Fractal Analytics Data Scientist Interview Questions, Process, and Tips

There are 10 multiple-choice questions (MCQs) on Python, 20 MCQs on machine learning (ML), and 10 questions on deep learning (DL).

Q1. The technical round was divided in three phases - phase -1 : intro and professional projects They asked about the projects I have contributed in my full-time tenure. Then, asked me to pick any one of them...read more
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15 MCQ, 2 coding round

Q1. What is PCA explain eigen values
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Q2. How chatbot works really
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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
Ans. 
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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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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