This course will teach you about the Power BI Analytics toolset so that you have the knowledge and know-how to apply this and answer related questions in the DA100 exam.

You'll learn how to use Power BI to perform data analytical functions, how to identify outliers in your data, how to group data together, and how to bin data for analysis. You'll also learn how to perform time series analysis. Finally, you'll work with advanced analytic features of Power BI, such as Quick Insights, AI Insights, and the Analyze feature.

Analyzing, visualizing, and troubleshooting system and application logs are mandatory in a distributed application. This course will teach you how to write simple and effective queries using KQL and analyze logs for security vulnerabilities.

This is an intermediate course focused on searching Kibana using the Lucene Query Syntax. This course will teach you how to perform complex searches in Kibana using Apache Lucene Query Syntax.

Any software application needs to be maintained over the long run. Elasticsearch Index is no different. This course will teach you the strategies and techniques you can follow to keep your index perform optimally, and your queries run faster.

Understanding the core features of a product is vital to leverage its fullest potential. This course will teach you to effectively search and visualize data by saving and sharing them with proper access across your organization.

This course covers the administration of various resources on Databricks SQL to ensure its users have clearly-defined roles and permissions. This will enable them to make the most of this big data platform while regulating resource expenses.

Snowflake offers a deep and wide feature set for data analytic tasks. This course will teach you how to perform common tasks such as data sampling, result approximation, compose queries with CTEs, views, subqueries, analytical functions and more.

MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of performing feature engineering within MATLAB.

Explore modern statistical analysis techniques using R, focusing on data exploration, modeling, and inference methods for effective data analysis.

Explore natural language processing techniques using R, focusing on text analysis, sentiment analysis, and machine learning applications.

Learn how MATLAB can be used for different types of statistical analysis. No matter if summary statistics, hypothesis tests, modeling or visualizations, MATLAB has you covered.