Master data collection, organization, and protection while understanding bias, credibility, and data ethics.
Covers how analysts decide which data to collect for analysis, including structured and unstructured data, data types, and data formats. Explores identifying different types of bias in data to ensure credibility. Introduces using spreadsheets and SQL with databases and data sets, including open data concepts. Covers the relationship between data ethics and data privacy, best practices for accessing databases, extracting, filtering, and sorting data, and organizing and securing data properly.
5 minutes
5 Questions
Prepare Data for Exploration is a crucial phase in the data analytics process covered in the Google Data Analytics Certificate. This stage focuses on collecting, organizing, and ensuring data quality before analysis begins.
During this phase, analysts learn to identify appropriate data sources that align with business questions. Data can come from internal databases, spreadsheets, external APIs, or public datasets. Understanding where to find relevant information is essential for successful analysis.
Data preparation involves several key activities. First, analysts must assess data integrity by checking for accuracy, completeness, consistency, and trustworthiness. This includes examining whether data was collected properly and if it represents the population being studied.
Organizing data is another critical component. This means structuring information in formats suitable for analysis, such as converting files to appropriate types, establishing naming conventions, and creating logical folder structures. Proper organization ensures efficiency throughout the analytical process.
Data cleaning is a significant part of preparation. Analysts learn techniques to handle missing values, remove duplicates, correct errors, and standardize formats. Clean data produces more reliable insights and prevents flawed conclusions.
The course also covers data security and ethics. Analysts must understand how to protect sensitive information, maintain privacy, and follow organizational policies. This includes recognizing personally identifiable information and implementing appropriate safeguards.
Additionally, students learn about different data formats and structures, including wide versus long data, and how to transform between them based on analytical needs.
Documentation plays a vital role during preparation. Keeping records of data sources, transformations applied, and decisions made ensures reproducibility and transparency in the analytical process.
By mastering data preparation, analysts build a strong foundation for exploration and analysis. Well-prepared data leads to more accurate insights, better visualizations, and more compelling data-driven recommendations for stakeholders.Prepare Data for Exploration is a crucial phase in the data analytics process covered in the Google Data Analytics Certificate. This stage focuses on collecting, organizing, and ensuring data quality before analysis begins.
During this phase, analysts learn to identify appropriate data sources tha…