Climate Connect Digital Data Science Manager Interview Questions and Answers

Q1. Difference between evar and prop
Ans. 
eVar is a conversion variable that captures values at the time of conversion, while prop is a traffic variable that captures values at the time of page view.
• eVar captures values at the time of conversion, while prop captures values at the time of page view.

• eVar is used to track conversion events, while prop is used to track traffic events.

• eVar is persistent across visits, while prop is not.

• Example: eVar can capture the...read more
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Q2. What is clustering and classification
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Clustering is grouping similar data points together while classification is assigning labels to data points based on their features.
• Clustering is unsupervised learning while classification is supervised learning.

• Clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

• Classification algorithms include decision trees, logistic regression, and support vector machines.

• Clustering is used for customer segm...read more
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Q1. Case study questions related to Data Science
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Q2. Why i want to join
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Q1. What is reason i am leaving previous company
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Q2. Team handling experience
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Q1. Quesrions related to Marketing mix modeling, Python programs, NLP and AWS were asked
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There were verbal, non verbal, reasoning , English and maths questions

Q1. Tell me about your project.
Ans. 
I worked on a project analyzing customer behavior using machine learning algorithms.
• Used Python for data preprocessing and analysis

• Implemented machine learning models such as decision trees and logistic regression

• Performed feature engineering to improve model performance
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Q2. What programming knowledge you have ?
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Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
• Proficient in Python for data analysis and machine learning tasks

• Experience with R for statistical analysis and visualization

• Knowledge of SQL for querying databases and extracting data

• Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
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Q1. Where do you stay ?
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I currently stay in an apartment in downtown area.
• I stay in an apartment in downtown area

• My current residence is in a city

• I live close to my workplace
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Q2. Tell me about you
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I am a data science enthusiast with a strong background in statistics and machine learning.
• Background in statistics and machine learning

• Passionate about data science

• Experience with data analysis tools like Python and R
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Q1. What is bias v variance tradeoff
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Q2. When should bagging v boosting techniques be used
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Basic DSA questions will be asked Leetcode Easy to medium

Q1. BERT vs LSTM and their speed
Ans. 
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For exa...read more
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Q2. What is multinomial Naive Bayes theorem
Ans. 
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the
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(51+52+53+......+100) =

Q1. M.s excel , red hat ,
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Q2. Introdution M.s word
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Q3. Linux , git , mavel , nexus.
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Q4. Sonar cube , aws , super putty.
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Q1. What are Large Language Models?
Ans. 
Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
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Q2. Do you know about RAGs?
Ans. 
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
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Q3. Which is the best clustering algorithm?
Ans. 
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
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