Great Learning via Great Learning Academy
Discover the significance of Pivot Tables and learn the essentials of creating them efficiently. Explore diverse methods for summarizing data and delve into various tools, mastering their implementation for insightful analysis. Gain expertise in style options, insert slicers for enhanced interactivity, and seamlessly change data sources. Understand the importance of refreshing data for dynamic updates. Conclude with a comprehensive summary, ensuring a solid grasp of Pivot Table fundamentals.
Transition seamlessly to Excel Graphs and Charts, where you'll receive a concise introduction to their role in data visualization. Implement and understand various chart types within Excel workbooks, concluding the course with a summary to solidify your proficiency in both Pivot Tables and Excel Graphs. Elevate your Excel skills and make data-driven decisions effortlessly!
Pivot tables are used to sort, count, total or reorganize the data stored in a table. Pivot tables are one of the most powerful features in Excel for Data Analytics. In this module, understand what Pivot tables are used for and how they are used by looking into examples shared by the instructor.
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MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
A curated collection of videos featuring top experts in artificial intelligence.
-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…
