Data Science Interview Questions with Answers
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. Buy ads: https://telega.io/c/DataScienceInterviews
Show more8 591
Subscribers
+7124 hours
+4067 days
+1 84630 days
- Subscribers
- Post coverage
- ER - engagement ratio
Data loading in progress...
Subscriber growth rate
Data loading in progress...
Amazon Interview Process for Data Scientist position
๐Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
๐ ๐ฅ๐ผ๐๐ป๐ฑ ๐ฎ- ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฟ๐ฒ๐ฎ๐ฑ๐๐ต:
In this round the interviewer tested my knowledge on different kinds of topics.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฏ- ๐๐ฒ๐ฝ๐๐ต ๐ฅ๐ผ๐๐ป๐ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฐ- ๐๐ผ๐ฑ๐ถ๐ป๐ด ๐ฅ๐ผ๐๐ป๐ฑ-
This was a Python coding round, which I cleared successfully.
๐๐ฅ๐ผ๐๐ป๐ฑ ๐ฑ- This was ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ where my fitment for the team got assessed.
๐๐๐ฎ๐๐ ๐ฅ๐ผ๐๐ป๐ฑ- ๐๐ฎ๐ฟ ๐ฅ๐ฎ๐ถ๐๐ฒ๐ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if youโre targeting any Data Science role:
-> Never make up stuff & donโt lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐ 1
Hey guys,
Here are some best Telegram Channels for free education in 2024
๐๐
Free Courses with Certificate
Web Development Free Resources
Data Science & Machine Learning
Programming Free Books
Python Free Courses
Ethical Hacking & Cyber Security
English Speaking & Communication
Stock Marketing & Investment Banking
Coding Projects
Jobs & Internship Opportunities
Crack your coding Interviews
Udemy Free Courses with Certificate
Free access to all the Paid Channels
๐๐
https://t.me/addlist/ID95piZJZa0wYzk5
Do react with โฅ๏ธ if you need more content like this
ENJOY LEARNING ๐๐
โค 2๐ 1
Coffee Break NumPy
Christian Mayer, 2018
Coffee Break NumPy.pdf2.34 MB
โค 4๐ 1
What is feature selection? Why do we need it?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
โค 4
What are the main parameters of the decision tree model?
โข maximum tree depth
โข minimum samples per leaf node
โข impurity criterion
โค 4
What are the decision trees?
This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.
In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.
A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.
Various techniques : like Gini, Information Gain, Chi-square, entropy.
โค 2๐ 1
Why is it require to split our data into three parts: train, validation, and test?
โข The training set is used to fit the model, i.e. to train the model with the data.
โข The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
โข Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
โค 1๐ 1
Can you explain how cross-validation works?
Cross-validation is the process to separate your total training set into two subsets: training and validation set, and evaluate your model to choose the hyperparameters. But you do this process iteratively, selecting differents training and validation set, in order to reduce the bias that you would have by selecting only one validation set
What is K-fold cross-validation?
K fold cross validation is a method of cross validation where we select a hyperparameter k. The dataset is now divided into k parts. Now, we take the 1st part as validation set and remaining k-1 as training set. Then we take the 2nd part as validation set and remaining k-1 parts as training set. Like this, each part is used as validation set once and the remaining k-1 parts are taken together and used as training set. It should not be used in a time series data.
๐ 1
What is the bias-variance trade-off?
โข Bias is the error introduced by approximating the true underlying function, which can be quite complex, by a simpler model. Variance is a model sensitivity to changes in the training dataset.
โข Bias-variance trade-off is a relationship between the expected test error and the variance and the bias - both contribute to the level of the test error and ideally should be as small as possible:
ExpectedTestError = Variance + Biasยฒ + IrreducibleError
โข But as a model complexity increases, the bias decreases and the variance increases which leads to overfitting. And vice versa, model simplification helps to decrease the variance but it increases the bias which leads to underfitting.
๐ 1
What is sigmoid? What does it do?
A sigmoid function is a type of activation function, and more specifically defined as a squashing function. Squashing functions limit the output to a range between 0 and 1, making these functions useful in the prediction of probabilities.
Sigmod(x) = 1/(1+e^{-x})
๐ 2
Choose a Different Plan
Your current plan allows analytics for only 5 channels. To get more, please choose a different plan.