Bharat Financial Inclusion Data Scientist Interview Questions and Answers

Q1. Common ways to evaluate Time Series model
Ans. 
Common ways to evaluate Time Series model include AIC, BIC, RMSE, MAE, ACF, PACF, etc.
• Use Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to compare models

• Calculate Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to assess model accuracy

• Analyze Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) to check for autocorrelation in residuals
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Q2. Best ways to handle multicollinearity
Ans. 
Use techniques like feature selection, regularization, PCA, and VIF to handle multicollinearity.
• Perform feature selection to choose the most relevant variables for the model.

• Apply regularization techniques like Lasso or Ridge regression to penalize high coefficients.

• Utilize Principal Component Analysis (PCA) to reduce dimensionality and decorrelate variables.

• Check for Variance Inflation Factor (VIF) to identify highly ...read more
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Q1. Write a function taking input as string and output a dictionary which will give key as characters in these string and values as their frequency of occurrence
Ans. 
This function counts the frequency of each character in a given string and returns a dictionary with characters as keys and counts as values.
• Use a dictionary to store character counts.

• Iterate through each character in the string.

• Increment the count for each character in the dictionary.

• Example: Input 'hello' -> Output {'h': 1, 'e': 1, 'l': 2, 'o': 1}

• Consider using collections.Counter for a more concise solution.
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Q2. TF IDF in NLP
Ans. 
TF IDF is a technique used in NLP to measure the importance of a word in a document within a collection of documents.
• TF IDF stands for Term Frequency-Inverse Document Frequency.

• It is used to determine how important a word is in a document relative to a collection of documents.

• TF IDF is calculated by multiplying the term frequency (TF) of a word in a document by the inverse document frequency (IDF) of the word across al...read more
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Create data frame, make histogram

Q1. Explain one of your project
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Q2. Are you ok with work from office culture
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Q1. Any issue in earlier working company
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Q2. Notice period working request
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Q3. Salary backage request
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Multiple Choice Questions

Q1. Why do you want to join us
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Asked questions of finance and aptitude

Q1. What is machine learning?
Ans. 
Machine learning is a branch of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data.
• Machine learning is a subset of artificial intelligence that focuses on developing algorithms that can learn from and make predictions or decisions based on data.

• It involves training models on large datasets to recognize patterns and m...read more
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Q2. What do you know about SQL?
Ans. 
SQL is a programming language used for managing and manipulating relational databases.
• SQL stands for Structured Query Language

• It is used to retrieve and manipulate data in relational databases

• Common SQL commands include SELECT, INSERT, UPDATE, DELETE

• SQL can be used to create tables, indexes, and views

• Examples of SQL databases include MySQL, PostgreSQL, Oracle
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Q3. What you know about software Development?
Ans. 
Software development involves creating, designing, testing, and maintaining software applications.
• Software development includes coding, testing, debugging, and documenting software applications.

• Developers use programming languages like Java, Python, C++, etc. to write code.

• Agile and Waterfall are common software development methodologies.

• Version control systems like Git are used to manage code changes.

• Software developm...read more
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Q1. What are the algorithms you know in machine learning and their details ?
Ans. 
Various machine learning algorithms with brief details
• Supervised Learning: Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forest

• Unsupervised Learning: K-means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA)

• Reinforcement Learning: Q-Learning, Deep Q-Networks (DQN)

• Neural Networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), L...read more
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Q2. Python questions
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Coding test python & SQL

Q1. Communication level interview
Ans. 
Effective communication is crucial for data analysts to convey insights and collaborate with stakeholders.
• Use clear and concise language to explain complex data findings.

• Tailor your communication style to your audience; for example, use technical terms with data teams and layman's terms with non-technical stakeholders.

• Utilize visual aids like charts and graphs to enhance understanding of data trends.

• Encourage feedback ...read more
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Q2. One to one Discussion
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Q1. Python coding test
Ans. 
Python coding test for Data Analyst role focusing on data manipulation and analysis.
• Use libraries like pandas for data manipulation. Example: df = pd.read_csv('data.csv')

• Utilize NumPy for numerical operations. Example: np.mean(array)

• Employ Matplotlib or Seaborn for data visualization. Example: plt.plot(data)

• Implement functions for data cleaning and preprocessing. Example: df.dropna()

• Practice SQL queries for data extrac...read more
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Q2. Communication level
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