- 3 min read https://www.dimodelo.com/subscribers/Data_Warehouse_Bus_Matrix.zip
- 5 min product evaluation: https://datastudio.google.com
Do data preparation (same methods as we did in Lecture 02), which will
..* fill all blank values (as our Python notebook) ..* Create a metadata description table (as ourr df_metadata) ..* Obtain results for each continent (note this definition is missing, you have to define it in your own reference table), so we have results like: Each decade, each continent, the average total injures ..* a stacked bar chart, to show monthly count of injuries by each maker (use color legend to tell a make)
in a new tool such as
..* Google Data Studio, Power BI or Tableau (note it has a separate data transform tool)
..* R, Spark
..* Alteryx, Knime, Orange or WEKA
Then save to local Exercise02 folder and commit, pull request
create a Slack or Discord bot and push your Homework results to a channel
"分析规划宜采用“业务导向+数据驱动”的方式（如下图所示）。从关键业务目标分解出发，关联到具体的业务领域（研发、建设、运行、运维、安全环保、销售、采购等），从重要度和紧迫度的角度，对可能的业务分析问题进行评估。然后，结合初步的因子分解，评估每个题目的所需数据的完备度（Readiness）。综合业务价值和数据完备度，进行多个项目的优先排序"