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I was interviewed in May 2024.
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
Background in statistics and machine learning
Experience in data analysis and visualization
Strong programming skills in Python and R
Ability to communicate complex findings to non-technical stakeholders
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
Maths involves the study of numbers, quantities, shapes, and patterns.
Stats involves collecting, analyzing, interpreting, and presenting data.
Maths is used to solve equations, calculate probabilities, and model real-world phenomena.
Stats is used to make informed decisions, draw conclusions, and test hypotheses.
Both ma...
Confusion matrix what are your job rolls explain me Gradient boosting algorithm?
I applied via Naukri.com and was interviewed in Feb 2024. There was 1 interview round.
Implemented a machine learning model to predict customer churn in a telecom company.
Collected and cleaned customer data including usage patterns and demographics
Used classification algorithms like Random Forest and Logistic Regression
Evaluated model performance using metrics like accuracy, precision, and recall
Diffie-Hellman algorithm is a key exchange protocol used to securely exchange cryptographic keys over a public channel.
It is based on the concept of discrete logarithm problem.
It involves two parties, Alice and Bob, who generate their own private and public keys.
The public keys are exchanged and used to generate a shared secret key.
The shared secret key is used for encryption and decryption of messages.
It is widely use...
What people are saying about NTT Data
I applied via Naukri.com and was interviewed before Jul 2021. There were 3 interview rounds.
NTT Data interview questions for designations
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
Context window helps LLMs capture dependencies between words in a sentence.
A larger context window allows the model to consider more context but may lead to increased computational complexity.
For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
top_k parameter is used to specify the number of top elements to be returned in a result set.
top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.
For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.
In natural language processing tasks, top_k can be used to limit the number of possible next words in a
Regex patterns in Python are sequences of characters that define a search pattern.
Regex patterns are used for pattern matching and searching in strings.
They are created using the 're' module in Python.
Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
Iterators are used to loop through elements in a collection, like lists or dictionaries
Tuples are similar to lists but are immutable, meaning their elements cannot be changed
Example of iterator: for item in list: print(item)
Example of tuple: my_tuple = (1, 2, 3)
Yes, I have experience working with REST APIs in various projects.
Developed RESTful APIs using Python Flask framework
Consumed REST APIs in data analysis projects using requests library
Used Postman for testing and debugging REST APIs
I applied via Recruitment Consulltant and was interviewed before Apr 2023. There were 2 interview rounds.
Different splitting criteria in decision trees include Gini impurity, entropy, and misclassification error. Random forest works better due to ensemble learning and reducing overfitting.
Splitting criteria in decision trees: Gini impurity, entropy, misclassification error
Random forest works better due to ensemble learning and reducing overfitting
Random forest combines multiple decision trees to improve accuracy and gener...
To generate embeddings on a data set, preprocess the data, choose a suitable embedding method, train the model, and extract the embeddings.
Preprocess the data by cleaning, tokenizing, and normalizing text data.
Choose a suitable embedding method such as Word2Vec, GloVe, or FastText.
Train the embedding model on the preprocessed data to learn the embeddings.
Extract the embeddings from the trained model to represent the da...
Case Study for Data Analysis for marketing company
Group Discussion on Case Study
I was interviewed in Aug 2024.
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