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Top 83 Data Processing Interview Questions
Entry Junior Mid Senior Expert
Data Processing Theoretical Questions
Q1:
What is Data Preprocessing? What preprocessing steps do you know?
Entry
Entry
Q3:
What is Data Binning?
Entry
Q4:
Related To: Feature Engineering
What's the difference between Feature Engineering vs. Feature Selection?
Related To: Feature Engineering Entry
Q5:
What is the Importance of Data Reduction?
Junior
Q6:
What is the difference between Feature Selection and Feature Extraction?
Junior
Q7:
Related To: Supervised Learning
What is Cross-Validation and why is it important in supervised learning?
Related To: Supervised Learning Junior
Junior
Junior
Q10:
Should your Test Data be Cleaned the same way that the Training Data is?
Junior
Junior
Q12:
What is the difference between Normalization and Scaling?
Junior
Junior
Junior
Q15:
Related To: Curse of Dimensionality, Dimensionality Reduction
Why is data more sparse in a high-dimensional space?
Related To: Curse of Dimensionality, Dimensionality Reduction Junior
Q16:
Related To: ML Design Patterns
Name some methods you know for Rebalancing a dataset using Rebalancing Design Pattern
Related To: ML Design Patterns Junior
Q17:
What is the difference between Non-dependency and Dependency-oriented data?
Junior
Q18:
Related To: ML Design Patterns, Feature Engineering
What are some recommended choices for Imputation Values?
Related To: ML Design Patterns, Feature Engineering Junior
Q19:
How do you encode Categorical Data which consists of both Ordinal and Nominal data types?
Mid Mid Q21:
How do you Normalize Real-Time Data?
Mid Q22:
Related To: K-Nearest Neighbors
If you are using k-Nearest Neighbors, what type of Normalization should be used?
Related To: K-Nearest Neighbors Mid Q23:
Is it a good idea to Clean the Data Automatically?
Mid Mid Q25:
What are some common steps in Data Cleaning?
Mid Q26:
Can you use different Normalization methods on different features?
Mid Q27:
What is the Normalization of a data?
Mid Q28:
Explain the two types of Data Reducing Algorithms
Mid Q30:
When would you use Equal Frequency Binning and when do you use Equal Width Binning?
Mid Q31:
What are some reasons that data goes Missing?
Mid Mid Mid Q34:
Explain some Encoding techniques for Categorical Data
Mid Q35:
Related To: Scikit-Learn
What's the difference between StratifiedKFold (with shuffle = True) and StratifiedShuffleSplit in Scikit-Learn?
Related To: Scikit-LearnStratifiedKFold (with shuffle = True) and StratifiedShuffleSplit in Scikit-Learn? Mid Mid Q37:
Related To: ML Design Patterns
Are there any problems with splitting data randomly into Training, Validation, and Test datasets?
Related To: ML Design Patterns Mid Q39:
How does K-fold Cross Validation work?
Mid Mid Mid Mid Q43:
Related To: Model Evaluation, Machine Learning
What are the difference between Type I and Type II errors?
Related To: Model Evaluation, Machine Learning Mid Q44:
Related To: Classification, Ensemble Learning, Bias & Variance
What's the difference between Bagging and Boosting algorithms?
Related To: Classification, Ensemble Learning, Bias & Variance Mid Mid Q46:
Related To: Decision Trees, Ensemble Learning
What are some disadvantages of using Decision Trees and how would you solve them?
Related To: Decision Trees, Ensemble Learning Mid Q47:
Name some best practices for working with Datasets
Mid Q48:
Related To: Statistics, Bias & Variance
When you sample, what potential Sampling Biases could you be inflicting?
Related To: Statistics, Bias & Variance Mid Mid Q50:
Related To: Feature Engineering, Neural Networks
How to choose the features for a Neural Network?
Related To: Feature Engineering, Neural Networks Mid Q51:
How can you create a model with a very Unbalanced dataset?
Mid Q52:
How do you find the Covariance Matrix from Missing Data?
Senior Senior Senior Q55:
Related To: Linear Regression
How would you address the problem of Heteroskedasticity caused for a Measurement error?
Related To: Linear Regression Senior Q56:
Related To: Statistics
How could I (statistically) find features that are more important than others?
Related To: Statistics Senior Q57:
Related To: Anomaly Detection, Linear Regression
How would you deal with Outliers in your dataset?
Related To: Anomaly Detection, Linear Regression Senior Q58:
How would you deal with an Imbalanced Dataset?
Senior Q59:
Related To: Classification
What's the difference between Random Oversampling and Random Undersampling and when they can be used?
Related To: Classification Senior Q60:
Related To: Classification, Model Evaluation
How would you use a Confusion Matrix for determining a model performance?
Related To: Classification, Model Evaluation Senior Senior Q62:
Related To: Linear Regression
Explain what is an Unrepresentative Dataset and how would you diagnose it?
Related To: Linear Regression Senior Q63:
When you want (and do not want) to scale or standardize a variable prior to model fitting?
Senior Q64:
Related To: Model Evaluation
What are some approaches to get a quantitative estimate of a model's Maximum Predictive Power given a certain level of noise?
Related To: Model Evaluation Senior Senior Senior Q67:
How you can extract useful features from Date and Time column if any?
Senior Senior Q69:
When would you use chi-Square or an ANOVA test?
Senior Q70:
How would you handle Missing Data and perform Data Imputation?
Senior Q71:
Compare Causation vs Correlation
Senior Q72:
How would you check the Quality of your Dataset?
Senior Senior Senior Q75:
What are some variations of Cross-Validation?
Senior Q76:
How do you check if the Missing Data is Missing at Random (MAR) or Not?
Expert Q77:
How would you normalise Longitude/Latitude feature?
Expert Q78:
Related To: ML Design Patterns, Feature Engineering
When would you use Fine-Tuning vs Feature Extraction in Transfer Learning?
Related To: ML Design Patterns, Feature Engineering Expert Q79:
Related To: ML Design Patterns, Model Evaluation, Neural Networks
Are there any troubles when using Early Stopping?
Related To: ML Design Patterns, Model Evaluation, Neural Networks Expert Expert Expert Q82:
Related To: Dimensionality Reduction
How does Normalization reduce the Dimensionality of the Data if you project the data to a Unit Sphere?
Related To: Dimensionality Reduction Expert Data Processing Practical Challenges
Q1:
Between Clean and Noisy data, which one has higher Entropy?
Mid 
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