Data Engineering

Data Collection and Acquisition

• What you Need to Know

  • Data Source Integration

    • APIs and web scraping for data collection
    • Database connections and SQL query optimization
    • Real-time data streams and batch processing
    • Resources:
      • Web Scraping with Python - Data extraction from web sources
      • SQL for Data Science - Kaggle SQL course
      • Apache Kafka Tutorial - Real-time data streaming

  • Data Quality Assessment

    • Missing data patterns and imputation strategies
    • Outlier detection and anomaly identification
    • Data consistency and validation rules
    • Resources:
      • Data Quality Assessment - Data validation framework
      • Missing Data Analysis - Flexible Imputation of Missing Data
      • Outlier Detection Methods - Scikit-learn outlier detection

  • Ethical Data Collection

    • Privacy regulations and compliance (GDPR, CCPA)
    • Bias in data collection and sampling
    • Data consent and anonymization techniques
    • Resources:
      • GDPR Compliance Guide - European data protection regulation
      • Bias in Data Collection - Survey of dataset bias
      • Differential Privacy - Privacy-preserving data analysis

Data Preprocessing and Cleaning

• What you Need to Know

  • Data Cleaning Techniques

    • Handling missing values with advanced imputation
    • Duplicate detection and deduplication strategies
    • Data type conversion and format standardization
    • Resources:
      • Data Cleaning with Pandas - Comprehensive data cleaning guide
      • Advanced Imputation Methods - Scikit-learn imputation strategies
      • Data Validation - Schema validation for pandas

  • Data Transformation and Normalization

    • Feature scaling (standardization, normalization, robust scaling)
    • Log transformations and Box-Cox transformations
    • Handling skewed distributions and power transforms
    • Resources:
      • Feature Scaling Guide - Preprocessing and normalization
      • Power Transformations - Non-linear transformations
      • Distribution Transformations - Statistical transformations

  • Categorical Data Encoding

    • One-hot encoding and dummy variable creation
    • Label encoding and ordinal encoding
    • Target encoding and feature hashing
    • Resources:
      • Categorical Encoding - Advanced categorical encoding library
      • Encoding Techniques Comparison - Comprehensive encoding guide
      • High Cardinality Encoding - Kaggle feature engineering course

Feature Engineering and Selection

• What you Need to Know

  • Advanced Feature Creation

    • Polynomial features and interaction terms
    • Time-based features from datetime data
    • Text feature extraction (TF-IDF, word embeddings)
    • Resources:
      • Feature Engineering Techniques - Comprehensive feature engineering
      • Time Series Feature Engineering - Automated time series feature extraction
      • Text Feature Engineering - Text vectorization techniques

  • Dimensionality Reduction Techniques

    • Principal Component Analysis (PCA) implementation
    • Independent Component Analysis (ICA)
    • Feature selection using statistical tests
    • Resources:
      • PCA Mathematical Foundation - Principal component analysis tutorial
      • ICA Implementation - Independent component analysis
      • Statistical Feature Selection - Univariate feature selection

  • Feature Selection Algorithms

    • Recursive Feature Elimination (RFE)
    • LASSO regularization for feature selection
    • Mutual information and correlation-based selection
    • Resources:
      • Feature Selection Methods - Comprehensive selection techniques
      • LASSO Feature Selection - L1 regularization for selection
      • Mutual Information - Information-theoretic selection

Data Pipeline Architecture

• What you Need to Know

  • ETL Pipeline Design

    • Extract, Transform, Load (ETL) vs ELT architectures
    • Data pipeline orchestration and scheduling
    • Error handling and data quality monitoring
    • Resources:
      • Apache Airflow - Workflow orchestration platform
      • Luigi Pipeline - Python pipeline framework
      • Prefect - Modern workflow orchestration

  • Data Storage and Retrieval

    • Relational databases vs NoSQL for ML data
    • Data lakes and data warehousing concepts
    • Columnar storage formats (Parquet, ORC)
    • Resources:
      • Database Design for Analytics - PostgreSQL for data analysis
      • Apache Parquet - Columnar storage format
      • Data Lake Architecture - Modern data storage patterns

  • Batch vs Stream Processing

    • Batch processing with Apache Spark
    • Stream processing for real-time ML
    • Lambda and Kappa architectures
    • Resources:
      • Apache Spark Tutorial - Distributed data processing
      • Stream Processing Concepts - Kafka Streams tutorial
      • Lambda Architecture - Batch and stream processing

Big Data Technologies

• What you Need to Know

  • Distributed Computing Frameworks

    • Apache Spark for large-scale data processing
    • Hadoop ecosystem and HDFS storage
    • Dask for parallel computing in Python
    • Resources:
      • PySpark Tutorial - Spark with Python
      • Hadoop Tutorial - Distributed storage and computing
      • Dask Documentation - Parallel computing library

  • NoSQL Databases for ML

    • MongoDB for document storage
    • Cassandra for time-series data
    • Redis for caching and real-time features
    • Resources:
      • MongoDB for Analytics - Document database tutorial
      • Cassandra Data Modeling - Time-series database design
      • Redis for ML - In-memory data structures

  • Cloud Data Services

    • AWS S3, RDS, and Redshift for data storage
    • Google BigQuery for analytics
    • Azure Data Factory for ETL processes
    • Resources:
      • AWS Data Services - Cloud data platform overview
      • Google BigQuery - Serverless data warehouse
      • Azure Data Platform - Cloud data architecture

Data Validation and Quality Assurance

• What you Need to Know

  • Data Schema Validation

    • Schema definition and enforcement
    • Data type validation and constraints
    • Automated data quality checks
    • Resources:
      • Great Expectations - Data validation and documentation
      • Pandera - Statistical data validation
      • Cerberus - Lightweight data validation

  • Statistical Data Profiling

    • Distribution analysis and summary statistics
    • Data drift detection and monitoring
    • Correlation analysis and dependency detection
    • Resources:
      • Pandas Profiling - Automated data profiling
      • Data Drift Detection - Alibi Detect library
      • Statistical Analysis - SciPy statistical functions

Time Series Data Processing

• What you Need to Know

  • Time Series Preprocessing

    • Handling irregular time intervals and missing timestamps
    • Resampling and interpolation techniques
    • Seasonal decomposition and trend analysis
    • Resources:
      • Time Series Analysis - Forecasting: Principles and Practice
      • Pandas Time Series - Time series functionality
      • Statsmodels - Time series analysis library

  • Feature Engineering for Time Series

    • Lag features and rolling window statistics
    • Fourier transforms for frequency domain analysis
    • Time-based aggregations and seasonality features
    • Resources:
      • TSfresh - Automated time series feature extraction
      • Feature Engineering for Time Series - Time series feature creation
      • Seasonal Features - Cyclical feature encoding

Text Data Processing and NLP

• What you Need to Know

  • Text Preprocessing Pipeline

    • Tokenization, stemming, and lemmatization
    • Stop word removal and text normalization
    • Handling different languages and encodings
    • Resources:
      • NLTK Tutorial - Natural Language Toolkit
      • spaCy Documentation - Industrial-strength NLP
      • Text Preprocessing - Scikit-learn text processing

  • Text Feature Extraction

    • Bag of Words and TF-IDF vectorization
    • N-grams and character-level features
    • Word embeddings (Word2Vec, GloVe, FastText)
    • Resources:
      • TF-IDF Implementation - Term frequency analysis
      • Word2Vec Tutorial - Gensim word embeddings
      • FastText - Subword embeddings

Image Data Processing

• What you Need to Know

  • Image Preprocessing Pipeline

    • Image loading, resizing, and format conversion
    • Normalization and data augmentation techniques
    • Handling different image formats and color spaces
    • Resources:
      • OpenCV Python Tutorial - Computer vision library
      • PIL/Pillow Documentation - Python imaging library
      • Image Augmentation - Advanced image transformations

  • Feature Extraction from Images

    • Traditional computer vision features (SIFT, SURF, ORB)
    • Histogram-based features and texture analysis
    • Preprocessing for deep learning models
    • Resources:
      • Feature Detection Tutorial - Traditional CV features
      • Image Features - Scikit-image processing
      • Deep Learning Preprocessing - TensorFlow image preprocessing

Data Version Control and Experiment Tracking

• What you Need to Know

  • Data Versioning Systems

    • DVC for data and model versioning
    • Git-based workflows for data science
    • Reproducible data pipelines and lineage
    • Resources:
      • DVC Tutorial - Data Version Control system
      • Git for Data Science - Version control best practices
      • Data Lineage - Tracking data dependencies

  • Experiment Management

    • MLflow for experiment tracking and model registry
    • Weights & Biases for advanced experiment management
    • Neptune for collaborative ML development
    • Resources:
      • MLflow Tracking - Experiment logging and management
      • Weights & Biases - Experiment tracking and visualization
      • Neptune Documentation - ML experiment management

Ready to Develop Models? Continue to Module 3: Model Development to master advanced model building, neural network architectures, and algorithm optimization.