Clarivate Data Scientist Interview Questions and Answers

Q1. Asked about some machine learning concepts like NLP, TensorFlow etc
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180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study

Q1. Explain Apriori Method
Ans. 
Apriori method is a popular algorithm for frequent itemset mining in data mining.
• Used for finding frequent itemsets in transactional databases

• Based on the concept of association rule mining

• Involves generating candidate itemsets and pruning based on support threshold

• Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent
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Q2. Explain train-test in Scikit learn
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Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.
• Split the dataset into two subsets: training set and testing set

• Training set is used to train the model, while testing set is used to evaluate the model's performance

• Common split ratios are 70-30 or 80-20 for training and testing sets

• Example: X_train, X_test, y_train, y_test = train_test_split(X, y,
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Q1. Explain about projects in current company
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Q2. Why do you want to move to an individual contributor role from a managerial position
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Q1. Why do you want to join Wolters Kluwer?
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Q2. Discussion around analytics and managerment reporting deliverables in current org.
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Explain dynamic programming with memoization

Q1. Where are you from, and why are you joining the company
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Q2. Why are you joining the company
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Q1. Identify geometric alorithm pattern
Ans. 
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
• Identifying and solving problems related to points, lines, angles, and shapes

• Utilizing geometric formulas and theorems to find solutions

• Examples include calculating area, perimeter, angles, and distances in geometric figures
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Q2. If minimal data, which would you train for categorical prediction model?
Ans. 
I would train a decision tree model as it can handle categorical data well with minimal data.
• Decision tree models are suitable for categorical prediction with minimal data

• They can handle both numerical and categorical data

• Decision trees are easy to interpret and visualize

• Examples: predicting customer churn, classifying spam emails
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Q1. What's the difference between k means and knn
Ans. 
K-means is a clustering algorithm while KNN is a classification algorithm.
• K-means is unsupervised learning, KNN is supervised learning

• K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors

• K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)

• Example: K-means can be used to group customers based on purc...read more
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Q1. Asked from resume about RAG
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