| Define Data Science |
| Define Data Profiling |
| What is regression? Which models can you use to solve a regression problem? |
| What is linear regression? When do we use it? |
| What is gradient descent? How does it work? |
| What is the normal equation? |
| What is SGD — stochastic gradient descent? What’s the difference with the usual gradient descent? |
| What is overfitting? |
| What is K-fold cross-validation? |
| What is classification? Which models would you use to solve a classification problem? |
| What is logistic regression? When do we need to use it? |
| What is sigmoid? What does it do? |
| What is accuracy? |
| What is the confusion table? What are the cells in this table? |
| What is precision, recall, and F1-score? |
| What is the ROC curve? When to use it? |
| What is AUC (AU ROC)? When to use it? |
| What is the PR (precision-recall) curve? |
| What is the area under the PR curve? Is it a useful metric? |
| What is regularization? Why do we need it? |
| What is feature selection? Why do we need it? |
| What is random forest? |
| What is gradient boosting trees? |
| What is ReLU? How is it better than sigmoid or tanh? |
| What is dropout? Why is it useful? How does it work? |
| What is backpropagation? How does it work? Why do we need it? |
| What is Adam? What’s the main difference between Adam and SGD? |
| What is model checkpointing? |
| What is transfer learning? How does it work? |
| What is object detection? Do you know any architectures for that? Spring |
| What is object segmentation? Do you know any architectures for that? Spring |
| What is bag of words? How we can use it for text classification? |
| What is TF-IDF? How is it useful for text classification? |
| What is unsupervised learning? |
| What is clustering? When do we need it? |
| What is the curse of dimensionality? Why do we care about it? |
| What is the ranking problem? Which models can you use to solve them? |
| What is precision and recall at k? |
| What is mean average precision at k? |
| What is a recommender system? |
| What is collaborative filtering? |
| What is the cold start problem? |
| What is a time series? |
| What are the methods for solving linear regression do you know? |
| What are MSE and RMSE? |
| What are the decision trees? |
| What are the main parameters of the decision tree model? |
| What are the benefits of a single decision tree compared to more complex models? |
| What are the main parameters of the random forest model? |
| What are the potential problems with many large trees? |
| What are the main parameters in the gradient boosting model? |
| What are the problems with sigmoid as an activation function? |
| What are augmentations? Why do we need them? What kind of augmentations do you know? How to choose which augmentations to use? |
| What are the advantages and disadvantages of bag of words? |
| What are N-grams? How can we use them? |
| What are word embeddings? Why are they useful? Do you know Word2Vec? |
| What are the other clustering algorithms do you know? |
| What are good unsupervised baselines for text information retrieval? |
| What are good baselines when building a recommender system? |
| What are the problems with using trees for solving time series problems? |
| What do we do with categorical variables? |
| How do we check if a variable follows the normal distribution? |
| How do we choose K in K-fold cross-validation? What’s your favorite K? |
| How do we evaluate classification models? |
| How do we select the right regularization parameters? |
| How do we interpret weights in linear models? |
| How do we train decision trees? |
| How do we handle categorical variables in decision trees? |
| How do we select the depth of the trees in random forest? |
| How do we know how many trees we need in random forest? |
| How do you approach tuning parameters in XGBoost or LightGBM? Spring |
| How do you select the number of trees in the gradient boosting model? |
| How do we use SGD (stochastic gradient descent) for training a neural net? |
| How do we decide when to stop training a neural net? |
| How do you do an online evaluation of a new ranking algorithm? |
| How to validate your models? |
| How to interpret the AU ROC score? |
| How to set the learning rate? |
| How to select K for K-means? |
| Are CNNs resistant to rotations? What happens to the predictions of a CNN if an image is rotated? Spring |
| Are there any differences between continuous and discrete variables when it comes to feature importance of gradient boosting models? Spring |
| Can we have both L1 and L2 regularization components in a linear model? |
| Can we use L1 regularization for feature selection? |
| Can we use L2 regularization for feature selection? |
| Can we formulate the search problem as a classification problem? How? |
| Can you explain how cross-validation works? |
| Can you tell us how you approach the model training process? |
| Do we want to have a constant learning rate or we better change it throughout training? |
| Do you know any other ways to get word embeddings? Spring |
| Do you know how K-means works? |
| Do you know how DBScan works? |
| Do you know any dimensionality reduction techniques? |
| Do you know how to use gradient boosting trees for ranking? Spring |
| Feature importance in gradient boosting trees — what are possible options? |
| How can we know which features are more important for the decision tree model? |
| How can we use machine learning for text classification? |
| How can you use neural nets for text classification? Spring |
| How can we use CNN for text classification? Spring |
| How can we use machine learning for search? |
| How can we get training data for our ranking algorithms? |
| How can we use clicks data as the training data for ranking algorithms? Spring |
| How does L2 regularization look like in a linear model? |
| How does a usual fully-connected feed-forward neural network work? |
| How does max pooling work? Are there other pooling techniques? |
| How is time series different from the usual regression problem? |
| How L1 regularization looks like in a linear model? |
| How large should be N for our bag of words when using N-grams? |
| How we can initialize the weights of a neural network? |
| How we can use neural nets for computer vision? |
| How we can incorporate implicit feedback (clicks, etc) into our recommender systems? |
| How would you evaluate your ranking algorithms? Which offline metrics would you use? |
| If a weight for one variable is higher than for another — can we say that this variable is more important? |
| If there’s a trend in our series, how we can remove it? And why would we want to do it? |
| If you have a sentence with multiple words, you may need to combine multiple word embeddings into one. How would you do it? |
| In which cases AU PR is better than AU ROC? |
| Is accuracy always a good metric? |
| Is feature selection important for linear models? |
| Is it easy to parallelize training of a random forest model? How can we do it? |
| Is it possible to parallelize training of a gradient boosting model? How to do it? |
| Is logistic regression a linear model? Why? |
| Possible approaches to solving the cold start problem? Spring |
| Precision-recall trade-off |
| What happens to our linear regression model if we have three columns in our data: x, y, z — and z is a sum of x and y? |
| What happens to our linear regression model if the column z in the data is a sum of columns x and y and some random noise? |
| What happens when we have correlated features in our data? |
| What happens when the learning rate is too large? Too small? |
| What if we want to build a model for predicting prices? Are prices distributed normally? Do we need to do any pre-processing for prices? |
| What if instead of finding the best split, we randomly select a few splits and just select the best from them. Will it work? Spring |
| What if we set all the weights of a neural network to 0? |
| What kind of regularization techniques are applicable to linear models? |
| What kind of problems neural nets can solve? |
| What kind of CNN architectures for classification do you know? Spring |
| What regularization techniques for neural nets do you know? |
| What’s a convolutional layer? |
| What’s pooling in CNN? Why do we need it? |
| What’s singular value decomposition? How is it typically used for machine learning? |
| What’s the normal distribution? Why do we care about it? |
| What’s the effect of L2 regularization on the weights of a linear model? |
| What’s the difference between L2 and L1 regularization? |
| What’s the interpretation of the bias term in linear models? |
| What’s the difference between random forest and gradient boosting? |
| What’s the difference between grid search parameter tuning strategy and random search? When to use one or another? |
| What’s the learning rate? |
| When do we need to perform feature normalization for linear models? When it’s okay not to do it? |
| When would you use Adam and when SGD? |
| When would you choose K-means and when DBScan? |
| Which feature selection techniques do you know? |
| Which metrics for evaluating regression models do you know? |
| Which model would you use for text classification with bag of words features? |
| Which models do you know for solving time series problems? |
| Which optimization techniques for training neural nets do you know? |
| Which parameter tuning strategies (in general) do you know? |
| Which regularization techniques do you know? |
| Why do we need to split our data into three parts: train, validation, and test? |
| Why do we need one-hot encoding? |
| Why do we need randomization in random forest? |
| Why do we need activation functions? |
| Why do we actually need convolutions? Can’t we use fully-connected layers for that? |
| Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words? |
| Would you prefer gradient boosting trees model or logistic regression when doing text classification with embeddings? |
| You have a series with only one variable “y” measured at time t. How do predict “y” at time t+1? Which approaches would you use? |
| You have a series with a variable “y” and a set of features. How do you predict “y” at t+1? Which approaches would you use? |
| Define Python Panda operations followed in Data Science technology |
| Define the structure of Artificial Neural Networks |
| Define Back Propagation and its working process |
| Define Lambda functions with example |
| Define Power Analysis in R |
| What are the types of machine learning? |
| What is the Supervised learning in machine learning? |
| What is the Unsupervised learning in machine learning? |
| What are the commonly used python packages? |
| What are the commonly used R packages? |
| What is precision? |
| What is recall? |
| What is a normal distribution? |
| What is overfitting? |
| What is underfitting? |
| What is a univariate analysis? |
| What is the Pearson correlation? |
| What is the common perception about visualization? |
| What are the time series algorithms? |
| What is the basic responsibility of a Data Scientist? |
| What does SAS stand out to be the best over other data analytics tools? |
| What is RUN-Group processing? |
| What is the right way to validate the SAS program? |
| What is means by precision and Recall? |
| What is deep learning? |
| What is the F1 score? |
| What is the difference between Machine learning Vs Data Mining? |
| What are confounding variables? |
| What are the types of biases that can occur during sampling? |
| What is alias in import statement? Why is it used? |
| What is a nonparametric test used for? |
| What are the pros and cons of Decision Trees algorithm? |
| What are pros and cons of Naive Bayes algorithm? |
| What are the types of Skewness? |
| What is skewed data? |
| What is the skewness of this data? 27 ; 28 ; 30 ; 32 ; 34 ; 38 ; 41 ; 42 ; 43 ; 44 ; 46 ; 53 ; 56 ; 62 |
| What is an outlier? |
| What are the applications of data science? |
| What are the steps in exploratory data analysis? |
| What are the types of data available in Enterprises? |
| What are the various types of analysis on type of data? |
| What is difference between primary data and secondary data? |
| What is the difference between qualitative & quantitative ? |
| What is histogram? |
| What are the common measures of central tendancies? |
| What are quartiles? |
| What are the commonly used error metrics in regression tasks? |
| What are the commonly used error metrics for classification tasks? |
| What is it called when there are more than 1 explanatory variables in the regression task? |
| What are residuals in a regression task? |
| What are the main classifications in Machine learning? |
| What are the main types of supervised learning tasks? |
| What is R square value? |
| What are some common ways of imputation? |
| What is the difference between series and list |
| What parameter is used to update the data without explicitly assigning data to a variable. |
| What is the difference between a dictionary and a set? |
| What is the function to create test train split? |
| What is pickling? |
| What is unpickling? |
| What are the most common web frameworks of Python? |
| What are lambda function in Python and how it is different from def (defining functionsin Python? |
| What is your opinion on our current data process &nbs p;? |
| What do you know by the term Normal Distribution? |
| What is data visualization? |
| What Is a System? |
| What are the different benefits of language? |
| What are the two main elements of the hottest architecture? |
| What is Logistic Recession? |
| What are the different features of the mechanical learning process? |
| What Is Normal Distribution? |
| What is Linear Recreation? |
| What Is Interpolation and Extrapolation? |
| What is Power Analysis? |
| What is Q-Meaning? Can K Choose a K-Method? |
| What is the recommended system? |
| What is Linear Recreation? |
| What is TFT / ITF Vectation? |
| What is the Cluster Model? |
| What is the regulatory model? |
| What are the agenciers and the agenwals? |
| What is a Pilab? |
| What is PEP8? |
| What is the monkey grafting in Python? |
| What does it mean to understand the list? |
| What is the output of the code below? |
| What are the basic assumptions for linear backlash? |
| What is the benefit of dimension reduction before applying an SVM? |
| What is Data Science ? |
| What are the skills required in Data Science ? |
| What is Machine Learning ? |
| What is the difference between Traditional Programming and Machine Learning ? |
| What are the types of Machine Learning Algorithms? |
| What are the main components of a data science project ? |
| What percentage of time is usually required for each component in data science projects ? |
| What is Artifical Intelligence ? |
| What is Deep Learning (DL)? |
| What is Backpropagation? |
| What is Stochastic Gradient Descent? |
| What is Data Science? |
| What is the difference between iloc and loc activity? |
| What package is used to import data from the Oracle server? |
| What do the review process do? |
| What do Dummies do? |
| What is the curtain? |
| What is the removal of data backward in advance? |
| What is Unequal Data? |
| What is standardization? |
| What is Panda in Data Science and for which data it is suitable? |
| What is a p-value? |
| What is Data science? What is the role of Machine Learning in Data science? |
| What you mean by Type I error and Type II error in Hypothesis testing? |
| What is Logistic regression? How will you evaluate your Logistic regression model? |
| What is the difference between ANOVA and t-test? |
| What is the difference between Overfitting and Underfitting? |
| What are the steps involved in an analytics project? |
| What all are the main packages used in Python for Data science and Machine Learning? |
| What are the assumptions required for linear regression? |
| What is the difference between Covariance and correlation? |
| What is Gradient descent? |
| What is Regularization? |
| What do you mean by Imbalanced Classes? |
| What is bias – Variance trade off? |
| What are the evaluation metrics in Classification algorithm? |
| What are Ensemble, Bagging and Boosting? |
| What are the skills are required to learn the data science with respect to python? |
| What are the types of joins? |
| What are the Types of Request database Flask allows? |
| What are the Various Methods for Sequential Supervised Learning? |
| What are the areas Pattern recognition is used. |
| What are the supported data types in Python? |
| What is Flask?Is flask equivalent to MVC Model? |
| What are the types of Bias? |
| What are the Different Data Structures in R? |
| Name the commonly used algorithms. |
| Name few methods for Missing Value Treatments. |
| Name some Classification Algorithms. |
| Name some Python Libraries used in Machine Learning . |
| Name some supervised and unsupervised deep learning algorithms. |
| Name some Python libraries used in Deep Learning |
| Write code to sort a DataFrame in Python in descending order. |
| Write a code using Panda |
| Write syntax for creating sting variable? |
| write the types of Techniques of Machine Learning? |
| Write a query that returns the Details of each department and a count of the number of Students in each: |
| write the types of Techniques of Machine Learning? |
| Write a syntax, how you access a module written in Python from C |
| Write the Components of relational evaluation techniques. |
| Write the types of paradigms of ensemble methods? |
| Write a syntax, how you access a module written in Python from C |
| Write syntax for creating sting variable? |
| write the types of Techniques of Machine Learning? |
| Write program to convert uppercase little to lower case |
| Explain about from capture of the correlation between continuous and categorical variable? |
| Explain what is the regulation and why it is useful. Regularization? |
| Explain the use of decorators? |
| Explain supervised and unsupervised machine learning |
| Explain Confusion Matrix |
| Explain Normal Distribution |
| Explain Covariance and Correlation in Data Science |
| Explain Linear Regression |
| Explain Collaborative Filtering |
| Explain Python Dictionary |
| Explain Auto Encoder |
| Explain Rmarkdown |
| Explain K-Means clustering? |
| Explain why data cleaning is important in analysis ? |
| Explain split(), sub(), subn(methods of “re” |
| Explain the use of // Divisionoperator in Python? |
| Explain about Sequence Learning? |
| Explain that why data cleaning is important in analysis ? |
| Why you should use NumPy arrays instead of nested Python lists? |
| Why is an import statement required in Python? |
| Why is data important in data analysis? |
| Why data analysis is an important part of the analysis? |
| Why is a useful metric? |
| Why we need to use a python tuple is preferred over python list ? |
| Which metric acts like accuracy in classification problem statement? |
| Which Python library is used for data visualization? |
| Which function is used to get descriptive statistics of a dataframe? |
| Which function can be used to filter a DataFrame? |
| Which language is suitable for text analysis? R or Python? |
| Which tool should you use to find the bugs? |
| Which Python Library is used by Machine Leader? |
| Which symbol is used to add a comment in R language? |
| How and by what methods data visualizations can be effectively used? |
| How to understand the problems faced during data analysis? |
| How to choose the right chart in case of creating a viz? |
| How can I achieve accuracy in the first model that I built? |
| How do I enhance a SAS analyst? |
| How is F1 score is used? |
| How can you randomize the items of a list in place in Python? |
| How to get indices of N maximum values in a NumPy array? |
| How make you 3D plots/visualizations using NumPy/SciPy? |
| How to access a specific script inside a module? |
| How to create a series with letters as index? |
| How to convert n number of series to a dataframe? |
| How to select a section of a dataframe? |
| How are exceptions handled in Python? |
| How to differentiate from KNN and K-means clustering? |
| How to Clean Data is an Important Part of the Process? |
| How do Data Scientists use statistics? |
| How is Machine Learning Used in Real World Scenes? |
| How does data modeling change from database format? |
| How to sort items of the list in Python? |
| How do you see if a panda data information is empty or not? |
| How to Assign Code to the List? |
| How to read an Excel file without a file file in the Byndah? |
| How often you should update an algorithm? |
| How will you define supervised and unsupervised learning? |
| How will you evaluate your regression model based on R2, Adjusted R2 and tolerance? |
| How will you define your number of clusters in K-MeAnswer: clustering algorithm? |
| How kNN is different from K-MeAnswer: clustering? |
| How gradient descent is helpful in ML? |
| How would you create an empty NumPy array? |
| How would you make a Python script executable on Unix? |
| How would we can create an empty NumPy array? |
| How will you reverse a list? |
| How will you remove last object from a list? |
| How to find the best approximate solution to the knapsack problem1 in a given time by using best Algorithm |
| Where to seek help in case of discrepancies in Tableau? |
| Where we are mostly using naiveBayes algorithm for classification? |
| Who is a Data Scientist ? |
| Difference between supervised and unsupervised machine learning? |
| Difference between Machine learning and Data Mining? |
| Difference between an Array and a Linked list? |
| Difference between “long” and “wide” format data? |
| Difference between distinct, bivariate and multivariate analysis? |
| Difference between Supervised and unsupervised? |
| In R, how will you load a .csv file? |
| In R Language, provide the usage of Next statement |
| Advantages of Tableau Prep? |
| Algorithm for a sorting a number dataset in Python. |
| Are the aliases used for a module fixed/static ? |
| Can Random forest be used for classification and regression? |
| Can the values be replaced in tuple? |
| Can you briefly describe the scientific method? |
| Can you quote some examples of false positives that are more false than negative ones? |
| Can you cite some of the worst negative examples of negative negative than negative ones? |
| Can you quote some examples of both false positives and misinformation? |
| Can you explain the difference between a verification set and test set? |
| Can the formula be written to calculate the R-square? |
| Can you provide sample code for creating a data frame in order to perform slicing in Panda? |
| Can you explain few things about ShinyR? |
| Can you perform some comparison on R and Python which is useful in Data Science? |
| Can you write a R programming code? |
| Cite an example where both false negative and false positives has equal importance |
| Compare SAS, R, and Python programming? |
| Data Science Carrier |
| Data Science job areas |
| Definitions of is BY-Group processing? |
| Describe univariate, bivariate and multivariate analysis? |
| Describe the feature selection approaches that is used to pick the correct variables |
| Describe Batch, and Epoch in Deep Learning |
| Describe LSTM |
| Differences between overfitting and underfitting |
| Differentiate between univariate, bivariate and then multivariate analysis. |
| Differentiate between univariate, bivariate and multivariate analysis? |
| Do you know any SAS functions and Call Routines? |
| Do you explain the word Botnet? |
| Do you know the various components of graphics grammar in R? |
| Explian Naïve – Bayes algorithm? |
| For a categorical variable, what is the process to check frequency distribution? |
| Give examples of supervised and unsupervised ML algorithms. |
| Give me two important tasks in the pants? |
| Give me the steps for an analytics project |
| Give an example of optimizing a python code |
| Give us a pictorial representation of the Decision Tree algorithm in Data Science |
| Give example for unzipping. |
| If you provide employees’ first and last names, what type of data in Python stores them? |
| Import of Flat File / CSV in Baidan |
| Is multiprocessing possible in python? |
| Is it possible to merge two (2data frames in R? If yes, how is that done? |
| List out the different classification algorithms |
| List out the Kernel functions available in SVM |
| List out few functions that are available in dplyr package |
| List out the Supervised Learning Functions. |
| list having tweets, find 10 most used top hashtags. |
| Mention the characteristics of symmetric data distribution? |
| Mention few important skills to contain in Python with respect to Data Analytics |
| Mention the functions that are used to copy objects in Python |
| Mention Any Five Algorithms of Machine Learning. |
| Mention the Different types of sequence learning process? |
| Now companies are heavily investing their money and time to make the dashboards. Why? |
| Program for one-linear that will count the number of capital letters in a file. |
| Provide the Life cycle of Data Science |
| Provide the technical concepts handled in Supervised, Unsupervised and Reinforcement Learning |
| Provide the different types of Biases that occur during Sampling and give a one-line definition for each type. |
| Provide the various layers of CNN |
| Provide the Machine Learning libraries along with its benefits |
| Provide the basic steps to create a new R6 class |
| Provide an example for False Positive in Data Science |
| Provide the various Deep Learning frameworks |
| Scope and Applications of Statistics |
| The Difference Between Data Modeling and Database Design? |
| The performance of the K modular system? |
| What’ s the difference between a Regression and a Classification problem? |
| You should find that data is stored in HDFS format and how the data is structured. Which command should you use to identify the names of HDFS keys? |
| You are given a dataset and you have build a decision tree model on top of it. You got an accuracy of 98%. Why you shouldn’t happy with your model performance? |
01 January 2021
#Data_Science
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment