
Comprehensive Excel training covering worksheets, formatting, functions, charts, and case studies. Develop essential skills for data management and analysis through hands-on practice and real-world applications.

Learn to create and manage multi-layered Vega-Lite visualizations for COVID-19 geodata using MLflow, enhancing data communication and lifecycle management for powerful insights.

Discover how Databricks simplifies data science with a unified platform for analytics workloads, from data preparation to predictive analytics, enhancing collaboration and productivity at scale.

Explore Databricks SQL Analytics: set up SQL endpoints, query workspaces, create dashboards, and connect BI tools. Learn how this tool enhances the Lakehouse architecture for SQL-savvy data analysts.

Comparison of open-source data quality tools for continuous imports, covering maturity, documentation, extensibility, and features like data profiling and anomaly detection.

Explore Apache Spark 3.2's interval enhancements, including ANSI SQL conformance, new interval types, and improved APIs. Learn to construct, use, and manage intervals effectively in Spark SQL and PySpark.

Explore wizard-driven AI anomaly detection for fraud prevention using Databricks. Learn unsupervised and supervised methods, aggregation frameworks, and human-in-the-loop feedback to improve models over time.

Explore advanced techniques for achieving low-latency, high-concurrency analytics on large-scale data lakes. Learn about optimized storage, indexing, and practical approaches to handle massive data volumes efficiently.

Explore large-scale geospatial indexing and analysis using Apache Spark, covering challenges, frameworks, data structures, and practical applications in cloud-first architecture with Databricks.

Explore unsupervised discovery of temporal sequences in high-dimensional datasets using seqNMF, a tool extending convolutional NMF. Learn to extract and validate neural sequences from large-scale recordings.

Explore automated time series forecasting with PyCaret's low-code module. Learn setup, EDA, training, and forecasting techniques to streamline your predictive modeling workflow.

Explore the evolution of sports analytics in the NBA, its impact on teams, and how data drives competitive advantage in professional basketball.

Explore SparkR for integrating R with Databricks and Apache Spark, covering architecture, APIs, libraries, and practical examples of data manipulation and analysis.

Learn to create and utilize SQL views in Databricks for efficient data analysis and pipeline reuse. Explore common table expressions, aggregations, and case statements for advanced querying techniques.

Explore the data science process from project inception to completion, covering key steps for successful implementation in Databricks and Apache Spark projects.

Learn to create and use Apache Spark clusters, understand their differences from Databricks, and explore data querying with Zeppelin notebooks in this step-by-step guide.

Comprehensive tour of Databricks Workspace, demonstrating tools and features with focus on the proprietary notebook IDE. Provides foundation for advanced Databricks and Apache Spark usage.

Learn to model bird bath observations in Australia using tidymodels, combining model specifications with feature engineering recipes for effective fitting and prediction.

Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.

Explore how handling class imbalance affects classification metrics using aircraft wildlife strike data, with focus on sensitivity and specificity in predictive modeling.

Explore decision tree modeling and partial dependence profiles for Mario Kart world records using tidymodels and DALEX, with a focus on predicting shortcut usage.