College Majors

The dataset we are going to analyze is college majors from the American Community Survey 2010-2012 Public Use Microdata Series regarding the different employment rates and salaries of a wide variety of majors in the US. Along with our dataset, we also decided to analyze the gender distribution of different majors. With our datasets, we are able to break down the different incomes, popularities and many other relevant properties of different college majors. Furthermore, this analysis will help colleges and academic suepervisors understand which majors are best suited for a particular student.

As a college student, we are aware that there are still a plethora of college students that are in the process of choosing a major which could be one of the biggest turning point of their lives. Many of which fear of choosing the wrong major and regret their decision later on in life. This analysis will provide the necessary information to help students decide their majors as well as understanding the potential of their future careers.

Throughout this analysis, we will use data provided by the American Community Survey 2010-2012 Public Use Microdata Series that contains data (employment status, gender, salary, major) from surverys of recently graduated students and graduate students of all ages. Moreover, further documentation regarding this data is available here.

Rank	Major_code	Major	Total	Men	Women	Major_category	ShareWomen	Sample_size	Employed	Full_time	Part_time	Full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th	College_jobs	Non_college_jobs	Low_wage_jobs
1	2419	PETROLEUM ENGINEERING	2339	2057	282	Engineering	0.1205643	36	1976	1849	270	1207	37	0.0183805	110000	95000	125000	1534	364	193
2	2416	MINING AND MINERAL ENGINEERING	756	679	77	Engineering	0.1018519	7	640	556	170	388	85	0.1172414	75000	55000	90000	350	257	50
3	2415	METALLURGICAL ENGINEERING	856	725	131	Engineering	0.1530374	3	648	558	133	340	16	0.0240964	73000	50000	105000	456	176	0
4	2417	NAVAL ARCHITECTURE AND MARINE ENGINEERING	1258	1123	135	Engineering	0.1073132	16	758	1069	150	692	40	0.0501253	70000	43000	80000	529	102	0
5	2405	CHEMICAL ENGINEERING	32260	21239	11021	Engineering	0.3416305	289	25694	23170	5180	16697	1672	0.0610977	65000	50000	75000	18314	4440	972
6	2418	NUCLEAR ENGINEERING	2573	2200	373	Engineering	0.1449670	17	1857	2038	264	1449	400	0.1772264	65000	50000	102000	1142	657	244
7	6202	ACTUARIAL SCIENCE	3777	2110	1667	Business	0.4413556	51	2912	2924	296	2482	308	0.0956522	62000	53000	72000	1768	314	259
8	5001	ASTRONOMY AND ASTROPHYSICS	1792	832	960	Physical Sciences	0.5357143	10	1526	1085	553	827	33	0.0211674	62000	31500	109000	972	500	220
9	2414	MECHANICAL ENGINEERING	91227	80320	10907	Engineering	0.1195589	1029	76442	71298	13101	54639	4650	0.0573423	60000	48000	70000	52844	16384	3253
10	2408	ELECTRICAL ENGINEERING	81527	65511	16016	Engineering	0.1964503	631	61928	55450	12695	41413	3895	0.0591738	60000	45000	72000	45829	10874	3170

Major_code	Major	Major_category	Total	Employed	Employed_full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th
1100	GENERAL AGRICULTURE	Agriculture & Natural Resources	128148	90245	74078	2423	0.0261471	50000	34000	80000
1101	AGRICULTURE PRODUCTION AND MANAGEMENT	Agriculture & Natural Resources	95326	76865	64240	2266	0.0286361	54000	36000	80000
1102	AGRICULTURAL ECONOMICS	Agriculture & Natural Resources	33955	26321	22810	821	0.0302483	63000	40000	98000
1103	ANIMAL SCIENCES	Agriculture & Natural Resources	103549	81177	64937	3619	0.0426789	46000	30000	72000
1104	FOOD SCIENCE	Agriculture & Natural Resources	24280	17281	12722	894	0.0491884	62000	38500	90000
1105	PLANT SCIENCE AND AGRONOMY	Agriculture & Natural Resources	79409	63043	51077	2070	0.0317909	50000	35000	75000
1106	SOIL SCIENCE	Agriculture & Natural Resources	6586	4926	4042	264	0.0508671	63000	39400	88000
1199	MISCELLANEOUS AGRICULTURE	Agriculture & Natural Resources	8549	6392	5074	261	0.0392304	52000	35000	75000
1301	ENVIRONMENTAL SCIENCE	Biology & Life Science	106106	87602	65238	4736	0.0512898	52000	38000	75000
1302	FORESTRY	Agriculture & Natural Resources	69447	48228	39613	2144	0.0425633	58000	40500	80000

• Rank: Rank by median earnings
• Major_code: Major code, FO1DP in ACS PUMS
• Major: Major description
• Major_category: Category of major from Carnevale et al
• Total: Total number of people with major
• Sample_size: Sample size (unweighted) of full-time, year-round ONLY (used for earnings)
• Men: Male graduates
• Women: Female graduates
• ShareWomen: Women as share of total
• Employed: Number employed (ESR == 1 or 2)
• Full_time: Employed 35 hours or more
• Part_time: Employed less than 35 hours
• Full_time_year_round: Employed at least 50 weeks (WKW == 1) and at least 35 hours (WKHP >= 35)
• Unemployed: Number unemployed (ESR == 3)
• Unemployment_rate: Unemployed / (Unemployed + Employed)
• Median: Median earnings of full-time, year-round workers
• P25th: 25th percentile of earnigns
• P75th: 75th percentile of earnings
• College_jobs: Number with job requiring a college degree
• Non_college_jobs: Number with job not requiring a college degree
• Low_wage_jobs: Number in low-wage service jobs

Overview of data:

Most popular major for all ages : Business with 9858741 people in that major

Least popular major for all ages : Interdisciplinary with 45199 people in that major

Most popular major for recent graduate students : Business with 1302376 people in that major

Least popular major for recent graduate students : Interdisciplinary with 12296 people in that major

The average median salary for recent grads is: 40151.4450867;

The standard deviation for median salary of recent grads is 11470.1818021.

Outlier analysis does not fit for these two sets of data because all three graphs are showing a trend of mean values for each topic. Outliers may be identified inside of eacj major category, but they are still in the major category, so does not effect the trend and the result of problem analysis.

Does gender affect major decisions?

In this section, we will analyze whether or not gender has a correlation between major choices, and if it does, we will also analyze whether or not women opt for lesser paying jobs than men. Throughout this question analysis, we will use datasets from recently graduated students from a wide variety of universities.

Subsequently, we will analyze and compare the differences in the ratio of female recent graduates and major categories - the categories each major falls in, e.g. Computer and Mathematics. In order to acquire this data, we calculated the the percentage of women in each major by dividing the number of women women with the total per major and multiplying by a hundred. Moreover, we used dplyr’s group_by() method to summarize with the use of dplyr’s summarize() into the means of male ratio, female ratio, salary medians, 25th percentile and 75th percentile. Furthermore, we were also able to plot the mean salary medians, 25th percentile and 75th percentile of a variety of major categories in order to identify which kinds of majors reflect to higher salaries.

Then, we will present a scatterplot of median salaries over ratio of men and women to prove whether or not women opt to lesser paying majors by using dplyr’s gather() to plot both men and women ratios in the same scatterplot.

• The mean median salary of all recent graduates is $40151.4450867.
• The mean 25th percentile salary of all recent graduates is $29501.4450867.
• The mean 75th percentile salary of all recent graduates is $51494.2196532.
• The highest median salary is $110000.
• There are 173 majors in the dataset.

PETROLEUM ENGINEERING

Throughout the analysis, we found 1 outlier on the major median salary vs gender plot. We found out that PETROLEUM ENGINEERING is the outlier which has a median salary of 110000.

The results of our analysis proves that gender indeed is a factor when it comes to major decisions. Furthermore, we discovered that more women opt to lower paying majors than men. WE found out that majors such as engineering, bussiness and Computer and Mathematics are the highest paying major categories, however has the lowest ratios of women.

Should you take popular majors?

At the beginning of the college, it is all about thinking and choosing this whole business of majors. Since many students are getting lost and having a hard time to make decisions, this question will give them a general idea on where they should start based on the income and umemployment rate of different major categoies.

We grouped all the 173 majors by major categories, and then add all the people in each major category, calculate the average income and unemployment rate.

Major with the highest income : Engineering with an average income of $77758.6206897/year

Major with the lowest income : Interdisciplinary with an average income of $43000/year

Major with the highest umempolyment rate : Arts with an average umempolyment rate of 0.0876005

Major with the lowest umempolyment rate : Agriculture & Natural Resources with an average umempolyment rate of 0.0395692

We saw that Business is the most popular major based on the number of people in that major, and Interdisciplinary is the least popular major. If the answer is people should take popular major, then we assume that Business should have the highest income and the lowest unemployment rate; otherwise, it is meaningless to go for a popular major since it is really competitive to stand out among so many people.

However, the result shows some kinds of better give back from going into popular majors. In the first graph showing income of all the major categories, although Business as the most popular major does not have the highest average income, and Interdisciplinary are not necessarily being the lowest income, we could still see a trend of higher income on the two ends and relatively lower income in the middle. We discovered something interesting that this may because companies give higher salaries to those who are in minority majors because they are fewer people to hire, and resource are scarce, and to those who are in majority majors because they are in large demand and are necessary for the company. Therefore, people could have two choice when considering higher incomes, either going for minority majors or popular majors.

In the second graph showing unemployment rate of all the major categories, we could see a normal distribution on the unemployment rate besides Interdisciplinary has a relatively high unemployment rate. Therefore, we could have the similar conclusion as the income graph that for those in the minority majors and popular majors are more likely to find a stable job becuase of the same reason as above.

Therefore, based on our interpretations, we suggest that people should choose either the popular majors or the minority majors if they do not have yet decide majors.

Do recent graduates receive higher or lower salaries and employment rate comparing to the average?

For this question, we are comparing the unemployment rate and median salary for recent grads and all-ages.

In order to analyze the data, we selected the columns needed for analysis from the two datasets, and renamed the columns when necessary. We then combined the two dataframes, grouped the data by major categories, and summarized the data for each category. Such process produced following data frame:

Major.Category	Recent.Grads.Unemployment.Rate	Recent.Grads.Median.Salary	Overall.Unemployment.Rate	Overall.Median.Salary
Agriculture & Natural Resources	0.0563283	36900.00	0.0395692	55000.00
Arts	0.0901727	33062.50	0.0876005	43525.00
Biology & Life Science	0.0609178	36421.43	0.0499360	50821.43
Business	0.0710635	43538.46	0.0544960	60615.38
Communications & Journalism	0.0755378	34500.00	0.0691245	49500.00
Computers & Mathematics	0.0842560	42745.45	0.0594370	66272.73
Education	0.0517020	32350.00	0.0467620	43831.25
Engineering	0.0633339	57382.76	0.0506300	77758.62
Health	0.0659202	36825.00	0.0472093	56458.33
Humanities & Liberal Arts	0.0810076	31913.33	0.0694287	46080.00
Industrial Arts & Consumer Services	0.0480713	36342.86	0.0585457	52642.86
Interdisciplinary	0.0708609	35000.00	0.0772690	43000.00
Law & Public Policy	0.0908048	42200.00	0.0678536	52800.00
Physical Sciences	0.0465111	41890.00	0.0545406	62400.00
Psychology & Social Work	0.0720648	30100.00	0.0778670	44555.56
Social Science	0.0957288	37344.44	0.0656857	53222.22

Other quantitative results we found are displayed below:

The average difference in median salary for all majors is: -28.97%.

The standard deviation is: 0.097239.

This plot displays the overall unemployment rate (with dark green dots) and the unemployment rate for recent grads (with yellow green dots), along with their differences shown in the form of a bar graph. From this visualized data, we can conclude that for most of the major categories, those who recently graduated has a higher unemployment rate than the overall average.

In this plot, the difference in median salary for each major category is illustrated - it shows that the median salary is lower for recent grads in every single major category.

This histogram is created using the percent difference in median salary for every single major. It portrays a symmetric and unimodal distribution, with an average difference of -28.97% and a standard deviation of 0.097239 - that is, on average, recent grads receives a -28.97% lower median salary than the overall level.