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Data Analytics And Data Science Training Interview Questions Asked in Top Companies

Data Analytics And Data Science Training Interview Questions

Data Science combines statistics, programming, and domain expertise to extract insights from data.

Data Analytics focuses on analyzing datasets to support decision-making.

Structured data is organized in tables with rows and columns.

Unstructured data includes text, images, and videos without a fixed format.

A dataset is a collection of related data used for analysis.

A variable is a feature or attribute in a dataset.

Removing errors and inconsistencies from data.

Data points that are not recorded in a dataset.

A file storing tabular data separated by commas.

Representing data using charts and graphs.

Chart showing categorical data with bars.

Chart showing trends over time.

Chart showing frequency distribution.

Average value of data.

Middle value of sorted data.

Most frequent value in data.

Measure of data spread.

Relationship between two variables.

Selecting data based on conditions.

Arranging data in order.

Programming language used in data science.

Python library for data analysis.

Library for numerical computing.

Language for querying databases.

System storing organized data.

Extract, Transform, Load process.

Key performance indicator.

Visual display of metrics.

Very large datasets.

Summarizing past data.

Flow of data processing.

Tool for analysis and visualization.

Decisions based on data insights.

Structuring data relationships.

To derive insights and predictions.

Exploratory Data Analysis to find patterns.

Creating new variables for models.

Scaling data to range.

Scaling to mean 0, std 1.

Extreme data point.

Predicting continuous values.

Predicting categories.

Grouping similar data.

Learning from labeled data.

Learning without labels.

Model memorizes data.

Model misses patterns.

Evaluation table.

Correct positive ratio.

Detected positives ratio.

Balance of precision & recall.

Rule-based model.

Multiple decision trees.

Clustering algorithm.

Dimensionality reduction.

Choosing important features.

Model evaluation curve.

Area under curve metric.

Data over time.

Predicting future values.

Combining tables.

Central data storage.

Visualization tool.

Checking model performance.

Validating model accuracy.

Unequal class distribution.

Scaling skewed data.

Selecting subset of data.

Normalizing features.

Improving model parameters.

ML using neural networks.

Brain-inspired model.

Optimization algorithm.

Optimizing parameters.

Combining models.

Sequential model improvement.

Parallel model training.

Efficient boosting algorithm.

Time-series neural network.

Processing text data.

Splitting text.

Word vector representation.

Suggesting items.

Detecting unusual data.

Learning via rewards.

Managing ML lifecycle.

Data changes over time.

Relationship change.

Explainability method.

Comparing versions.

Big data processing tool.

Distributed storage system.

Raw data storage.

Transform before vs after load.

Managing pipelines.

Tracking performance.

Systematic error.

Unbiased results.

Understanding models.

Processing near source.

Managing data quality.

Impact of features.

Live data analysis.

Multi-system processing.

Automated ML process.
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