Exploring the Fundamentals of Data Science

The video discusses the essence of data science, emphasizing its role in creating impact for companies. It traces the evolution from data mining to data science, highlighting the rise of big data and machine learning. The video clarifies misconceptions, explaining that data science is not just about advanced models but solving real problems. It outlines the hierarchy of data science needs and describes roles in different company sizes, from startups to large corporations.

What is the primary goal of data science according to the introduction?

• Writing advanced code

• Designing visualizations

• Creating impactful insights for companies

• Building complex models

What is NOT a direct application of data science mentioned in the video?

• Creating digital art

• Product recommendations

• Insight generation

• Machine learning

Who played a key role in evolving data mining into data science by integrating computer science?

• Albert Einstein

• William S. Cleveland

• John McCarthy

• Geoffrey Hinton

What was the term used before 'data science' became popular?

• Information science

• Statistical learning

• Data mining

• Data analysis

What technological advancement is NOT mentioned as a solution to handle big data?

• Blockchain

• Spark

• MapReduce

• Hadoop

Which of the following is crucial for handling big data, as mentioned in the video?

• Antivirus software

• Cloud storage

• Quantum computing

• Parallel computing technology

According to the video, what is a common misconception about data science?

• It's the same as data mining

• It's only about coding

• It doesn't require statistical knowledge

• It's mainly focused on AI and machine learning

What does the industry prioritize in hiring data scientists, according to the video?

• Advanced machine learning skills

• Ability to generate impactful insights

• Proficiency in coding

• Expertise in data visualization

In a startup, what is the scope of a data scientist's responsibilities?

• Handling a wide range of tasks from infrastructure to A/B testing

• Exclusively working on data visualization

• Limited to data collection

• Focused on AI and deep learning

For large companies, how are data science tasks typically divided?

• One data scientist does everything

• Tasks are split between data engineers and data scientists

• Focus is only on AI and deep learning

• All employees must learn data science