💿Data Visualization Unit 4 – Data Preprocessing & Exploratory Analysis

What's the Deal with Data Preprocessing?

• Involves preparing raw data for analysis and visualization by cleaning, transforming, and organizing it
• Ensures data quality, consistency, and usability for downstream tasks
• Includes handling missing values, outliers, inconsistencies, and irrelevant information
• Standardizes data formats, units, and scales for better comparability
• Combines data from multiple sources and resolves conflicts or duplicates
• Selects relevant features and creates new derived variables for analysis
• Splits data into training, validation, and test sets for machine learning tasks
• Enables more accurate and meaningful insights from the data

Cleaning Up the Mess: Data Cleaning Techniques

• Identifies and corrects errors, inconsistencies, and inaccuracies in the data
• Handles missing values by removing records, imputing values, or using advanced techniques (k-nearest neighbors, matrix factorization)
• Detects and removes duplicate records based on unique identifiers or similarity measures
• Standardizes data formats, such as date and time, across all records
• Corrects inconsistent or misspelled categorical values using mapping or fuzzy matching
• Removes irrelevant or redundant features that do not contribute to the analysis
• Validates data against predefined rules or constraints to ensure integrity
• Performs data type conversions (string to numeric) and ensures consistent data types across columns

Getting to Know Your Data: Exploratory Data Analysis

• Involves summarizing and visualizing data to gain insights and understand patterns, relationships, and anomalies
• Calculates descriptive statistics (mean, median, mode, standard deviation) to understand data distribution and central tendencies
• Identifies the shape of the data distribution (normal, skewed, bimodal) using histograms or density plots
• Examines relationships between variables using scatter plots, correlation matrices, or pair plots
• Detects outliers and extreme values using box plots, Z-scores, or isolation forests
• Analyzes categorical variables using frequency tables, bar charts, or pie charts
• Explores time-series data using line plots, moving averages, or decomposition techniques
• Generates hypotheses and identifies potential issues or areas for further investigation

Spotting Patterns: Statistical Summaries and Visualizations

• Summarizes data using measures of central tendency (mean, median) and dispersion (range, variance, standard deviation)
• Visualizes univariate distributions using histograms, density plots, or box plots
• Uses scatter plots or line plots to identify relationships between two continuous variables
• Creates heat maps or correlation matrices to examine relationships between multiple variables
• Employs bar charts, pie charts, or stacked bar charts for categorical data
• Identifies trends, seasonality, and irregularities in time-series data using line plots or decomposition techniques
• Detects clusters or groups in the data using scatter plots, k-means clustering, or hierarchical clustering
• Communicates findings effectively using clear and informative visualizations

Dealing with the Weird Stuff: Outliers and Missing Data

• Outliers are extreme values that deviate significantly from the majority of the data
  • Can be detected using statistical methods (Z-scores, interquartile range) or visual inspection (box plots, scatter plots)
  • May represent genuine anomalies or measurement errors
• Missing data occurs when values are not recorded or available for certain instances
  • Can be handled by deleting records, imputing values, or using advanced techniques (k-nearest neighbors, matrix factorization)
  • Imputation methods include mean, median, mode, or regression-based approaches
• Assesses the impact of outliers and missing data on the analysis and decides on appropriate treatment
• Documents the handling of outliers and missing data for transparency and reproducibility

Transforming Data: Scaling, Encoding, and Feature Engineering

• Scaling normalizes numerical features to a common range (0-1) or standard distribution (mean=0, std=1)
  • Ensures fair comparison and prevents features with larger values from dominating the analysis
  • Common techniques include min-max scaling, standardization (Z-score), and robust scaling
• Encoding converts categorical variables into numerical representations
  • One-hot encoding creates binary dummy variables for each category
  • Label encoding assigns integer values to categories
  • Ordinal encoding preserves the order of categories, if applicable
• Feature engineering creates new features from existing ones to capture domain knowledge or improve model performance
  • Includes mathematical transformations (logarithm, square root), interaction terms, or domain-specific calculations
  • Requires creativity, domain expertise, and iterative experimentation

Tools of the Trade: Software for Preprocessing and EDA

• Python libraries:
  • Pandas for data manipulation, cleaning, and exploration
  • NumPy for numerical computing and array operations
  • Matplotlib and Seaborn for data visualization
  • Scikit-learn for preprocessing, feature scaling, and encoding
• R packages:
  • dplyr for data manipulation and transformation
  • ggplot2 for creating informative and aesthetic visualizations
  • caret for preprocessing, feature selection, and model evaluation
• Other tools:
  • Tableau for interactive data exploration and visualization
  • Excel for basic data cleaning and analysis tasks
  • OpenRefine for data cleaning, transformation, and reconciliation

Putting It All Together: From Raw Data to Visualization-Ready

• Starts with acquiring raw data from various sources (databases, APIs, files)
• Performs data cleaning to handle missing values, outliers, and inconsistencies
• Conducts exploratory data analysis to understand patterns, relationships, and anomalies
• Applies data transformation techniques (scaling, encoding, feature engineering) to prepare the data for visualization
• Selects appropriate visualization techniques based on the data type, distribution, and relationships
• Creates clear, informative, and visually appealing plots, charts, or dashboards
• Iterates and refines the process based on insights and feedback
• Communicates findings and insights effectively to stakeholders