HTC Global Services Data Conversion Specialist Interview Questions and Answers

Q1. My self Suresh I have data entry experience 3 years
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Q2. What do you know? your experience data entry? how is typing speed?
Ans. 
I have 3 years of experience in data entry with a typing speed of 70 words per minute.
• I have 3 years of experience in data entry.

• I am proficient in using Microsoft Excel and Word for data entry tasks.

• My typing speed is 70 words per minute, with a high level of accuracy.

• I have experience in entering large volumes of data accurately and efficiently.

• I am detail-oriented and able to meet tight deadlines for data entry proj...read more
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The first was a mcq based coding round for campus placement

This was a pairing coding round

Q1. Explain different unsupervised algo
Ans. 
Unsupervised algorithms are used to find patterns in data without labeled outcomes.
• K-means clustering: partitions data into K clusters based on similarity

• Hierarchical clustering: creates a tree of clusters based on similarity

• Principal Component Analysis (PCA): reduces dimensionality by finding orthogonal components

• Association rule mining: discovers interesting relationships between variables in large datasets
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Q1. Questions on Python datatypes, class, object, inheritance,
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Q2. Machine learning algorithms
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Machine learning algorithms are used to train models on data to make predictions or decisions.
• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve learning through trial and error.

• Examples of machine learning applications include image recognition, natural l...read more
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Q3. Sql queries and python coding questions
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Q1. Related to project
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Q1. Conceptual Questions on SQL and Python
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Q1. About the project
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Q2. Evaluation metrics in linear regression, Assumption of linear regression,
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Evaluation metrics and assumptions in linear regression
• Evaluation metrics in linear regression include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, and Adjusted R-squared.

• Assumptions of linear regression include linearity, independence, homoscedasticity, and normality of residuals.

• Example: MSE = sum((actual - predicted)^2) / n
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