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Definition

Data analysis in data science

Data analysis is the process of collecting, cleaning, transforming, and modeling data to extract meaningful insights. It is a critical component of data science, which is a field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data.

The six steps of data analysis from the Google Data Analysis Certification

Google’s Data Analytics Certificate is a professional certificate program that teaches students the skills they need to become data analysts. The program covers six steps in the data analysis process:

1. Ask: The first step is to ask clear and concise questions about the data that need to be answered. This involves understanding the business goals and the specific problems that the data analysis is trying to solve.
2. Prepare: The second step is to prepare the data for analysis. This involves cleaning the data to remove errors and inconsistencies, and transforming the data into a format that can be easily analyzed.
3. Process: The third step is to process the data using statistical and machine learning algorithms to extract insights.This involves choosing the right algorithms for the specific questions that are being asked.
4. Analyze: The fourth step is to analyze the results of the data processing step to identify patterns and trends. This involves using visualization tools to create charts and graphs that help to understand the data.
5. Share: The fifth step is to share the results of the data analysis with others. This involves communicating the findings in a clear and concise way, and using storytelling techniques to make the data more engaging.
6. Act: The final step is to act on the insights that have been gained from the data analysis. This involves making recommendations to stakeholders about how to improve business operations or solve problems.

These six steps provide a comprehensive framework for conducting data analysis. By following these steps, data analysts can ensure that their analysis is rigorous, reliable, and actionable.

Example

A company wants to understand why its customers are churning (canceling their subscriptions). The company could use the six steps of data analysis to answer this question:

1. Ask: What are the key factors that are driving customer churn?
2. Prepare: The company would need to collect data on customer behavior, such as how often they use the product,what features they use, and how they interact with the company’s customer support team. The company would also need to clean and transform the data so that it is in a format that can be easily analyzed.
3. Process: The company could use statistical and machine learning algorithms to identify patterns and trends in the data. For example, the company could use a regression model to identify the factors that are most correlated with customer churn.
4. Analyze: The company could use visualization tools to create charts and graphs that help to understand the data.For example, the company could create a bar chart showing the percentage of customers who churned after using a particular feature.
5. Share: The company could share the results of the data analysis with its stakeholders, such as the product team, the marketing team, and the customer support team. The company could also publish a blog post or white paper sharing the findings of the analysis.
6. Act: The company could use the insights from the data analysis to improve its product and services. For example,the company could invest in improving the features that are most correlated with customer churn.

By following the six steps of data analysis, the company could gain valuable insights into the factors that are driving customer churn and take steps to reduce churn.