Ushur Technologies Data Scientist Interview Questions and Answers

Basic python sql questions, mcq based and coding questions.

Q1. They asked the projects which I am working on and had discussion on the same. Asked few statistical questions like boxplots and normal distribution. Also asked basic questions on advance SQL
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Q1. Problem solving questions. Gave a case study to check presence of mind
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Q1. Basic salary discussion
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Q1. Explain Transformers how different from previous RNN, LSTM etc.
Ans. 
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
• Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.

• Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.

• Transformers have been shown ...read more
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Q2. What are different types of Attention?
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Different types of Attention include self-attention, global attention, and local attention.
• Self-attention focuses on relationships within the input sequence itself.

• Global attention considers the entire input sequence when making predictions.

• Local attention only attends to a subset of the input sequence at a time.

• Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
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Q3. Difference between GPT and BERT model
Ans. 
GPT is a generative model while BERT is a transformer model for natural language processing.
• GPT is a generative model that predicts the next word in a sentence based on previous words.

• BERT is a transformer model that considers the context of a word by looking at the entire sentence.

• GPT is unidirectional, while BERT is bidirectional.

• GPT is better for text generation tasks, while BERT is better for understanding the cont
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Q1. Difference between Data scientist, ML and AI
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Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
• Data scientists analyze large amounts of data to uncover patterns and insights.

• Machine learning involves developing algorithms that improve automatically through experience.

• Artificial intelligence r...read more
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Python sql basic questions

Python coding and ML resume based questions

Q1. Past Projects with the Director
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Q2. Behavioral Round questions Mostly
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First round is coding round where two use cases are there. Need to solve them

Q1. They will all topics Statistics, SQL, Python, Machine Learning, Data Science
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Q1. They will discuss more on the projects what we worked on
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Q1. Salary Discussion
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Q1. Questions on Transformer Architecture and
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Q2. System design tradeoffs and basic principles
Ans. 
System design tradeoffs involve balancing various factors to optimize performance and efficiency.
• Consider scalability, reliability, latency, and cost when designing systems

• Tradeoffs may involve sacrificing one aspect for the benefit of another

• Examples include choosing between consistency and availability in distributed systems
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Q1. Various questions on my projects
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Q2. NLP based questions and metrics calculation and case study
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Q1. Basic HR questions
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Q2. Why Fractal, etc
Ans. 
Fractals are used in data science for analyzing complex and self-similar patterns.
• Fractals are useful for analyzing data with repeating patterns at different scales.

• They are used in image compression, signal processing, and financial market analysis.

• Fractal analysis can help in understanding the underlying structure of data and making predictions.
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Q1. About project and question about ml
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Q1. Question deploymnet process and ci&cd process
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Basic python and ML test.

Q1. Explain so and so about the projects in the past
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Q2. ML algorithms in detail
Ans. 
ML algorithms are tools used to analyze data, make predictions, and learn patterns from data.
• ML algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of supervised learning algorithms include linear regression, decision trees, and support vector machines.

• Examples of unsupervised learning algorithms include k-means clustering, hierarchical clustering, and principal component an...read more
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Q3. Statistics formula and concept.
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Q4. Deep learning questions to improve.
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Q5. NLP related like Transformers.
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