Data Science Projects quiz

Question 1

What is a data science project?

Accepted Answer

A structured investigation using data to answer questions or solve problems

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A project that builds websites using databases

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A school report written without any numbers

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A software engineering project with no data involved

Question 2

What does 'data collection' mean in a project?

Accepted Answer

Gathering raw information relevant to your research question

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Only downloading data from Kaggle websites

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Collecting physical objects for a museum

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Taking photos and turning them into videos

Question 3

What is 'data cleaning'?

Accepted Answer

Fixing errors, inconsistencies, and missing values before analysis

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Making data look pretty in a spreadsheet

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Deleting all the data you don't like

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Saving data files to the cloud

Question 4

What is a 'dataset'?

Accepted Answer

An organized collection of data in rows and columns

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A single number or data point

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A graph showing one variable

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A computer program that makes graphs

Question 5

What is the difference between 'training data' and 'test data'?

Accepted Answer

Training data is for learning; test data is unseen data to measure real performance

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Training data is newer; test data is older

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Training data is larger; test data is smaller

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Training data has labels; test data never has labels

Question 6

What is an 'outlier' in data?

Accepted Answer

A data point very different from the rest of the dataset

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Data that is inside a circle on a chart

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Data that comes from outside the country

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The last row in a spreadsheet

Question 7

What is 'correlation' and what common mistake does it lead to?

Accepted Answer

Correlation shows two variables move together, but does not prove one causes the other

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Correlation proves that one thing causes another

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Correlation is the same as coincidence

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Correlation only applies to positive relationships

Question 8

What is a 'regression' project in data science?

Accepted Answer

A project that predicts continuous number outputs like prices or sales

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A project that classifies images into categories

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A project that stores data in a relational database

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A project that reverses the direction of data flow

Question 9

What is 'underfitting' in machine learning?

Accepted Answer

When a model is too simple and misses real patterns in data

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When a model trains on too much data

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When data is processed too slowly

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When a model only works in one country

Question 10

What is 'Kaggle' and how do beginners use it?

Accepted Answer

A platform for data science competitions and learning

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A type of table chart for data

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A programming language for data analysis

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A cloud storage service for big data

Question 11

What is a 'Jupyter Notebook' used for?

Accepted Answer

An interactive environment mixing code, charts, and text in one document

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A physical notebook used in data science classes

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A database for storing large datasets

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A type of computer hardware for data processing

Question 12

What does a 'confusion matrix' show?

Accepted Answer

A grid showing how often a model correctly and incorrectly classified each category

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A graph showing how confused the model is in general

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A table listing all the errors in a dataset

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A chart used only for binary (yes/no) predictions

Question 13

What is 'feature engineering'?

Accepted Answer

Creating new informative columns from existing data to help the model

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Building physical sensors to collect new data

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Downloading additional features from the internet

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Renaming columns to make them clearer

Question 14

What is the goal of 'model evaluation'?

Accepted Answer

Measuring how well the model performs on unseen data using metrics

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Testing how fast the model runs

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Evaluating the quality of the raw dataset

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Deciding how many features to include in the model

Question 15

What is 'data imbalance' and why is it a problem?

Accepted Answer

When one category has far more examples than another, skewing model training

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When some columns have more data than others

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When data files are different sizes

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When datasets are collected at different times

Question 16

What is a 'recommendation system' and how does it work?

Accepted Answer

A system that suggests items based on past behavior and similar users

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A system that forces you to buy items

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A system for managing stock in a warehouse

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A search engine that finds the cheapest products

Question 17

What is 'natural language processing' (NLP) used for in projects?

Accepted Answer

Projects that involve computers understanding and processing human language

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Projects that use natural photographs for training

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Projects that use only numerical data, no text

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Projects that build physical language labs

Question 18

What is 'model drift' and why should data scientists monitor it?

Accepted Answer

When the real world changes and the model's learned patterns become outdated

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When a model slowly loses its internet connection

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When a model's file size grows too large

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When a programmer moves to a different team

Question 19

What open datasets can beginners use to practice?

Accepted Answer

Free datasets from Kaggle, UCI, data.gov, and scikit-learn are great for beginners

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You must buy datasets from companies to practice

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Data science practice requires real company data

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You must collect all your own data when learning

Question 20

What is a 'dashboard' in data science?

Accepted Answer

An interactive visual display of key data and metrics in one place

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A physical panel with buttons on a machine

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A type of database for fast queries

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A command-line tool for data scientists

Question 21

What does 'precision' mean as a model metric?

Accepted Answer

How often the model is correct when it predicts positive

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How accurately the model is calibrated in milliseconds

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The percentage of data that was correctly labeled

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The model's speed in making predictions

Question 22

How do data scientists communicate findings to non-technical audiences?

Accepted Answer

Using plain language, strong visuals, and real-world impact to tell the data story

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Sending raw numbers in an email

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Publishing the Jupyter notebook to all staff

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Showing every metric and chart without explanation

Question 23

What is 'Matplotlib' used for?

Accepted Answer

A Python library for creating charts and graphs

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A library for building machine learning models

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A tool for web scraping data from sites

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A database connection library for Python

Question 24

Why is reproducibility important in data science projects?

Accepted Answer

Getting the same result every time you run a project

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Running the same project on multiple computers simultaneously

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Reproducing copies of your dataset

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Making your project faster by running it again

Question 25

What is 'feature selection' and why does it matter?

Accepted Answer

Feature selection removes unhelpful or redundant columns to improve model performance

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Using every available feature always produces the best model

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Feature selection only applies to image datasets

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You should never remove features from a dataset

Question 26

What is 'data augmentation' and when is it used?

Accepted Answer

Creating modified versions of existing data to artificially increase dataset size and model robustness

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Downloading additional data from the internet

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Removing bad examples from the training dataset

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A technique for compressing large datasets

Test yourself