Data Science Basics

What is data collection in the context of data science?

• Storing data in a secure location

• Gathering and measuring information on targeted variables

• Analyzing and interpreting data

• Creating visualizations for data

Why is data cleaning important in the data science process?

• To introduce errors and inaccuracies into the analysis

• To ensure that the data is accurate, complete, and reliable for analysis.

• To make the data more confusing and difficult to analyze

• To waste time and resources on unnecessary tasks

What are some common techniques used for data analysis in data science?

• Some common techniques used for data analysis in data science are regression analysis, classification, clustering, and data visualization.

• Machine learning, data cleaning, and statistical analysis

• Algebraic analysis, sorting, and data entry

• Text analysis, image recognition, and web scraping

Explain the importance of data visualization in data science.

• Data visualization makes data more confusing and difficult to analyze

• Data visualization has no impact on understanding patterns or trends in data

• Data visualization helps in understanding complex data by presenting it in a visual format, making it easier to identify patterns, trends, and outliers.

• Data visualization is only useful for presenting simple data, not complex data

What are the basic concepts of machine learning?

• Neural networks, support vector machines, natural language processing, and algorithms

• Supervised learning, unsupervised learning, deep learning, and algorithms

• Regression, classification, clustering, and decision trees

• Supervised learning, unsupervised learning, reinforcement learning, and algorithms

What are the different types of data collection methods used in data science?

• Surveys, interviews, observations, experiments, and existing data analysis

• Cooking recipes

• Weather forecasts

• Social media posts

What are some common challenges faced during data cleaning?

• Not correcting inconsistent data formats

• Creating more duplicate entries

• Handling missing data, dealing with duplicate entries, correcting inconsistent data formats, and identifying outliers

• Ignoring missing data

How does machine learning differ from traditional programming?

• Machine learning does not require any data to make predictions.

• Machine learning uses data to train models and make predictions.

• Machine learning uses pre-defined rules to make predictions.

• Traditional programming does not involve writing code.