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00:02:57 : ( 1) What is Data Science 00:04:40 : ( 2) What are the important skills to have in Python with regard to data analysis (pandas, numpy) 00:06:23 : ( 3) What is Selection Bias (selection effect, distortion, randomized selection, non-stratified sample) 00:09:18 : ( 4) Difference b/w long and wide format data 00:11:00 : ( 5) What is a Normal Distribution (sym. Bell curve, (Std normal distribution: mean 0, Std deviation 1); Central Limit Th, law of large numbers...) 00:14:30 : ( 6) What is the goal of A/B Testing 00:17:08 : ( 7) What do u understand by statistica power of Sensitivity and how do you calc it (Confusion matrix, Precision, Specificity, Recall, F-1 Score, 00:22:31 : ( 8) Differences b/w overfitting and underfitting (generalization as the baseline, below baseline, above baseline) 00:26:01 : ( 9) Python or R, which would u prefer for text analytics (R packages: tm, Python packages: pandas, numpy, nltk) 00:27:18 : (10) How does data cleansing plays a vital role in analysis 00:29:22 : (11) Differentiate b/w univariate, bovariate and multi variate analysis 00:30:44 : (12) What is cluster sampling (Systematic sampling) (used when it is diffult to study the whole pipulation spread across wide areas) 00:32:21 : (13) What is systemtic sampling 00:32:44 : (14) What is an eigen value and eigen vectors (linear combination of variables, for reducing the variables / dimensionality reduction .. PCA basis) 00:35:38 : (15) Can u site some examples where a false positive is important than a false negative 00:38:30 : (16) Can u site some examples where a false negative is important than a false positive 00:40:11 : (17) Cite cases when both FP / FN are important 00:41:13 : (18) Difference between Test set and validation Set (Valiation step, k-fold Cross validation, tuning params) 00:44:22 : (19) What is Cross validation (Training on varios subsets of data) 00:46:10 : (20) What is ML 00:47:08 : (21) What is Supervised learning (Egs: Support vectormachines , regression, ) 00:48:20 : (22) What is unSupervised learning 00:49:10 : (23) What are various classification algos (Various classifiers are Linear ; Deccision Trees ; SVM ; Neural networks ; Kernel Estimation ; Quadratic) 00:50:29 : (24) What is logistic regression. State an example when used this (Used for binary classification) 00:51:47 : (25) What are recommendater systems 00:54:40 : (26) What is a linear regression (continuous regression, root mean square error) 00:58:26 : (27) What is collaborative filtering (user based / item based recommendation engines) 00:59:37 : (28) How can outlier values be treated (removing mean +/- 3Std deviation;) 01:02:36 : (29) Steps involved in Analytics project( Problem statement ; transformation and visualzation, correlations ; ) 01:04:52 : (30) How to u treat missing data during analysis (finding average and replacing, mean and ) 01:06:47 : (31) How will u define the number of clusters in a clustering algo (K-mean clustering; elbow curve creation) 01:09:39 : (32) In any 15m interval there is 20% prob that u ll see atleast 1 shootin star. What is the probability to see that in an hour ??? 01:12:52 : (33) How do u generate a random number between 1-7 with only a die 01:16:40 : (34) A couple has two kids and atleast one of them is a girl, what is the probability that they have two girls 01:18:06 : (35) a jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random and toss it 10 times. Given all are heads, what is pro next is also head
Few pointers, for first problem it is actually a poisson distribution, so with a mean (0.20 x 4 = 0.8), so probability of not seeing any star is e^(-0.8), and the probability to the contrary is 1-e^(-0.8) ~ 0.55
Also you need to check 'nova show ' output. If you can see the compute host present in "OS-EXT-SRV-ATTR:hypervisor_hostname" , then we can understand that the scheduler was successfully able to allocate a compute host and something went wrong with the compute host. In that case, you need to check the nova-compute logs of that hypervisor. Hope this helps :)
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1:17:30...Question number 34. The answer 1/3 is wrong, the actual answer is 1/2 because the you have considered BG and GB as two different cases but this problem requires Combination not Permutation. We are only interested in knowing if they have one boy and a girl, we are not interested in knowing weather the first child is Boy and the second is a Girl and vice versa.
Vedha Viyash Actuaally he was right. I thought the same like you did. But then I thought the answer 1/2 is only correct if the parent says "My first child is a girl" (or my second child is a girl"). That means it eliminates the possibility that the first child is a boy. That means there are more probability to have BG and GB, than GG.
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