Data Scientist — Data Engineering & Pipelines

[Description] Master advanced data transformation techniques and feature engineering methodologies to prepare data for complex machine learning models. Explore advanced topics like: data quality, anomaly detection, time series analysis, and advanced feature selection. - Resources/Activities: - Read papers on advanced feature engineering techniques (e.g., interaction features, polynomial features, and domain-specific feature engineering). - Implement a data quality monitoring and anomaly detection system using libraries like PyOD or similar. - Explore advanced time series analysis techniques like ARIMA, SARIMA, and Prophet for feature generation. - Apply advanced feature selection methods (e.g., using permutation importance and SHAP values). - Expected Outcomes: - Proficiency in advanced data transformation and feature engineering techniques. - Ability to detect and handle data quality issues. - Expertise in using advanced time series analysis methods for feature generation. - Skill in selecting optimal features for machine learning models.

[Description] Focus on performance tuning and scaling data pipelines to handle large datasets and high-velocity data streams. Learn about techniques like: parallel processing, data compression, query optimization, and resource management. - Resources/Activities: - Study performance optimization techniques for Apache Spark (e.g., data partitioning, caching, and tuning configurations). - Implement a data pipeline using a cloud-based data lake (e.g., AWS S3, Azure Data Lake Storage, or Google Cloud Storage) and explore optimization strategies. - Learn about data compression techniques like Snappy, Gzip, and Parquet. - Experiment with different query optimization strategies for your chosen database or data processing engine. - Expected Outcomes: - Mastery of performance tuning techniques for data pipelines. - Understanding of how to optimize for cloud-based data lakes. - Ability to choose and implement appropriate data compression techniques. - Skill in optimizing queries for faster data retrieval and processing.

[Description] Explore data governance best practices, data security measures, and compliance requirements in the context of data pipelines. Learn about data lineage, access control, encryption, and privacy regulations. - Resources/Activities: - Study data governance frameworks and best practices. - Implement data lineage tracking in your data pipelines. - Learn about data encryption techniques and implement data security measures to protect sensitive data. - Research and understand relevant data privacy regulations (e.g., GDPR, CCPA). - Use tools for access control and auditing. - Expected Outcomes: - Understanding of data governance principles and best practices. - Ability to implement data lineage tracking and security measures in data pipelines. - Knowledge of data privacy regulations and compliance requirements.

[Description] Focus on building robust monitoring and alerting systems for data pipelines to ensure data quality and pipeline reliability. Explore topics like: metrics collection, log analysis, automated testing, and CI/CD pipelines. - Resources/Activities: - Implement a monitoring and alerting system for your data pipelines using tools like Prometheus, Grafana, or cloud-native monitoring services. - Set up automated testing frameworks for data pipeline components. - Explore CI/CD pipelines for data pipelines. - Automate the deployment and management of data pipelines using tools like Airflow or cloud-native orchestration services. - Expected Outcomes: - Ability to build comprehensive monitoring and alerting systems for data pipelines. - Proficiency in automated testing and CI/CD for data pipelines. - Understanding of how to automate the deployment and management of data pipelines.

[Description] Explore cutting-edge data pipeline tools and frameworks and learn how to choose the right tools for different use cases. Learn about advanced features and best practices for popular tools like Apache Airflow, Apache Beam, and cloud-native services. - Resources/Activities: - Dive deep into advanced features of Apache Airflow or another orchestration tool. - Explore Apache Beam for unified batch and stream processing. - Compare and contrast different data pipeline tools and frameworks based on their strengths and weaknesses. - Experiment with cloud-native data pipeline services. - Expected Outcomes: - In-depth understanding of advanced features of data pipeline tools and frameworks. - Ability to choose the right tools and frameworks for different use cases. - Expertise in using orchestration and data processing services.

[Description] Apply all the learned concepts and build a complete, end-to-end data pipeline for a real-world scenario. This involves data ingestion, transformation, feature engineering, loading, monitoring, and alerting. - Resources/Activities: - Choose a real-world data science project (e.g., fraud detection, recommendation system, or predictive maintenance). - Design and build a data pipeline from scratch based on project requirements. - Implement data ingestion, transformation, feature engineering, and data loading. - Implement monitoring and alerting. - Deploy the data pipeline to a production environment (cloud or on-premise). - Expected Outcomes: - Ability to build a production-ready data pipeline from start to finish. - Experience in applying all learned concepts to solve a real-world problem. - Ability to design, implement, deploy, and manage a complex data pipeline.