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This case analyzes data analytics jobs in a region through multiple dimensions, aiming to gain a detailed understanding of the current state of the data analyst industry. Specifically, it covers various aspects such as salary levels and job demands in order to provide comprehensive and in-depth insights to help practitioners and enterprises fully grasp market dynamics and industry trends.
Data Processing
By looking at the data upfront, we can see that there are spaces in some of the columns for values that will affect our subsequent operations, and we'll let Bayeslab handle that.
prompt:
Remove the spaces in the experience and education columns, and write the results back to the original table.
We can also have Bayeslab automatically check for problems such as outliers in the data and deal with them.
prompt:
Check for duplicate and null values, Delete if any and write the result back to the original form
Exploratory analysis of data
We mainly analyze the regional distribution, salary distribution and correlation between data analysts in terms of multiple dimensions (region, salary status, years of working experience, education, industry, company size).
Demand for data analyst positions in different regions
First, let's look at the distribution of jobs in different regions, by counting the number of jobs in each region, we can get a map of the distribution of jobs in different regions.
prompt:
Count the occurrences of each address, obtain the number of positions for different addresses, bar chart
AI Chart Interpretation
The chart shows the number of job positions available in different districts. Notably, the A district has the highest number of positions, totaling 280, which is significantly more than any other district. The B district follows with 81 positions, while the D, C, F, and E districts have substantially fewer positions, with 28, 13, 8, and 7 positions respectively.
Demand for data analyst jobs in different industries
Next, let's take a look at the state of demand for data analysts in different industries, in the form of a bar chart.
prompt:
Count the number of different foursquare (if there are multiple industries in foursquare, only the first one is taken), histograms
AI Chart Interpretation
The chart displays the number of different Foursquare industries related to data analyst jobs. Notably, the "Mobile Internet" industry has the highest count at 154, followed by the "Financial" sector with 74. There's a significant drop after these, with "E-commerce" at 37 and other categories decreasing gradually. The industries towards the end of the list, such as "Big Data" and "Internet of Things (IoT)," have a count of just 1, indicating they are less represented.
Requirements for job seekers' years of experience
Do companies require years of experience from candidates? We can draw conclusions by counting the number of jobs with different experience.
prompt:
Count the number of different experience, bar chart
AI Chart Interpretation
The chart displays the number of job postings for data analyst positions grouped by different experience levels. The highest number of postings is for those with 3-5 years of experience, totaling 179. This is followed by 1-3 years of experience with 110 postings. The categories with the fewest postings are "more than 10 years" and "less than 1 year," with only 4 and 9 postings respectively.
Requirements for job seekers' academic qualifications
What kind of educational requirements do companies have for the position of Data Analytics?
prompt:
Count the number of different education levels, pie chart
AI Chart Interpretation
The pie chart illustrates the distribution of education levels required for Data Analyst jobs. The largest segment, 74.1%, requires an "Undergraduate and above" education, indicating this is the most common requirement. The next largest, "College and above," constitutes 12.5% of the job postings. "Not limited" makes up 8.87%, while "Master's degree or above" is the least common requirement at 4.56%.
Data Analyst Salary Range
What is the approximate salary range for a Data Analyst?
prompt:
Every 10k is a step (e.g. 0-10k, 10k-20k), counting the number of different salaries and plotting a histogram of the salary distribution (the horizontal axis is 0k, 10k, 20k and so on).
AI Chart Interpretation
The histogram shows the salary distribution for data analyst jobs in increments of 10k. The distribution is right-skewed, indicating a larger concentration of salaries on the lower end. The most frequent salary range is between 10k and 20k, followed by the 0k to 10k range, which indicates that most data analyst salaries fall within these categories. Salaries above 30k are less common, with very few reaching beyond 50k.
Relationship between company size and remuneration
What is the relationship between company size and compensation?
prompt:
Group the data by the 'figure' column and count the number of entries in each group. Only count the data where 'figure' is 50-150, 150-500, 500-2000 and 2000 or more. Calculate the average salary based on figure count and salary, where the salary column is in string format; take the midpoint of Xk-Xk. Use a box plot to show the salary distribution for each size category, including the median, upper and lower quartiles, and outliers (arranged in ascending order of figure values).
AI Chart Interpretation
Increasing Salary with Company Size:
As the company size increases, the median salary tends to rise. This trend is evident across the categories from "50-150" to "2000 or more", reflecting that larger companies typically offer higher salaries.
Variability Within Categories:
The largest salary variability is seen in the "2000 or more" category, indicated by the larger interquartile range and presence of outliers, suggesting more diverse salary offerings in very large companies.
Outliers:
Outliers are present in most categories, particularly noticeable in the "500-2000" and "2000 or more" groups, signaling sporadic higher salaries within these groups.
Relationship between work experience and remuneration
Will the pay be different depending on work experience?(Since the values in the salary column are in a range, we take the middle value for the calculation)
prompt:
1.Group the data by the "experience" column, and count the number in each group.
2.Combine "recent graduate", "Unlimited experience", and "lessthan1year" into one group named Unlimited experience.
3.Calculate the average salary based on experience count and salary, where the salary column is in string format; take the midpoint of XK-XK.
4.Use a boxplot to show the salary distribution for each experience level, including the median, upper and lower quartiles, and outliers.
AI Chart Interpretation
The chart displays salary distributions for different experience levels as boxplots.
1-3 years Experience: The median salary is lower compared to higher experience brackets, with a wide interquartile range (IQR) and several outliers, indicating variability in salaries within this group.
Unlimited Experience: This group has the lowest median salary and IQR, showing limited salary growth compared to others, despite grouping various experience levels.
3-5 years Experience: A significantly higher median salary than the two previous groups, with a moderate IQR and a few outliers, indicating more consistent salaries within this experience level.
5-10 years Experience: This group shows an even higher median salary, with a wider IQR, suggesting varied salary offerings at this experience level.
More than 10 years Experience: Exhibits the highest median and range of salaries, highlighting substantial salary growth with increased experience.
Impact of academic qualifications on remuneration
Will the salary be different depending on the degree?(Since the values in the salary column are in a range, we take the middle value for the calculation)
prompt:
1.Group the data by "education" and count the number of entries in each group.
2.Calculate the average salary based on education count and salary, where the salary column is in string format, take the midpoint of XK-XK.
3.Use a box plot to show the salary distribution for each education level.
AI Chart Interpretation
The chart displays the salary distribution by education level using box plots for four categories: "Under graduate and above," "not limited," "College and above," and "Master's degree or above."
Salary Range and Medians:
The "Master's degree or above" category has the highest median salary, indicating that individuals with this level of education generally earn more.
Salaries for "Under graduate and above" are more spread out, with a broader interquartile range compared to "College and above" and "not limited."
Outliers:
There are several outliers in the "Under graduate and above" and "Master's degree or above" categories, indicating some individuals in these groups earn significantly more than others.
Comparison Across Levels:
The "not limited" education level has a tight salary distribution with fewer outliers, indicating more consistency in salaries compared to the other groups.