What you will learn in Process Data from Dirty to Clean Course
- Define different types of data integrity and identify risks to data integrity.
- Apply basic SQL functions to clean string variables in a database.
Develop basic SQL queries for use on databases.
Describe the process of verifying data cleaning results.
Program Overview
The Importance of Integrity
⏱️ 3 hours
- Explore methods to check data for integrity, including handling insufficient data and avoiding sampling bias.
Clean Data for More Accurate Insights
⏱️5 hours
- Learn the difference between clean and dirty data, and practice cleaning data in spreadsheets and other tools.
Data Cleaning with SQL
⏱️ 4 hours
- Use SQL to clean data from databases, exploring how SQL queries and functions can clean and transform data before analysis.
Verify and Report on Cleaning Results
⏱️ 2 hours
- Learn to verify that data is clean and report your data cleaning results, ensuring accuracy and transparency.
Optional: Add Data to Your Resume
⏱️ 3 hours
- Focus on building a resume that highlights your strengths and relevant experience in data analytics.
Course Challenge
⏱️ 3 hours
- Apply the skills learned in a hands-on project to process data from dirty to clean.
Get certificate
Job Outlook
Proficiency in data cleaning is crucial for roles such as Data Analyst, Business Analyst, and Data Scientist.
Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
Completing this course can enhance your qualifications for entry-level data analytics positions.
Specification: Process Data from Dirty to Clean
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FAQs
- The course teaches fundamental data cleaning principles that can be applied across platforms.
- Techniques like detecting missing values, duplicates, and formatting inconsistencies are relevant in both Excel and SQL.
- SQL modules introduce basic functions like JOINs, filtering, and aggregation for cleaning database tables.
- Skills can be adapted to other database systems (PostgreSQL, MySQL) or big data tools.
- Understanding these principles helps transition between small datasets in spreadsheets and larger datasets in databases.
- Covers data integrity checks, including identifying missing, duplicate, or inconsistent entries.
- Teaches validation techniques to ensure data accuracy and consistency.
- Introduces methods to spot potential bias or anomalies in data collection.
- Explains cleaning and standardizing inconsistent values across multiple columns or datasets.
- Prepares learners to maintain high-quality, trustworthy datasets for analysis.
- Teaches structured documentation of data cleaning steps using spreadsheets and SQL comments.
- Encourages maintaining step-by-step notes and logs of changes made.
- Reinforces the importance of reproducibility for team projects or professional reporting.
- Provides guidance to create audit-ready datasets, essential in professional environments.
- Prepares learners for collaborative analytics tasks and workflow transparency.
- Includes real-world scenarios like customer data cleaning, survey responses, and business datasets.
- Assignments simulate tasks commonly performed by entry-level data analysts.
- Exercises use spreadsheets and SQL to mirror professional work environments.
- Quizzes and projects reinforce hands-on practical applications.
- Helps learners gain confidence applying techniques in actual analytics roles.
- Part of the Google Data Analytics Professional Certificate; easily integrates with other courses.
- Combine cleaning skills with data organization, analysis, visualization, and reporting from other modules.
- Helps build a complete workflow from messy raw data to actionable insights.
- Prepares learners for entry-level analyst roles and professional projects.
- Enhances your resume and portfolio with practical, certificate-backed experience.
