R

This path is similar to the Data Analyst learning path above, but is geared to building a strong and broad foundation of data analysis skills, as compared to being more specifically focused on passing the PL 300 exam.

This Learning Path delves into data analysis and visualization using both R and Python, covering data cleaning, data frames, and the use of packages like dplyr, tidyverse, skim R, and seaborn. It offers hands-on examples, such as creating various plots, performing group-based operations, and utilizing descriptive statistics for effective data interpretation.

This learning path offers a deep dive into data management and analysis, emphasizing the hands-on use of Python and Scala languages for manipulating CSV and JSON files, and the application of SQL for data querying. 

This path is designed to help you harness the full power of Power BI in combination with R and Python. You will learn how to conduct advanced analyses and gain insight into complex business questions.

This Learning Path in statistical analysis and inference equips learners with practical skills to conduct tests, estimate sample sizes, and understand test assumptions. 

Enhance your Power BI reports and analysis with R's visualization, text analysis and statistical modeling capabilities.

Discover the key to unlocking insights and making informed decisions through data.

The second part of the preparation course for the Microsoft's DP-500 Exam Certification

A practical guide to recognizing, understanding, and managing uncertainty in data 

Amplify your Power BI visualizations with R's comprehensive statistical analysis and graphics language

Dive deeper into using ChatGPT for data science tasks. Explore techniques for data preprocessing, feature engineering, model training, and deploying ChatGPT models for specific data analysis and prediction tasks.

The first of the two-part preparation course for the Microsoft's DP-500 Exam Certification

Learn valid inference techniques for two-sample and two-variable data questions.

Explore possibilities for supercharging Power BI with R’s modeling and visualization capabilities.

Creating basic tables and figures with R to check assumptions and communicate findings

Create basic pipelines for data cleaning, visualization and modeling in R

WORKOUTS is an innovative approach that combines hands-on exercises, practical applications, and collaborative learning to help data practitioners master data skills effectively.

This session will cover which Python and R packages and examples can facilitate functionalities found in Power BI