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Вернуться к Judgmental Business Forecasting in Excel

Отзывы учащихся о курсе Judgmental Business Forecasting in Excel от партнера Университет Маккуори

4.4
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Оценки: 29

О курсе

In this course, we extend your business forecasting expertise from the first two courses of our Business Forecasting Specialisation on Time Series Models and Regression Models. We will explore the role of judgmental forecasting, when more quantitative forecasting methods have limitations, and we need to generate further business insights. We will be exploring some structured methodologies to create judgmental business forecasts using Business Indicators, Subjective Assessment Methods, and Exploratory Methods. For each of these methods, we will look at how we can use Excel to help us in achieving these judgmental forecasts and how Excel can help us visualising our forecast findings. Being judgmental forecasting methods, we will also look at the role of biases in Business Forecasting,...
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1–6 из 6 отзывов о курсе Judgmental Business Forecasting in Excel

автор: Sergio V P

29 мая 2022 г.

I very much appreciated the perfect balance between theory and hands-on work to apply the concepts learned. Excellent content and enjoyable lerning experience! Great job Macquarie team, continue the good work!

автор: AMIR

12 нояб. 2021 г.

Best trainer on Coursera

автор: Ali S

8 авг. 2021 г.

More than great, thanks

автор: Pritamdas S

29 окт. 2021 г.

This last course was a bit of a struggle

автор: Ong X H

17 нояб. 2021 г.

Not very user friendly for Mac users. I have some problem locating the tools shared in the view. Maybe the instructor can make a note and publish a mac guide.

автор: David C

30 июля 2022 г.

This course is not only useless but incorrect about 20% of the time. I have expereince in forecasting and the topics cocered in this course in the series are out of date and the piees related to cyclical/judgemental are out of date, poorly explained and many of the statements are wrong including the quizzes. I quit most of the way through because it seemed to be doing more harm than good.