Most Frequently Asked Data Analyst Interview Questions
1. What are some of the data validation methodologies used in data analysis?
Many types of data validation techniques are used today. Some of them are as follows:
Field-level validation: Validation is done across each of the fields to ensure that there are no errors in the data entered by the user.
Form-level validation: Here, validation is done when the user completes working with the form but before the information is saved.
Data saving validation: This form of validation takes place when the file or the database record is being saved.
Search criteria validation: This kind of validation is used to check whether valid results are returned when the user is looking for something.
2.What is the difference between the concepts of recall and the true positive rate?
Recall and the true positive rate, both are totally identical. Here’s the formula for it:
Recall = (True positive)/(True positive + False negative)
3.What are the ideal situations in which t-test or z-test can be used?
It is a standard practice that a t-test is used when there is a sample size less than 30 and the z-test is considered when the sample size exceeds 30 in most cases.
4.How can one handle suspicious or missing data in a dataset while performing analysis?
If there are any discrepancies in data, a user can go on to use any of the following methods:
Creation of a validation report with details about the data in the discussion
Escalating the same to an experienced data analyst to look at it and take a call
Replacing the invalid data with corresponding valid and up-to-date data
Using many strategies together to find missing values and using approximation if needed
5.How can you use data analysis to optimize supply chain operations?
Data analysis can be used to optimize inventory management, streamline logistics, forecast demand, identify bottlenecks, and improve supplier relationships.
6.Explain how a recommendation system can contribute to increasing revenue in an e-commerce setting.
A recommendation system can drive revenue by personalizing user experiences, increasing engagement, promoting upsell and cross-sell opportunities, and improving customer retention.
7.How would you optimize a model in a real-time streaming data application?
Optimization could involve using lightweight models (e.g., linear models), employing model quantization or pruning, optimizing the data pipeline, and utilizing distributed computing resources.
8.Discuss a scenario where you'd prefer to use a Bayesian approach over frequentist statistics.
A Bayesian approach might be preferred when there’s a need to incorporate prior knowledge into the analysis, or when working with small datasets where the flexibility of Bayesian methods can provide more robust estimates.
9.Explain primary key and it's importance.
A primary key serves as a unique identifier for records in a database table, promoting data integrity and preventing duplicates. This important element enhances data retrieval efficiency, supports indexing, and facilitates table relationships in relational databases.
Here is an example demonstrating how we can set primary key while creating table:
10.How would you use Natural Language Processing (NLP) techniques in sentiment analysis of user reviews?
Sentiment analysis can involve pre-processing (tokenization, lemmatization, etc.), feature extraction (TF-IDF, word embeddings), and classification using machine learning models (SVM, Naive Bayes, LSTM).
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