Introduction to Data Science

What is data science?

• D. The study of ancient civilizations

• A. The study of plants and animals

• B. The combination of math, statistics, programming, analytics, AI, and machine learning to uncover insights in data

• C. The art of painting and drawing

What is the role of a data scientist?

• C. To design buildings and structures

• B. To interpret data and provide actionable recommendations to improve business outcomes

• D. To write novels and poems

• A. To perform surgeries in hospitals

What is the data science lifecycle?

• D. Data ingestion, data storage and processing, data analysis, and communication

• B. Data collection, data visualization, data interpretation, and data storage

• A. Data ingestion, data storage, data analysis, and communication

• C. Data cleaning, data transformation, data modeling, and data presentation

What are some common tools used in data science?

• A. Hammer, screwdriver, and wrench

• B. R Studio, Python, SAS, and IBM SPSS

• C. Paintbrush, canvas, and easel

• D. Calculator, ruler, and protractor

What are some responsibilities of a data scientist?

• C. Building furniture and fixing appliances

• B. Applying statistics and computer science to data analysis

• D. Writing novels and poetry

• A. Cooking meals and baking desserts

How can cloud computing benefit data science projects?

• D. By increasing the cost of data storage

• A. By limiting access to processing power and storage

• C. By slowing down data processing jobs

• B. By providing additional processing power, storage, and tools required for data science projects

What are some common data science use cases?

• A. Building sandcastles and flying kites

• D. Painting and sculpting

• B. Process optimization through intelligent automation and enhanced targeting and personalization

• C. Gardening and landscaping

What are some popular programming languages used in data science?

• C. Chinese, Japanese, and Korean

• D. English, Italian, and Russian

• B. R Studio, Python, and SAS

• A. French, Spanish, and German

What is the purpose of data visualization in data science?

• A. To confuse decision-makers

• B. To make insights and their impact on business easier to understand

• C. To hide information from stakeholders

• D. To complicate the data analysis process

How does data visualization help in communicating insights effectively in data science?

• A. By making the data more confusing

• B. By simplifying complex information for better understanding

• C. By hiding important details from stakeholders

• D. By complicating the analysis process