Bhavishek Malik

The best data technologies to support your data strategy goals include: Data Ingestion: Apache Kafka, Apache NiFi Data Storage: Cloud data warehouses (Redshift, BigQuery, Synapse), Data Lakes (S3, ADLS, GCS) Data Processing: Apache Spark Data Transformation: Azure Data Factory, AWS Glue, Talend, Informatica BI and Analytics: Tableau, Power BI, R, Python Data Governance: Collibra, Informatica Axon, Apache Ranger, Privacera Data Management: Apache Airflow, Alation, AWS Glue Data Catalog

Partitioning data into training and testing sets is crucial for building accurate machine learning models. It helps evaluate model performance on unseen data, prevent overfitting, tune hyperparameters, and compare models objectively. Common partitioning techniques include: Train-Test Split: Split data randomly into training (e.g., 80%) and test (e.g., 20%) sets. K-fold Cross-Validation: Split data into K folds, train on K-1 folds, test on the remaining fold, and repeat K times. Stratified Splitting: Split data while preserving target variable distribution in each set. Time-Series Split: Split chronological data, using earlier data for training and later data for testing.

Scatter plots are key to understanding relationships. Correlation: Upward diagonal = positive correlation (both variables increase together). Downward diagonal = negative correlation (one increases, the other decreases). No pattern = weak or no correlation. Strength: Tight clusters = strong correlation. Scattered points = weaker correlation. Trends: Diagonal = linear relationship (consistent change). Curved = non-linear relationship (impact changes). Outliers: Points far from the main cluster could be errors or interesting exceptions.

Tableau enables compelling storytelling by creating guided narratives with annotations and explanations. Visuals can be exported to various formats like PDFs and PowerPoints for easy sharing. Publishing to Tableau Server or Online allows distributing interactive dashboards and facilitating collaboration, ensuring insights reach the right audiences effectively.

Manipulate Data: Utilize aggregations, groupings, and joins to organize member transactions. Perform Calculations: Apply formulas and table calculations for insights into loan approvals and member engagement. Conduct Statistical Analysis: Use correlation and regression to identify factors affecting member satisfaction. Refine Granularity: Use LOD expressions to control the granularity of performance metrics across branches.

Leverage color encoding, size encoding, labeling, annotations, and tooltips to highlight key insights. Adjust formatting like gridlines and axes for clarity. Incorporate filters, parameters, and strategic dashboard layouts to allow interactive exploration. Utilize custom shapes, icons, and advanced calculations to reveal complex patterns effectively. Iterate and refine visuals based on feedback.