Explore tutorials, revision guides, practice papers, and more to sharpen your data skills.

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📊 Datasets Used in This Guide

The study guides and practice paper sets used on this website are publicly available datasets from reputable sources such as Kaggle, GitHub, and UCI Machine Learning Repository. These datasets are selected for educational use and are freely accessible for learning purposes. If you have any questions, please feel free to contact us here or HERE.

🔗 Free Version – Linked Resources

In the free version of this guide, you’ll find:

• Direct links to all datasets hosted on external platforms
• Instructions for downloading and importing into Power BI
• Attribution to original data owners and sources

Note: You are responsible for reviewing and complying with each dataset’s license or usage terms when using the linked resources.

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📂 10 Free Open Data Sources – Used For Practice

Practice your skills with these free, easy-to-follow, readily available and accessible data sources.

📁 Show / Hide Sources 🔽

• • Kaggle Datasets
  • Google Dataset Search
  • UCI Machine Learning Repository
  • Data.gov (USA)
  • World Bank Open Data
  • FiveThirtyEight Datasets
  • EU Open Data Portal
  • Awesome Public Datasets
  • UN Data
  • OpenStreetMap (OSM)

• Kaggle Datasets
• Google Dataset Search
• UCI Machine Learning Repository
• Data.gov (USA)
• World Bank Open Data
• FiveThirtyEight Datasets
• EU Open Data Portal
• Awesome Public Datasets
• UN Data
• OpenStreetMap (OSM)

📄 Data Cleaning in Power BI – Practice Papers

Put your skills to the test with realistic practice tasks designed to reinforce each core data cleaning technique covered in the revision guide. These are hands-on and ideal for preparing for assessments or real-world use.

🧪Practice Task 1: Customer Contact Fix

 

📂 File Provided: customer-contacts.csv

 

📂 File Provided: contact-customers.csv

 

Skills Covered: Removing nulls, standardizing text, fixing column types.

 

✅ Task Instructions:

• Import the dataset into Power BI.

 

• Remove rows with missing email addresses.

 

• Convert all names to proper case (e.g., “john doe” → “John Doe”).

 

• Standardize phone numbers to the format (XXX) XXX-XXXX.

 

• Remove duplicate rows.

 

📝 Bonus: Add a column to label contacts missing a phone number as “Incomplete”.

 

🧪 Practice Task 3: Website Log Clean-Up

 

📂 File Provided: site-logs.csv

Skills Covered: Filtering rows, replacing errors, formatting columns, handling dates.

 

✅ Task Instructions:

• Remove any rows where “SessionID” is null.

 

• Replace error values in the “Duration” column with 0.

 

• Format “Visit Date” as a valid Date.

 

• Trim whitespace in the “User Agent” column.

 

• Remove duplicates.

 

📝 Bonus: Create a grouped summary of total sessions per browser type.

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🧪 Practice Task 4: Survey Response Cleanup

 

📂 File Provided:

📄 survey-data.xlsx

 

📂 File Provided:

📄 survey.data.xlsx

 

🧠Skills Covered: Conditional replacement, column renaming, filtering nulls, value mapping.

 

✅ Task Instructions:

• Replace all instances of “N/A” in the “Age” column with null.

 

• Remove responses where “Consent” is marked as “No”.

 

• Rename columns to be more descriptive (e.g., “Q1” → “Overall Satisfaction”).

 

• Map values in a new column: “Very Satisfied” = 5, “Satisfied” = 4, etc.

 

• Convert all responses to lowercase.

 

📝 Bonus: Create a “Cleaned Survey” query and load only selected columns.

 

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✅ Submission Guidelines

• Complete your practice using Power BI Desktop.

 

• Save your .pbix file with each task labeled in separate queries.

 

• Optional: Submit to your instructor/mentor for feedback or portfolio review.

⬇️ Download All Practice Files (.zip)

 

📚 A. Study Guides

• Data Cleaning Revision Guide

Minimalist Data Wrangling with Python – a free PDF/book covering core data cleaning techniques in Python (Pandas), ideal as a reference companion.

 

• Data Cleaning Practice Papers

Kaggle Notebooks – look up “data cleaning” on Kaggle to find shared notebooks and hands-on examples.

 

📝 B. Practice Tests & Mock Exams

 

🎯 1. Data Cleansing using Python – Free Online Practice Test (TestPrepTraining)

• Data Cleaning Practice Test Paper 1

Take a 100-question interactive test for Python-based data cleaning. (Note: some follow-up content is paid, but the initial practice is free.)

 

🎯 2. Data Science with Python – Free Practice Exam

• Data Cleansing using Python – Free Practice Test (Simplilearn)

 

▶️ 3. Theory & Practice of Data Cleaning Quiz – A 15-question multiple-choice quiz covering schema-level cleaning, outliers, duplicates, and more.

• Take the free quiz (Quiz-Maker)

 

▶️ 4. Data Cleaning Test – A 12-question, 20-minute test focusing on practical skills like duplicate removal and outlier detection.

• Try the Data Cleaning Skill Test (Alooba)

 

▶️ 5. Quiz: Data Exploration and Cleaning – A 50-question interactive quiz that reinforces data cleaning concepts learned in Python, including integrity checks and data validation.

• Start the Data Exploration & Cleaning Quiz (Educative)

 

✔️ 6. Data Science with Python – Free Practice Mock Exam

• Free 50-question Data Science with Python Mock Exam (Simplilearn) – It takes 60 minutes to complete, multiple choice format, unlimited reattempts. 

 

✔️ 7. Data Cleansing using Python – Free Practice Mock Test

• Take the free Python cleansing test (100 questions) (Testpreptraining) – A 100-question interactive test for Python-based data cleaning.

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