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WorksheetsData Science Quiz
Total questions: 20
Worksheet time: 10mins
Which programming language is most widely used for data analysis, machine learning (ML), and artificial intelligence (AI)?
Python
R
SQL
Java
Which of the following is a popular library used with the R programming language for data visualization?
Pandas
ggplot2
MySQL
TensorFlow
A data scientist needs to perform statistical analysis and create visualizations. Which programming language and libraries should they consider using?
Python with Pandas and TensorFlow
R with ggplot2 and dplyr
SQL with MySQL and PostgreSQL
Java with Scikit-learn and caret
What type of database is MangoDB classified as?
Relation DB
NoSQL DB
Distributed file system
Data warehouse tool
If you need to store logs and JSON files, which database technology would be most appropriate?
MySQL/PostgreSQL
MangoDB
Hadoop HDFS
Apache Hive
Explain how the use case of Apache Hive differs from that of Hadoop HDFS in the context of big data management.
Apache Hive is used for storing big data, while Hadoop HDFS is used for querying large datasets.
Apache Hive is used for querying large datasets using SQL-like syntax, while Hadoop HDFS is used for big data storage.
Both Apache Hive and Hadoop HDFS are used for storing structured data.
Apache Hive is a NoSQL database, while Hadoop HDFS is a relational database.
Which tool is primarily used for data manipulation and analysis in Python?
Pandas (Python)
NumPy
Excel
Apache Spark
Which tool would you most likely use for creating pivot tables and performing basic data analysis?
Excel
Dask
Apache Spark
NumPy
If you need to process large-scale data and perform real-time analytics, which tool would be most appropriate?
Apache Spark
Pandas (Python)
Excel
NumPy
Dask is best suited for which of the following tasks?
Parallel computing for big datasets (Python-based)
Creating charts and graphs
Text mining
Database transactions
To distribute computations across multiple cores in Python for a very large dataset, which tool should you choose and why?
Dask, because it supports parallel computing for big datasets in Python.
Excel, because it is easy to use for basic analysis.
NumPy, because it is good for array operations.
Apache Spark, because it is only for real-time analytics.
For analyzing a massive dataset in real-time and handling large-scale data processing, which tool is most suitable due to its design for such tasks?
Excel, because it is user-friendly for basic analysis.
Apache Spark, because it is designed for large-scale data processing and real-time analytics.
NumPy, because it handles numerical computing.
Dask, because it is only for parallel computing.
A company wants to manipulate and analyze data using Python. Which tool should they use, and how does it support their needs?
NumPy, because it is for numerical computing.
Excel, because it is good for pivot tables.
Pandas (Python), because it is specialized for data manipulation and analysis in Python.
Apache Spark, because it is for real-time analytics.
Compare the primary use cases of Scikit-learn and XGBoost / LightGBM. How do they differ in their application within data science projects?
Scikit-learn is used for traditional ML algorithms, while XGBoost / LightGBM is used for structured data prediction.
Both are used for deep learning tasks.
Scikit-learn is for image recognition, XGBoost / LightGBM is for speech recognition.
Both are primarily used for quick prototyping of deep learning models.
What is the primary use case of Tableau in data visualization?
Web-based visual analytics
Drag-and-drop visual dashboards
Enterprise-level reporting
Line charts and histograms
If you need to create web-based visual analytics, which data visualization tool would be most appropriate?
Matplotlib / Seaborn
Tableau
Power BI
Plotly
Compare the use cases of Matplotlib/Seaborn and Plotly. How do their primary applications differ in data visualization?
Matplotlib/Seaborn is used for web-based analytics, while Plotly is for line charts.
Matplotlib/Seaborn is for line charts, heatmaps, and histograms; Plotly is for web-based visual analytics.
Both are used for enterprise-level reporting.
Both are used for drag-and-drop dashboards.
What is the primary use case of Google Cloud Platform (GCP) in data science?
Scalable analytics, ML model training
End-to-end ML pipeline
Track changes in code
Storage, cloud computing, model deployment
Which cloud platform provides Azure ML Studio for end-to-end ML pipelines?
Microsoft Azure
AWS (Amazon Web Services)
Google Cloud Platform (GCP)
GitHub
Jupyter Notebook is best described as a tool for:
Writing, running, and visualizing code in browser-friendly format
Tracking changes in code
Cloud computing and storage
Model deployment
