Vodafone Idea Data Scientist Interview Questions and Answers

Q1. Basic ML questions were asked.
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Q1. Difference between Data Warehouse, Data Lakes and Tables
Ans. 
Data Warehouse stores structured data for reporting and analysis, Data Lakes store raw and unstructured data, Tables are basic data structures.
• Data Warehouse is used for storing structured data from various sources for reporting and analysis.

• Data Lakes store raw and unstructured data in its native format for future processing and analysis.

• Tables are basic data structures used to organize and store data in a structured ...read more
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Q2. Query difference betweeen Group by and Partition by
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Group by is used to group rows that have the same values into summary rows, while Partition by is used to divide the result set into partitions to which the function is applied separately.
• Group by is used with aggregate functions to group rows based on a column or set of columns.

• Partition by is used with window functions to divide the result set into partitions.

• Group by is used with SELECT statement, while Partition by...read more
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Q3. Machine Learning questions such as Regression, Trees etc
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Basic aptitude ques were asked

Q1. Projects discussion
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Q2. Probability questions
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Q1. Why Ericsson and 5 year plan
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Q2. Your strength and weakness
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Q1. Isolatn forest work? evalution metrics in laymann tems , pyspark basics , job lib
Ans. 
Isolation Forest is an anomaly detection algorithm that works by isolating outliers in a dataset.
• Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection.

• It works by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

• The number of splits required to isolate an outlier is used as a measure of its abnormali...read more
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Q1. Scenerio based data science questns , paramatric and non parametric data
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Q1. Statistics, python, machine learning, regression
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Q1. Statistics, machine learning, regression, python
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Q1. Tell me about yourself
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Q1. Deep discussion about project did in last year. Basics of statistics like regression, hypothesis testing etc. Machine learning algorithms. Python programming basics.
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Q1. Statistics, Machine Learning, Project -Easy to moderate questions
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Q1. ML questions and deep learning
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Q2. Project in your cv
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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