R

In the modern era of data, the AI-driven approach is rapidly redefining how data science operates. Our learning path, "AI-Driven Data Science: Exploring PandasAI and ChatGPT", is crafted to immerse learners into the realms of PandasAI and ChatGPT. The path focuses on harnessing the potential of these AI tools, enabling learners to analyze, visualize, and interpret data with enhanced proficiency. From the foundational principles of data analysis with PandasAI to the expansive capabilities of ChatGPT in data science, this path offers a comprehensive journey into the world of AI-powered data analytics.

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.

Embark on a journey where traditional data science meets advanced AI in our "Data Science w/Python & ChatGPT " learning path. This curriculum offers a blend of foundational Python-driven data science techniques, coupled with the power of ChatGPT to revolutionize data analysis workflows. From mastering data wrangling to leveraging ChatGPT for enhanced coding support, this path promises a transformative learning experience for data analysts.

This path is designed to help you build expert level skills in the implementation, monitoring and governance of Power BI, including making optimal use of the Service through dataflows, datasets, thin reports, etc.

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. 

Elevate your Power BI prowess by delving into the confluence of machine learning and Python, two of the most transformative domains in data science. Our learning path, "Advanced Power BI: Harnessing Python and Machine Learning", unravels a structured approach to predictive analytics and advanced data manipulations. Through an immersive hands-on experience, you will journey from the intricacies of predictive modeling in Power BI to harnessing the versatility of Python, ensuring you're well-equipped to tackle complex analytical challenges.

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.

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

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

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

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

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

From Data Preprocessing to Advanced AutoML: Mastering Predictive Analytics in Power BI

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

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.

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

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

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

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

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