to know the Data Science Journey for Freshers:    • Data Science Journey for Freshers | Data S...  

✅Don’t forget to Subscribe to Ivy Pro School’s YouTube Channel:    / @ivyproschool  

✅Learn from Ivy Pro School’s Data Science and Data Engineering Students’ Success Stories :    • Student Success Story  

Liked the video? Check out below more playlists on Data Science and Data Engineering learning tutorial, alumni interview experiences, live data science case studies,etc:

⏩Students Share Interview Experiences-    • Data Science Interview Experience and Plac...  
⏩Data Cleaning using excel-    • Data Cleaning using Excel  
⏩SQL Interview questions solved in Hindi -    • Sql Interview Questions solved in Hindi  
⏩PowerBI Tutorials -    • Power BI Tutorial  
⏩Industry case studies and projects by students -    • Industry Case Studies & Projects by Students  
⏩Data Science for Beginners -    • Data Science for Beginners  
⏩Excel VBA tutorial -    • Excel VBA Tutorial  
⏩Excel Tutorials by Eeshani Agrawal -    • Excel for Beginners  
⏩Tableau Tutorials by Eeshani Agrawal -    • Tableau Tutorial by Eeshani Agrawal  

For more updates on courses and tips don’t forget to follow us on:
Instagram:   / ivyproschool  
LinkedIn:   / ivy-professional-school  
Facebook:   / ivyproschool  
Twitter:   / ivyproschool  
Telegram: https://t.me/learndswithexperts

Want to take a Data Science Course with Ivy Professional School - visit us at https://ivyproschool.com You can call us at +91-7676882222 or mail to [email protected].

#ivyproschool #datawithivypro #dataanalytics #analytics