Data Preparation in Analytics

The video emphasizes the importance of data quality in analytics, introducing tools and practices for data cleaning and preparation. It covers data accessibility, preparation processes, handling missing data, and the significance of documentation. The video also provides resources for learning data access in Excel and Google Sheets, and recommends tools like Data Studio and Power BI. It concludes with a guide and exercise for data preparation.

Why is data quality crucial in analytics?

• It ensures faster data processing.

• It prevents flawed analysis results.

• It reduces the cost of data storage.

• It increases the speed of data collection.

Which of the following is NOT mentioned as a tool for accessing data?

• Tableau

• Google Sheets

• Data Studio

• Excel

What are the three main dimensions of the data preparation process?

• Data storage, data retrieval, data processing

• Data collection, data cleaning, data visualization

• Data understanding, data preparation, statistical pre-processing

• Data mining, data analysis, data reporting

What should you consider when familiarizing yourself with the data?

• Data cost, data location, and data owner

• Data color, data shape, and data texture

• Data format, data size, and data speed

• Data source, biases, and missing data

What is a key consideration when dealing with missing data?

• Ignore missing data if it's less than 5%.

• Ensure choices are backed by logical reasoning.

• Always replace missing data with zeros.

• Remove all missing data entries.

What is the purpose of data splitting in statistical preprocessing?

• To speed up data processing

• To test predictive applications

• To enhance data visualization

• To increase data storage capacity

Why is documentation important in data preparation?

• It helps in reducing data size.

• It prevents the bad data cycle.

• It speeds up data processing.

• It ensures data is stored securely.

What is a data dictionary used for?

• To visualize data trends

• To document transformations and assumptions

• To store raw data

• To encrypt sensitive data

What percentage of time is typically spent on data preparation in analytics projects?

• 100%

• 30-40%

• 10-20%

• 50-90%

What is the risk of not spending enough time on data preparation?

• More accurate predictions

• Faster data processing

• Flawed analysis results

• Increased data storage costs