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 Course
|
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.
