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Bewertung und Feedback des Lernenden für Excel Fundamentals for Data Analysis von Macquarie University

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As data becomes the modern currency, so the ability to analyse the data quickly and accurately has become of paramount importance. Excel with its extraordinarily broad range of features and capabilities is one of the most widely used programs for doing this. In the first course of our Excel Skills for Data Analysis and Visualization Specialization, you will learn the fundamentals of Excel for data analysis. When you have completed the course, you will be able to use a range of Excel tools and functions to clean and prepare data for analysis; automate data analysis with the help of Named Ranges and Tables; and use logical and lookup functions to transform, link and categorise data. This course will enable you to build a strong foundation in the fundamentals, helping you to be more efficient in your day-to-day and developing the necessary skills to work with the more advanced techniques used in later courses. To make the content easy to relate to and to personalize the learning experience, we are going to follow Zara's journey through the course. Who is Zara? Well, she is no-one and everyone. You will find that Zara's trials and tribulations sound familiar, and together with Zara, you will develop your Excel skills along the way — and, importantly, have some fun doing it. The Excel Skills for Data Analytics and Visualization courses are the sequel to one of most successful specializations on Coursera, Excel Skills for Business, which has attracted hundreds of thousands of learners and top ratings. Transform your skills, your confidence, and your opportunities by adding this new set of skills to your repertoire....

Top-Bewertungen

MC

24. Sep. 2022

It was an excellent experience to learn in these type of flatform. This is course give me a tremoundous learning oppurtunity . iam studied all studied materila, attended all quizes with 80 t0 95% .

FV

17. Juli 2020

Well done. A small thing, though: in the quizzes, the grading could be made more fault-tolerant. Example: an answer such as AND(y,x) instead of AND(x,y) is deemed wrong, which borders on the nasty.

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