This by the number of urban statistical posts, I choose the bar charts compare, then press the count value reverse ordering, do some modification, beautification
Summary: the most big cities, the demand for data analyst, Beijing, Shanghai, shenzhen, the first three,
Task 2: statistics degree required
the condition of the employees,According to the degree required, statistics are jobs respectively, the most simple way: data perspective and table, choose education as the line, positionID for value, value way to count,
Selection chart type as bar charts, line filter remove blank value, it is concluded that the results are as follows:
Summary: bachelor degree in data analyst position, most undergraduate can play enough data analysts
Task three: statistics for job seekers work experience requirements
Similar to the above subject, simple pivot can be solved, the effect is as follows:
1-5 years required data analyst position most
Task 4: statistics of various position demand
A variety of positions that have a question, what kind of position is a kind of, I this to him in the firsttype, divided into technology, products, operations such as jobs, statistics are jobs
Results:
Summary: technology, products, operating post up,
Task 5: statistical different city industry demand for data analyst is
There are two classification variables to technology of data analyst positions, I the city added a filter, with data perspective and table, if is only use all the presents, clusters of bar chart, may be too crowded, it is concluded that the results are as follows:
Summary: data analyst in mobile Internet post demand the most, you can use the pie chart, actually, can through the city,
button to select different cityTask 6: statistical average salary level of different cities
Statistics in different cities, each in PivotTable the average of the minimum wage, city, a value of salary (7-11 k to 7 k after transformation, and numeric type), average value method to
Chart selection bar chart, it is concluded that the results are as follows:
Summary city average salary is higher, Beijing Shanghai shenzhen salary top three
Task 7: statistical average salary level in different industries
Industry is the definition that I use industryField, there should be no visible characters, not visible characters approach: 1 using clearn nonprintable characters, found that no matter how,
2 use the trim function transformation, remove Spaces, 3 find replace replace Spaces, the last two methods is used, it took me a long time, this problem is to find where is different, finally through the len function just know originally is a space reasons, there are several is much more space, and then you can use data perspective and table, OK, the results are as follows:
Summary: "mobile, social networking, e-commerce is can choose pay higher field
Task eight: statistical demand different position
Here the different jobs transformed using Positionlabel, of course, there are other positionname conversion can also, Label words will be a little thin, including skills have involved
Way the ETL Pivot - query, I choose will be Positionlabel inverse perspective, with hot finally tried to render, such skills, general direction were all
After the transformation:
Use pivot table, different position data is line, positionID is values, the value is count, then to make pivottables, copy paste into ordinary table, select the heat to
The result is shown in figure:
Summary: BI, C, ETL, DBA, data mining, data warehouse, is one of bigger,
Task: nine statistics of Beijing, Shanghai, shenzhen, guangzhou four cities in different industries and the degree of the wage distribution
Specific operation: c pivot table, line is education, value is salry, value counting method is the average salary, add two slicer, one city, one is indeustfield, industyfied find replace Spaces, the final figure as shown in figure
The Internet is the higher education, the higher salaries, wages and individual field is out of record of formal schooling, especially in health care industry
Task: ten statistics of Beijing, Shanghai, shenzhen, guangzhou four cities and working fixed number of year of salary distribution of different industries
Practice with task 9, the effect is as follows:
Summary: working fixed number of year, the higher the salary