Complete PowerBI Course with Augmented Analytics & Auto ML

🚀 Complete PowerBI Course with Augmented Analytics & Auto ML 📊

Recent Updates:

• June 2023: Dive into the world of Clustering and Segmentation with our latest video lecture.
• Nov 2022: Enhance your analytical prowess by mastering advanced DAX functions like Calculate and Lookup.
• June 2022: Explore a case study on leveraging Python within the PowerBI environment to unlock new capabilities.
• May 2022: Discover the power of Benford Law in detecting anomalies and fraudulent data.
• April 2022: Learn about Ageing Analysis and how it can provide valuable insights into your business trends.

Course Description:

The landscape of reporting, analytics, and business intelligence (BI) has undergone a revolution in the past half-century, transitioning from descriptive to prescriptive and predictive analytics. The integration of machine learning and artificial intelligence, particularly with the advent of Auto ML, has democratized predictive analytics, enabling users to forecast future outcomes with just a few clicks.

Power BI stands at the forefront of this transformation, offering interactive, user-friendly dashboards alongside powerful AI-enabled visuals and self-serve analytical capabilities. This comprehensive course is your gateway to mastering Power BI and understanding the nuances of augmented analytics.

What You'll Learn:

• Data Transformation: Master the art of reshaping, cleaning, and preparing your data for analysis.
• DAX (Data Analysis Expressions): Understand DAX functions to perform complex calculations in Power BI.
• Data Models: Design and implement robust data models that provide the backbone for powerful analytics.
• Simple & Complex Visualizations: Learn to create visuals that convey insights clearly and effectively, from basic charts to sophisticated data stories.
• AI-Enabled Visuals: Discover how AI can be used to enhance visuals with predictive and prescriptive capabilities.
• Forecasting, Anomaly & Sentiment Analysis: Gain the skills to predict future trends, detect anomalies in your data, and analyze sentiment across various datasets.
• Auto ML Concepts: Explore no-code approaches to machine learning that enable anyone to forecast future outcomes with Power BI.

Why Take This Course? 🎓

• No Prior Experience Required: While familiarity with Excel will be beneficial, this course is designed for learners of all levels.
• Two Courses in One: This comprehensive course covers both Power BI and AI & ML, providing you with a holistic understanding of the tools and techniques.
• Real-World Applications: Engage with case studies that showcase the practical application of these skills in real-world scenarios.

Join us on this journey to become a power user of Power BI and an analyst who leverages the cutting-edge capabilities of augmented analytics and Auto ML. Elevate your data analysis skills, transform raw data into actionable insights, and drive data-driven decision-making across any industry. Enroll now to unlock the full potential of your data! 💫📈

📚 Course Review for "Introduction to Machine Learning with Power BI"

Overview: The "Introduction to Machine Learning with Power BI" course has received an overall rating of 4.40 from recent reviews. This course is designed for individuals looking to expand their skill set by integrating machine learning into Power BI. The content is tailored to both beginners and intermediate users who wish to understand and apply ML techniques within the Power BI environment.

Pros:

• Skill Development: Reviewers highlighted the course's ability to add a new skill set to their list, indicating that the course successfully imparted valuable knowledge on Machine Learning within the context of Power BI.
• Insightful Content: The instruction was described as insightful and understanding of the concepts, suggesting that the course is well-structured and easy to follow for those unfamiliar with ML.

Cons:

• Practical Application Limitations: A significant concern raised by reviewers was the lack of test files provided for practical application. Only one test file (Sales Table) was available, and most models lacked such files. This limitation hindered students' ability to practice and build the models on their systems, which is a critical aspect of learning and mastering the skills taught in the course.
• Dataset Organization: The organization of datasets within the course materials was problematic. Reviewers found issues with table naming conventions, with examples like "sheet1[Sales]." There were also instances where it was unclear whether the datasets presented were for practical use or just for illustration. In one instance, datasets for Section 31 (Customers) were erroneously attached to Section 43 (Lesson), causing confusion and potential loss of time as students tried to locate the relevant data.
• Course Material Updates: Some reviewers expressed a desire for more frequent updates to the course content, including the inclusion of additional case studies. This would provide a more comprehensive learning experience and keep the course material current with the latest advancements in both Power BI and Machine Learning.

Additional Notes:

• Course Structure: The course structure seems to be well-received, with the content being informational and enjoyable for students.
• Scope of Coverage: While the course provides a solid introduction to Machine Learning within Power BI, it does not delve deeply into advanced topics. As such, it is suitable as an entry-level course but may not satisfy those seeking in-depth knowledge.

In conclusion, the "Introduction to Machine Learning with Power BI" course is a valuable resource for beginners and intermediate users looking to understand the basics of integrating ML into their Power BI projects. However, the course could be significantly improved by providing more practical application files, improving the organization and labeling of datasets, and incorporating updates and additional case studies to enrich the learning experience.