Data Cleaning & Preparation Techniques Training Course

This course focuses on the essential processes of cleaning, preparing, and transforming raw data into high-quality, usable formats for analysis, reporting, and decision-making. Participants will gain both theoretical knowledge and hands-on experience in identifying data issues, applying cleaning techniques, and building automated workflows that ensure data consistency, accuracy, and reliability.

Target Groups

• Data analysts and data scientists
• Business intelligence professionals
• Database administrators
• Data engineers and ETL developers
• Researchers and academics working with large datasets
• Students pursuing data analytics or computer science studies
• Professionals working with messy or inconsistent data sources

Course Objectives

By the end of this course, participants will be able to:

• Understand the importance of data cleaning and preparation in analytics.
• Identify and resolve common data quality issues.
• Apply techniques to handle missing, duplicate, and inconsistent data.
• Standardize, normalize, and transform datasets for usability.
• Automate data preparation workflows with modern tools.
• Ensure compliance with data governance and quality standards.
• Use Python, SQL, and BI tools for effective data cleaning.
• Integrate cleaned data into analytics, reporting, and machine learning pipelines.

Course Modules

Module 1: Introduction to Data Cleaning & Preparation

• Importance of clean data for analytics and decision-making
• Common challenges in raw datasets
• Data cleaning vs. data preparation vs. data wrangling
• Data quality dimensions (accuracy, consistency, completeness, timeliness)

Module 2: Identifying Data Issues

• Detecting missing values, duplicates, and outliers
• Recognizing inconsistent formats and data entry errors
• Profiling datasets for quality assessment
• Tools for data auditing and validation

Module 3: Handling Missing & Incomplete Data

• Deletion vs. imputation strategies
• Mean, median, mode, and advanced imputation methods
• Interpolation and predictive imputation techniques
• Best practices for handling incomplete datasets

Module 4: Removing Duplicates & Inconsistencies

• Identifying duplicate records across large datasets
• Fuzzy matching and record linkage techniques
• Normalization and standardization methods
• Ensuring data integrity across multiple sources

Module 5: Data Transformation & Standardization

• Data type conversions and reformatting
• Standardizing units, currencies, and naming conventions
• Encoding categorical data
• Normalization and scaling for machine learning

Module 6: Data Cleaning with SQL & Spreadsheets

• SQL queries for missing values and duplicates
• Data validation in Excel and Google Sheets
• Using advanced SQL functions for transformation
• Practical exercises with relational databases

Module 7: Data Cleaning with Python (Pandas, NumPy)

• Introduction to data cleaning libraries in Python
• Handling missing and duplicate values in Pandas
• String manipulation and formatting
• Automating cleaning workflows with Python scripts

Module 8: Data Preparation for Analysis & Machine Learning

• Feature engineering basics
• Splitting datasets for training and testing
• Data balancing and resampling methods
• Preparing time-series and text data

Module 9: Tools for Data Cleaning & Preparation

• Overview of popular tools: OpenRefine, Trifacta, Power Query
• BI integration: Tableau Prep, Alteryx, KNIME
• Cloud-based preparation tools (AWS Glue DataBrew, Google Dataprep)
• Choosing the right tool for organizational needs

Module 10: Case Studies & Best Practices

• Case study: cleaning customer and sales data
• Case study: preparing survey and text data for analysis
• Data governance and compliance considerations
• Future of data preparation: automation and AI-driven cleaning