Clarivate Data Scientist Interview Questions and Answers

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. NLP techniques, word stop,
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Q2. K-nearset neighout algorithm, gaussian mixtture model
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2 hours of coding with 2 Questions