Exploring Data Science Basics in 60 Seconds

A data scientist uses data to solve problems, uncover patterns, and gain insights. They work in organizations that handle large data sets and use skills from statistics, programming, and machine learning. Their workflow includes understanding business issues, gathering and cleaning data, and using tools like Python or R for analysis. They communicate insights and recommendations to stakeholders, aiding in data-driven decision-making. Data science plays a crucial role in improving efficiency and making informed decisions in the modern data age.

What is the primary goal of a data scientist?

• To manage company finances

• To design new products

• To create websites

• To solve problems using data

What fields of knowledge does a data scientist typically draw from?

• Literature and Art

• Statistics, Computer Programming, and Machine Learning

• History and Geography

• Biology and Chemistry

What is the first step a data scientist takes in their daily routine?

• Communicating with stakeholders

• Choosing the right tools for modeling

• Understanding the business issues

• Cleaning the data

Why is it important to write down the hypothesis before proceeding with an experiment?

• To choose the right tools for modeling

• To communicate with stakeholders

• To ensure data is gathered efficiently

• To guide the data analysis process

Which programming languages are mentioned as tools for data modeling?

• HTML and CSS

• R and Python

• C++ and Java

• Swift and Kotlin

What is the purpose of using visualization tools in data science?

• To clean the data

• To make the data look more attractive

• To identify trends and insights

• To communicate with the IT department

How do data scientists validate their results?

• By ignoring outliers

• By asking their colleagues

• By using statistical tests

• By guessing

What might a data scientist recommend to a transport company?

• Solutions to make routes more efficient

• New office locations

• Employee training programs

• New marketing strategies

What has enabled better data-driven decisions in the modern age?

• The data age and data scientists

• Traditional marketing techniques

• The advent of the internet

• Social media