Introduction

Data cleaning and preparation are fundamental steps in the data science pipeline. High-quality data is essential for accurate analysis and machine learning model performance. This course will provide participants with the necessary tools, techniques, and best practices to clean, preprocess, and prepare data for analysis. Using a variety of tools such as Pandas (Python), dplyr (R), and specialized data cleaning libraries, participants will learn to handle missing data, outliers, duplicates, and inconsistencies, ensuring that their data is ready for further analysis or modeling.

Objectives

By the end of this course, participants will:

• Understand the importance of data cleaning and preparation in the data science process.
• Gain proficiency in using Python (Pandas) and R (dplyr) for data cleaning tasks.
• Learn how to handle missing data, outliers, and duplicates.
• Understand techniques for data transformation, normalization, and feature engineering.
• Become familiar with strategies for data validation, consistency checks, and creating reproducible workflows.
• Build an understanding of data preprocessing for machine learning and statistical analysis.

Who Should Attend?

This course is ideal for:

• Data analysts, data scientists, and business analysts who want to improve their data cleaning skills.
• Anyone transitioning into data science roles who needs to work with messy or unstructured data.
• Professionals who need to prepare data for machine learning, visualization, or statistical analysis.
• Individuals interested in learning best practices for data preprocessing and workflow automation.

Day 1: Introduction to Data Cleaning and Preparation

Morning Session: Introduction to Data Cleaning

• Why data cleaning is crucial: Impact on analysis, insights, and model performance
• Common data issues: Missing values, duplicates, inconsistent formatting, and outliers
• Data cleaning challenges: Handling large datasets, noisy data, and time-consuming tasks
• Overview of popular tools for data cleaning: Python (Pandas), R (dplyr), and other libraries
• The data cleaning pipeline: Steps from raw data to ready-to-analyze data
• Hands-on: Overview of a real-world messy dataset and understanding common issues

Afternoon Session: Data Loading and Inspection

• Loading data: Reading data from CSV, Excel, databases, and web APIs
• Basic data exploration: Previewing data, checking types, summarizing datasets
• Identifying and diagnosing issues: Missing values, outliers, and duplicates
• Tools for data inspection in Python (Pandas) and R (dplyr)
• Hands-on: Importing data, inspecting data quality, and identifying initial problems

Day 2: Handling Missing Data

Morning Session: Techniques for Handling Missing Data

• Understanding types of missing data: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR)
• Visualizing missing data: Identifying patterns in missing data with heatmaps and other techniques
• Techniques for imputing missing values: Mean/median imputation, regression imputation, and advanced methods
• Handling categorical data: Using modes, frequent categories, or machine learning imputation
• Deciding when to drop rows or columns with missing data
• Hands-on: Identifying and imputing missing data in real datasets using Python and R

Afternoon Session: Advanced Missing Data Techniques

• Predictive modeling for missing data imputation: Using machine learning to predict missing values
• Multiple imputation: A technique to handle uncertainty in missing data
• Handling missing data in time series and sequential datasets
• Best practices for documenting and validating data imputation strategies
• Hands-on: Implementing multiple imputation techniques and comparing results

Day 3: Data Transformation and Normalization

Morning Session: Data Transformation Basics

• Data scaling and normalization: Standardization (z-score), Min-Max scaling, and Robust scaling
• Handling skewed data: Log transformations and Box-Cox transformations
• Encoding categorical variables: One-hot encoding, label encoding, and ordinal encoding
• Feature extraction and creation: Deriving meaningful features from existing data
• Handling text data: Tokenization, stemming, and stop-word removal
• Hands-on: Applying data transformation techniques to clean and scale datasets

Afternoon Session: Feature Engineering and Data Aggregation

• Feature engineering techniques: Combining variables, creating interaction terms, and time-based features
• Data aggregation: Grouping, summarizing, and pivoting data for analysis
• Creating new features from dates/times, categorical data, and numerical data
• Handling multi-dimensional data: Flattening, reshaping, and pivoting data
• Hands-on: Creating new features from raw data and aggregating datasets for analysis

Day 4: Handling Outliers and Duplicates

Morning Session: Identifying and Dealing with Outliers

• Understanding outliers: Types of outliers (global vs. local) and their impact on data analysis
• Visualizing outliers: Box plots, scatter plots, and Z-scores
• Methods for handling outliers: Capping, transforming, or removing outliers
• Robust statistical techniques for dealing with outliers in modeling
• Impact of outliers in machine learning algorithms: Decision trees, SVM, and neural networks
• Hands-on: Identifying and handling outliers in real-world datasets

Afternoon Session: Duplicates and Data Consistency

• Identifying and removing duplicate rows: Techniques in Python and R
• Data consistency checks: Ensuring consistent formatting, spelling, and value types
• Detecting and fixing inconsistent data entry errors (e.g., date formats, currency symbols)
• Using regular expressions for text cleaning and standardization
• Hands-on: Identifying duplicates and fixing consistency issues in a dataset

Day 5: Best Practices and Automation for Data Cleaning

Morning Session: Automation and Reproducibility

• Automating repetitive data cleaning tasks using functions and libraries
• Creating reproducible data cleaning workflows with RMarkdown and Jupyter Notebooks
• Version control for data cleaning projects: Using Git and GitHub for tracking changes
• Documenting data cleaning steps: Keeping track of decisions, imputation methods, and transformations
• Creating reusable scripts for standard data cleaning tasks
• Hands-on: Building a fully automated data cleaning pipeline

Afternoon Session: Final Project and Course Wrap-Up

• Final project: Participants will work on a comprehensive data cleaning project, involving loading, cleaning, transforming, and preparing a dataset for analysis or machine learning
• Presentation of results: How to present cleaned data and describe the cleaning process
• Reviewing best practices: Key takeaways for ensuring clean, reliable data
• Q&A and further learning: Resources for deepening knowledge and staying up-to-date with new techniques
• Certification of completion for those who successfully complete the course and final project

Materials and Tools:

• Required tools: Python (Pandas, NumPy, scikit-learn), R (dplyr, tidyr), and Jupyter Notebooks
• Real-world datasets (e.g., Kaggle datasets, government databases)
• Access to cloud-based platforms for additional practice (optional)

Conclusion and Final Assessment

• Recap of key concepts: Data loading, cleaning, transformation, outliers, and missing data handling
• Final project presentations and peer feedback
• Certification of completion for those who successfully complete the course and demonstrate practical application of data cleaning techniques