Data Wrangling and Preparation with Python and R

Data Analytics and Business Intelligence October 25, 2025
Enquire About This Course

Introduction

This course provides comprehensive training in data wrangling and preparation techniques using both Python and R programming languages. Participants will learn to handle messy, real-world data and transform it into clean, analysis-ready formats. The curriculum covers data cleaning, transformation, integration, and quality assurance processes essential for reliable analytics. Through practical exercises, learners will master the tools and techniques needed to prepare data for advanced analysis, visualization, and machine learning applications.

Objectives

Key learning objectives include:

  • Master data cleaning techniques in Python and R
  • Handle missing values and outliers effectively
  • Transform and reshape data for analysis
  • Merge and join datasets from multiple sources
  • Automate data preparation workflows
  • Implement data quality validation checks
  • Work with different data formats and structures
  • Optimize data preparation performance

Target Audience

  • Data scientists and analysts
  • Business intelligence professionals
  • Research analysts
  • Data engineers
  • Python/R developers working with data
  • Academic researchers
  • Marketing and customer analysts

Methodology

The course employs a hands-on approach with real-world datasets from various domains including finance, healthcare, and e-commerce. Participants work through scenarios simulating common data quality challenges. Case studies demonstrate end-to-end data preparation pipelines, while group activities focus on collaborative problem-solving for complex data issues. Individual exercises build proficiency in both Python and R, and mini-case studies present specific data cleaning challenges. Syndicate discussions explore best practices and alternative approaches.

Personal Impact

  • Enhanced ability to handle complex data quality issues
  • Improved proficiency in both Python and R for data preparation
  • Stronger problem-solving skills for data challenges
  • Increased efficiency in data cleaning workflows
  • Better understanding of data quality principles
  • Developed ability to automate data preparation tasks

Organizational Impact

  • Higher quality data for analysis and decision-making
  • Reduced time spent on data preparation tasks
  • Improved consistency in data processing
  • Enhanced reliability of analytical results
  • Standardized data preparation processes
  • Better utilization of analytical resources

Course Outline

Unit 1: Data Wrangling Fundamentals

Introduction to Data Preparation
  • Data wrangling lifecycle and best practices
  • Common data quality issues and challenges
  • Python vs R for data preparation
  • Essential libraries and packages overview

Unit 2: Data Import and Exploration

Data Loading Techniques
  • Reading CSV, Excel, and JSON files
  • Database connections and SQL integration
  • API data extraction
  • Web scraping for data collection
Initial Data Assessment
  • Data profiling and summary statistics
  • Identifying data types and structures
  • Missing value analysis
  • Data distribution examination

Unit 3: Data Cleaning Techniques

Handling Data Issues
  • Missing value imputation strategies
  • Outlier detection and treatment
  • Data type conversions
  • String cleaning and standardization
Data Quality Assurance
  • Duplicate detection and removal
  • Data validation rules implementation
  • Consistency checks across datasets
  • Data quality metrics and monitoring

Unit 4: Data Transformation

Data Reshaping
  • Filtering and subsetting data
  • Column operations and feature creation
  • Data aggregation and summarization
  • Pivoting and melting data frames
Advanced Transformations
  • Conditional transformations
  • Date and time manipulation
  • Text processing and pattern matching
  • Custom function development

Unit 5: Data Integration

Combining Datasets
  • Merge and join operations
  • Appending and concatenating data
  • Handling different key structures
  • Data integration best practices

Unit 6: Automation and Workflow

Process Automation
  • Scripting data preparation pipelines
  • Parameterized workflows
  • Error handling and logging
  • Performance optimization techniques

Ready to Learn More?

Have questions about this course? Get in touch with our training consultants.

Submit Your Enquiry