Data Analysis with Python, R and Power BI Training in Australia

This training provides a comprehensive guide to data analysis using powerful tools and programming languages, including Python, R, Power Query, and Power BI. Participants will learn how to manipulate, analyze, and visualize data efficiently to generate insights and support data-driven decision-making.

Welcome to the official Data Analysis with Python, R and Power BI Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in Australia. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

• Basic understanding of statistics and data analysis concepts.
• Familiarity with programming (preferably Python or R).
• Experience with Microsoft Excel or similar tools for basic data manipulation.

Participants will gain skills in:

• Data cleaning, transformation, and exploration using Python and R.
• Applying libraries like Pandas, NumPy, and ggplot2 for analysis and visualization.
• Utilizing Power Query to streamline data preparation workflows.
• Developing interactive Power BI dashboards with real-time insights.
• Integrating datasets and performing advanced data modeling techniques.

By the end of the training, participants will be able to:

• Perform data wrangling and analysis using Python and R.
• Utilize Power Query for data transformation and modeling.
• Create dynamic, interactive dashboards using Power BI.
• Apply statistical methods and machine learning models for predictive analytics.
• Integrate data from multiple sources and present insights effectively.