Norway path Kurs path Kurskalender path BUSINESS INTELLIGENCE KURS path Now You See It: Visual Data Analysis

Now You See It: Visual Data Analysis

Tilbake
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Varighet 1 dag
dag3.
Duration      Price Delivery type  
1 NOK 5 500,- Instructor-led classroom  

Ninety percent of all business data analysis can be done using simple graphing techniques to discern meaningful patterns in data. The remaining 10%, which requires sophisticated statistical and financial analysis skills, are well addressed by available resources, but where are the resources that teach the skills needed by the rest of us? Even though these skills are easy to learn and apply with proper guidance, very few people involved in analyzing business data know them. This course provides a solution. 

This course is intended for all those whose work requires them to make sense of quantitative business data. This audience is much broader than financial analysts, or even analysts by any name. This course provides practical skills that are useful to managers at all levels and to anyone interested in keeping a keen eye on the business. Anyone who uses Excel or any of the many other business productivity tools used for data access, analysis, and reporting, will learn how to use them productively, perhaps for the first time.

Mål

• Understand each of the prominent quantitative relationships and the stories they have to tell (time-series, distribution, correlation, etc.)

• Use the best graphs, visual analysis techniques, and practices for analyzing each type of quantitative relationship

• Recognize the visual characteristics of data that are meaningful

• Navigate through data analytically and efficiently

• Apply the findings of information visualization research to the analysis of business data

Innhold

1. An introduction to visual data analysis

2. The traits of top data analysts

3. The best data for meaningful analysis

4. Visual perception and data visualization

5. Visual characteristics to look for in the data

6. Quantitative business analysis techniques by type

a. Analyzing time series

b. Analyzing rankings and parts-to-whole

c. Analyzing deviations

d. Analyzing distributions

e. Analyzing correlations

f. Analyzing multivariate profiles

g. Analyzing geo-spatial data

7. Analytical navigation

8. The critical contributions from the information visualization research community

Tid
  • 21.09.2012
Tilbake
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