Briq Associate Data Analyst Interview Questions and Answers

The first round was easy which consist of basic aptitude questions including java,c,cpp,dbms,sql,reasoning,etc

Q1. Explain Data Analysis Life Cycle
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Q1. How will you research on a company
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Q2. What is pivot table and it's significance
Ans. 
A pivot table is a data summarization tool used in spreadsheet programs to analyze, summarize, and present data.
• Allows users to reorganize and summarize selected columns and rows of data

• Helps in analyzing trends, patterns, and relationships within the data

• Enables users to perform calculations, such as sums, averages, counts, etc., on the summarized data

• Provides a dynamic way to view and manipulate data for better decis...read more
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Q1. Tell about yourself and
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Q1. What about your self?
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Q2. Family background
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Q3. Power BI test and advanced excel
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Q4. Microsoft access test
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Q5. Python test and One to one discussion with super boss
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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 ?
Ans. 
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
Ans. 
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. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more
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Q2. Parameters of Decision Tree
Ans. 
Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
• Max depth: maximum depth of the tree

• Min samples split: minimum number of samples required to split an internal node

• Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')

• Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
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Q3. Explain any one of your project in detail
Ans. 
Developed a predictive model to forecast customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest to build the model

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

• Provided actionable insights to the company to reduce customer churn rate
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Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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Q1. SQL and pandas coding
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Q2. Resume projects deep dive
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