Tredence Data Scientist Interview Questions, Process, and Tips

Q1. Interpretation of logistic regression
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (0/1, yes/no, true/false, etc.)

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function...read more
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Q1. Evaluation metrics, AU ROC Curve, explain one use case
Ans. 
AU ROC Curve is used to evaluate the performance of classification models by measuring the trade-off between true positive rate and false positive rate.
• AU ROC Curve is commonly used in binary classification problems.

• It helps in comparing different models based on their ability to distinguish between classes.

• The area under the ROC curve (AUROC) value closer to 1 indicates a better model performance.

• For example, in healt...read more
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Q1. General managerial questions
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Q1. How to handle missing values
Ans. 
Handle missing values by imputation, deletion, or using algorithms that can handle missing data.
• Impute missing values using mean, median, mode, or predictive modeling

• Delete rows or columns with missing values if they are insignificant

• Use algorithms like XGBoost, Random Forest, or LightGBM that can handle missing data
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Q2. Linear equations model metrics
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Q1. Precession vs recall?
Ans. 
Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.
• Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• Recall is the ratio of correctly predicted positive observations to all actual positives.

• Precision is important when the cost of false positives is high, while recall is important when the cost of fa...read more
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Q2. Clustering evaluation metrics
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1 hour - SQL, Python, ML, Deep Learning

Aptitude + Technical Questions

Coding Questions of SQL and DSA

Q1. Transpose a matrix in python and machine learning questions
Ans. 
To transpose a matrix in Python, use numpy.transpose() or the T attribute.
• Use numpy.transpose() function to transpose a matrix.

• Alternatively, use the T attribute of a numpy array.

• Example: np.transpose(matrix) or matrix.T
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Q2. What are numpy and pandas
Ans. 
NumPy is a library for numerical computing in Python, while Pandas is a data manipulation and analysis tool.
• NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

• Pandas offers data structures like DataFrame for easy data manipulation and analysis, with tools for reading and writing data from various file formats.

• Both librari...read more
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Q3. Visualization libraries in python
Ans. 
Python has various visualization libraries like Matplotlib, Seaborn, Plotly, and Bokeh.
• Matplotlib is a widely used library for creating static, interactive, and animated plots.

• Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics.

• Plotly is great for creating interactive plots and dashboards.

• Bokeh is another interactive visualization librar...read more
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Q4. Object Oriented Programing
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Data Science related

Q1. Asked about the projects
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Q2. Interview revolved around M.L based questions
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Q1. Asked to execute SQL questions
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Q2. Technical interview related to Data Science
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Q1. Project related questions, from CV
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Q1. On deep learning, from cv
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Q1. Basic HR questions like why you want to change, etc
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