essay 代寫：失業率
以上數據和圖表解釋了過去5-6年所有國家失業率或統計數字背後的故事。數據還顯示了男性和女性的失業勞動力。從圖1可以看出，2010年失業率較低，2012年和2013年開始上升。2014年，失業率高於前幾年。每個國家的失業率都在平均上升。2015年的失業率如表所示較高，但由於缺乏數據，2015年的as數據並沒有顯示更多的信息。據《南華早報》(2016)報道，富士康公司已經要求6萬多名員工失業，該公司正在用“機器人”代替。從圖2和圖3可以看出，大多數國家的平均失業率爲25%。與早些年相比，這個失業率確實很高。所有這些來源和數據都表明，由於工業的技術進步和自動化，未來將缺乏更多的工作機會。隨着就業機會的減少和人口的增加，未來將是一段相當艱難的時期。
essay 代寫：失業率
這些數據是從世界銀行和國際勞工組織等各種數據平臺收集的。這些政府機構擁有多年的所有可用數據。
數據採集流程:
首先，點擊平臺的數據部分
搜索“失業”數據
獲取列表後，單擊所需的數據鏈接
數據來自19世紀，所以數據是根據需要定製的
自定義後，應用篩選器
在過濾器的幫助下，數據以excel表格的形式下載
下載數據後根據數據需求進行細化
這些數據隨後被用於使用excel獲取圖表
essay 代寫：失業率
The above data and charts explain the story behind the unemployment rate or statistics for all countries for last 5-6 years. The data also shows male vs. female unemployment labour force. The Fig-1 indicates that in 2010 the unemployment rate was less and it started increasing in 2012 and 2013. In 2014, the unemployment rate went higher than last few years. In each country the unemployment rate was increasing on an average. In 2015, the unemployment rate was higher as indicated but figure does not show more information about 2015 as because of unavailability of data. According to “South China Morning Post (2016)”, Foxconn company has asked more than 60,000 employees to lose their job and the company is replacing with “robots”. The Fig-2 & 3 indicate that average unemployment rate in most of the countries is 25%. This unemployment rate is literally very high in comparison to earlier years. All these sources and data indicate that there is dearth in coming future for losing more jobs because of technology advancement and automation in the industries. It is going to be quite a tough time in future with decrease in jobs and increase in population.
essay 代寫：失業率
The data was collected from various data platforms for example World Bank and International Labour Organization. These government organizations have all available data for long years.
The data collection process:
Firstly, click on the data section of the platform
Searched “Unemployment” data
After getting the list click on the desired data link
The data was from 19th century so the data was customized according to the need
After customization, the filter was applied
With help of the filter the data was downloaded in form of excel sheet
After downloading the data was refined as per data need
This data was then used for getting graphs using excel
