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Data Science Trainer Interview Questions and Answers

Updated 4 Aug 2024

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Q1. Difference between pass and continue ? What is try-except ? What is semi supervised ML , difference between supervised and unsupervised ML, Difference between regression and classification , explain any one ML...

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Ans.

Questions related to programming and machine learning concepts.

  • pass and continue are control statements in Python. pass does nothing and continue skips the current iteration of a loop.

  • try-except is a block of code used to handle errors and exceptions in Python.

  • Semi-supervised ML is a type of machine learning where some data is labeled and some is not. Supervised ML uses labeled data while unsupervised ML uses unlabeled data.

  • Regression is used to predict continuous values whil...read more

Q2. What are the functions you have used in pandas library?

Ans.

Pandas library functions are used for data manipulation and analysis in Python.

  • Data manipulation functions like merge(), concat(), and pivot_table() are commonly used in pandas.

  • Data analysis functions like groupby(), describe(), and value_counts() are also frequently used.

  • Functions like read_csv() and to_csv() are used for reading and writing data from/to files.

Data Science Trainer Interview Questions and Answers for Freshers

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Q3. How much you confident with the syllabus ?

Ans.

I am very confident with the syllabus.

  • I have thoroughly studied and prepared the syllabus.

  • I have experience teaching the topics covered in the syllabus.

  • I am up-to-date with the latest developments in the field.

  • I am confident in my ability to explain complex concepts in a simple manner.

Q4. How will you check accuracy of ML model?

Ans.

Accuracy of ML model can be checked using metrics like confusion matrix, accuracy score, precision, recall, F1 score.

  • Calculate confusion matrix to see true positives, true negatives, false positives, and false negatives.

  • Use accuracy score to measure overall correctness of the model.

  • Calculate precision to see the ratio of correctly predicted positive observations to the total predicted positives.

  • Calculate recall to see the ratio of correctly predicted positive observations to ...read more

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Q5. Do you know SVM ? Explain

Ans.

SVM stands for Support Vector Machine, a supervised learning algorithm used for classification and regression analysis.

  • SVM finds the best possible boundary between two classes of data points

  • It works by mapping data points to a high-dimensional feature space and finding the hyperplane that separates the classes

  • SVM can handle both linear and non-linear data

  • It is widely used in image classification, text classification, and bioinformatics

Q6. Python and SQL coding Ml algorithm

Ans.

Python and SQL coding are essential for data science. ML algorithms are used for predictive modeling.

  • Python is used for data cleaning, manipulation, and visualization.

  • SQL is used for data querying and manipulation in databases.

  • ML algorithms are used for predictive modeling and classification tasks.

  • Examples of ML algorithms include linear regression, decision trees, and neural networks.

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Q7. White noise in time series

Ans.

White noise is a random signal with equal intensity at all frequencies.

  • White noise is a type of stationary stochastic process.

  • It has a constant power spectral density.

  • It is often used as a reference signal for analyzing other signals.

  • Examples include the sound of static on a TV or radio, or the sound of waves crashing on a beach.

  • White noise can be generated using a random number generator.

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