Difference between revisions of "IS428 2016-17 Term1 Assign2 Dina Heng Li Gwek"
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==Blog== | ==Blog== | ||
+ | To get things started, I examined and analysed the raw data ("WPI_data") to understand the dataset and attributes given to me. I also noticed that some data are either improper or irrelevant. For example, there were some spelling mistakes. As a data analyst, I needed to ensure that I have done proper data cleaning before doing analytics so that my analysis is true and accurate. I used Microsoft Excel 2016 and JMP to clean and transform my data. Then, I used Tableau to do my visualization. | ||
==Findings & Recommendations== | ==Findings & Recommendations== |
Revision as of 18:18, 25 September 2016
Contents
Abstract
Twenty-two workers have died from workplace accidents since the start of 2016, more than the 18 from the same period last year, the Workplace Safety and Health (WSH) Council said on Monday (Apr 4). It is urging relevant industries to review their safety procedures at work immediately and to ensure that all control measures are in place.
Manpower Minister Lim Swee Say, noting that in the first half of 2016, there have already been 42 fatalities - 12 more compared to the same period last year. Mr Lim added that the construction sector remains the top contributor with 18 fatalities - an 80 per cent increase from last year, which saw 10 fatalities in the sector.
This issue has sparked my curiosity as I want to find out more about the trends occurring in specific industries, the key figures in workplace injuries, top sectors with workers injured, top causes of injuries etc.
Theme of Interest
Questions for Investigation:
• Which industry has the highest rate of workplace injuries?
• Which body parts got injured most of the time?
• Which gender suffered more injuries and particularly in which industry?
• Who is more prone to injuries? Is it the younger or older ones?
• How did they get injured?
• Who gets injured most of the time?
• When do accidents occur the most? (Which month and why? Does it occur more frequently during overtime?)
• What is the distribution of the number of MC given to injured employees?
Identifying appropriate attributes
The following data attributes are selected for analysis:
- Reported Date
- Reported Month
- Informant's No Of Employees
- Accident Month
- Major Industry
- Sub Industry
- Body Parts Injured
- Nature of Injury
- Major Injury Indicator
- Accident Type Category
- Victim's Age
- Cause
- Victim's Gender
Blog
To get things started, I examined and analysed the raw data ("WPI_data") to understand the dataset and attributes given to me. I also noticed that some data are either improper or irrelevant. For example, there were some spelling mistakes. As a data analyst, I needed to ensure that I have done proper data cleaning before doing analytics so that my analysis is true and accurate. I used Microsoft Excel 2016 and JMP to clean and transform my data. Then, I used Tableau to do my visualization.
Findings & Recommendations
Others
Tools Utilized
- Excel 2013 for data preparation
- Tableau for visualization