Updated: Mar. 21, 2019
1. This graph, which originally appeared in The New York Times, shows more than 500 jobs, their required skills and how likely they are to be automatable. You can enter a job in the “Find a job ...” field to find where it is on the graph. You can also hover the cursor over a job title to find more information. On March 21, we will provide additional background about the graph as well as relevant statistical concepts.
After looking closely at the graph above, think about these three questions:
• What do you notice?• What do you wonder?What are you curious about that comes from what you notice in the graph?• What might be going on in this graph?Write a catchy headline that captures the graph’s main idea. If your headline makes a claim, tell us what you noticed that supports your claim.
The questions are intended to build on one another, so try to answer them in order. Start with “I notice,” then “I wonder,” and end with “The story this graph is telling is ….” and a catchy headline.
2. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment, although teachers of younger students are welcome to post what their students have to say or they can have their students use this same activity on Desmos.)
3. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button or the @ symbol to address that student directly.
On Wednesday, March 20, our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern Time to help students’ understanding go deeper. You might use their responses as models for your own.
4. On the afternoon of Thursday, March 21, we will reveal more information about the graph at the bottom of this post. Students, we encourage you to post an additional comment after reading the reveal. How does the original New York Times article and the moderators’ comments help you see the graph differently? Try to incorporate the statistical terms defined in the Stat Nuggets in your response.
• Read our introductory post, which includes information about using the “Notice and Wonder” teaching strategy.• Learn about how and why other teachers are using this feature, and use the 2018-19 “What’s Going On in This Graph?” calendar to plan ahead for the 25 Wednesday releases. • Go to the A.S.A. K-12 website, which includes This is Statistics, resources, professional development, student competitions, curriculum, courses and careers.
Updated: Mar. 21, 2019
Do you have an idea of what job you are interested in? What skills or interests would you need for this job? This scatterplot, provided by the U.S. Department of Labor, shows the skill characteristics of about 550 jobs and how likely they are to be automatable. Notice where the “green” (least likely to be automated) jobs are and where the “purple” (most likely to be automated) jobs are. What does this tell us about the job market? To explore jobs, enter a job in the “Find a job…” field to see where it is on the graph. You can also hover the cursor over a job to find more information. Or, decide what characteristics fit you best on these two continua — from communicating with critical thinking to physical work and operating machines to processes and service work. Then, examine the jobs with these characteristics.
This graph appeared in the July 27, 2017 New York Times Upshot article “Old Skills, New Careers: Workers in Fading Jobs Often Already Have What it Takes to Succeed in Growing Ones.” The article’s focus was on how workers who lost their jobs to automation leveraged their skills with some specialized training to go into complete different fields. Often this training involved computer skills, sometimes paid for by new employers. With money, effort and the political will, a transition can be made to 21st century jobs.
It is important to know which jobs are least likely to be eliminated as robots and automation replace workers. If the job you are interested in is likely to be automatable, then find other jobs on the graph that have similar characteristics. An interactive table toward the end of the article allows you to specify a job and click to get two lists: jobs that have the most and least overlap in skills with your selected job. Also, use the O*NET tools (see below) to inform you about career choices.
You may want to think critically about these additional questions.
• Notice the location on the graph of “green” jobs and of “purple” jobs. What is the story about where on the graph the “green” and the “purple” jobs are? What labor market story does this tell?
• An alternative to a long, written story about a topic is a graph that incorporates large datasets. It can clearly and quickly reveal a story. Let’s see how large this graph’s dataset is.
There are about 550 jobs shown on this graph. Each job has three variables in this scatterplot. What are they? Each job also has three characteristics listed when you click on the job. What are they? Each characteristic is based on 161 different skills. With all of these jobs and their variables, characteristics and skills, about how many data items does this dataset incorporate into this graph?
• So you want to be a (you fill your job of choice). Go to the middle of the article and see a table where you enter your preferred job and see which other jobs have the most and least skills that overlap with your preferred job. Then, note for these jobs which are most (or least) likely to be automatable. O*NET database shows how much overlap there is between many seemingly dissimilar occupations. To find out how your interests relate to occupations, go to the O*NET Interest Profiler. By responding to 60 very brief questions, the site will generate a list of careers that may meet your interests. Then use the website to learn more about the training you will need.
• There are many other resources on the O*NET website. For example, we searched for graphic designer and found lists of needed skills, education, work styles, wages and job openings. Peruse the website to research a career that interests you. Other interesting sections of the site include apprenticeships and “hot technologies.”
Below in the Stat Nuggets, we define and explain mathematical terms that apply to these graphs. Look into the archives to see past Stat Nuggets.
Thank you for participating in “What’s Going On in This Graph?” which is intended to help you think more critically about graphs and the underlying data. Critical thinking is an essential element of statistics, the science of learning from data. Data visualizations, like these graphs, are an important part of statistics. They help us to understand and learn from data.
Keep noticing and wondering. We continue to welcome your responses.
Join us Wednesday, April 3 to notice and wonder about when spring springs with life; online moderation will take place between 9 a.m. - 2 p.m. E.T. The graph will be released on Thursday, March 28.
Stat Nuggets for “Old Skills, New Career”
A scatterplot is a graph that is used to display the relationship between two quantitative variables and is described by its direction, strength, form and unusual observations.
Direction: A positive direction means that the y-variable tends to increase as the x-variable increases (or a downhill trend). A negative direction means that the y-variable tends to decrease as the x-variable increases (or an uphill trend). Scatterplots are said to have no direction when the two variables seem to have no consistent positive or negative relationship.
Strength: A scatterplot is strong if the points fall in a “tight” pattern and weak if they are spread out.
Form: Form is the pattern the points make. The most common forms are linear, curved and scattered.
Unusual observations: Unusual points do not fit the general trend of the scatterplot. They may be outliers.
In the jobs and skills graph, the two quantitative variables are the range of job skills from more emphasis on operating machines and processes to clerical and service work (vertical axis, y-axis) and from more emphasis on communication and critical thinking to physical work (horizontal axis, x-axis). The graph is scattered with no direction. The strength is weak. The most unusual jobs are airline pilots, surgeons and physicists who require communication and critical thinking skills and operate machines or processes. Their jobs are less likely to be automated.
The graphs for “What’s Going On in This Graph?” are selected in partnership with Sharon Hessney, a mathematics teacher in Boston. Ms. Hessney wrote the “reveal” and Stat Nuggets. Erica Chauvet and James Bush, both professors at Waynesburg University in Pennsylvania, edited the reveal and moderated, respectively.B:
【九】【个】【月】【的】【写】【作】【时】【间】，【三】【个】【多】【月】【的】【准】【备】，120【多】【万】【字】…… 【这】【本】【书】【的】【成】【绩】【只】【能】【说】【是】【一】【塌】【糊】【涂】，【一】【开】【始】【我】【已】【经】【在】【这】【方】【面】【写】【了】（【抱】【怨】）【五】【百】【多】【字】，【但】【回】【头】【一】【想】，【这】【种】【破】【事】【有】【必】【要】【详】【细】【说】【明】【吗】，【于】【是】【就】【全】【部】【删】【掉】【了】。 【前】【期】【的】【不】【足】【是】【很】【明】【显】【的】，【而】【且】【在】【第】【一】【卷】【还】【有】【大】【量】【桥】【段】【模】【仿】【了】【三】【渣】【的】【作】【品】，【第】【一】【卷】【最】【后】【也】【是】【因】【为】【写】【不】【下】
【狮】【鹫】【的】【骨】【架】【翻】【腾】【着】【黑】【雾】，【诡】【异】【的】【飞】【翔】【在】【蓝】【天】【之】【下】，【决】【明】【已】【经】【摘】【下】【了】【眼】【镜】，【强】【烈】【疯】【狂】【的】【情】【绪】【使】【他】【无】【法】【继】【续】【坚】【持】，【而】【镜】【片】【上】【的】【裂】【痕】【又】【多】【了】【一】【条】。 【众】【人】【坐】【在】【狮】【鹫】【的】【背】【上】【一】【路】【前】【行】，【可】【眼】【前】【的】【景】【象】【却】【带】【着】【一】【股】【子】【渗】【人】【的】【感】【觉】。 【脚】【下】【的】【湖】【泊】【很】【大】，【原】【以】【为】【只】【有】【岸】【边】【漂】【浮】【着】【尸】【体】，【可】【现】【在】【看】【来】【不】【止】【如】【此】。 【从】【高】【空】【向】【下】【望】【去】
【雷】【毅】【整】【个】【人】【就】【石】【化】【了】。 【然】【后】【听】【到】【宋】【太】【生】【说】【句】，“【真】【好】，【免】【费】【洗】【了】【个】【脸】！” 【雷】【毅】【直】【接】【就】【跳】【起】【来】，【宋】【太】【生】【赶】【紧】【后】【退】！ “【兄】die，【别】【冲】【动】！” “【宋】【狗】【子】！！！” “【我】【突】【然】【想】【起】【我】【还】【有】【些】【事】【情】【没】【做】，【我】【先】【走】【了】【啊】，【小】【少】【爷】【就】【交】【给】【你】【了】！” 【宋】【太】【生】【说】【完】，【直】【接】【狂】【奔】【离】【去】。 【雷】【毅】【气】【得】【直】【翻】【白】【眼】。
“19【号】【客】【人】【出】【价】【两】【百】，【还】【有】【其】【他】【客】【人】【竞】【价】【吗】。” 【万】【宁】【刚】【在】【光】【屏】【上】【写】【下】“【贰】【佰】”，【只】【过】【了】【三】【秒】【钟】，【拍】【卖】【师】【那】【边】【就】【收】【到】【了】【信】【息】，【他】【抬】【起】【头】，【说】【道】。 【不】【过】【除】【了】【万】【宁】【如】【此】【奢】【侈】【用】【死】【蛋】【喂】【养】【宠】【物】，【其】【他】【人】【似】【乎】【并】【没】【有】【竞】【争】【的】【想】【法】，【于】【是】【在】【拍】【卖】【师】【三】【二】【一】【的】【倒】【数】【后】，【他】【成】【功】【地】【把】【炎】【纹】【兽】【蛋】【收】【入】【囊】【中】。 “【客】【人】，【这】【是】【您】【拍】2017年48期马报图片“【难】【道】【你】【还】【要】【我】【重】【复】【第】【二】【次】？”【沈】【夜】【安】【眉】【头】【一】【皱】，【啧】【了】【一】【声】。 【若】【不】【是】【灵】【力】【被】【沈】【风】【封】【印】【住】【了】，【只】【怕】【此】【刻】【已】【经】【是】【一】【巴】【掌】【抽】【了】【过】【去】。 “【马】【上】【去】【马】【上】【去】！”【两】【个】【天】【机】【门】【的】【外】【门】【弟】【子】【连】【忙】【点】【头】【说】【道】。 【看】【着】【沈】【夜】【安】【等】【人】【阴】【沉】【着】【脸】，【再】【看】【了】【看】【沈】【风】【的】【悠】【然】【自】【得】【模】【样】，【大】【概】【也】【能】【猜】【到】【一】【二】。 “【进】【来】【一】【坐】。”【沈】【夜】【安】【心】【中】【不】
【太】【后】【转】【头】【看】【见】【我】，【忽】【地】【停】【住】【身】：“【听】【说】【你】【住】【在】‘【梨】【花】【伴】【月】’，【每】【日】【还】【要】【来】【松】【鹤】【斋】【请】【安】，【实】【是】【难】【为】【你】【了】。” 【从】【未】【见】【太】【后】【如】【此】【和】【颜】【悦】【色】【过】，【我】【一】【怔】【神】【的】【功】【夫】，【乾】【隆】【笑】【道】：“【她】【年】【纪】【小】，【每】【日】【多】【走】【几】【步】，【当】【散】【心】【罢】【了】，【皇】【额】【娘】【不】【必】【心】【疼】【她】。” 【我】【受】【宠】【若】【惊】，【原】【本】【还】【有】【些】【拘】【谨】，【看】【来】【没】【人】【在】【太】【后】【面】【前】【嚼】【舌】【根】【子】【时】，【她】【对】【我】
“【噗】…！”【唐】【傲】【喷】【出】【一】【口】【鲜】【血】，【缓】【缓】【倒】【下】。 “【唐】【傲】…！”【他】【听】【到】【了】【呼】【喊】，【就】【在】【附】【近】【但】【在】【刀】【剑】【碰】【撞】【和】【枪】【林】【弹】【雨】【声】【中】【模】【糊】【地】【出】【现】。 【他】【可】【以】【尝】【到】【嘴】【中】【铁】【腥】【味】。 【战】【斗】【在】【他】【面】【前】【结】【束】。 【他】【的】【左】【臂】【麻】【木】【了】，【力】【量】【随】【着】【血】【液】【流】【泻】【在】【土】【地】【上】。 【黑】【袍】【子】【为】【他】【而】【来】，【怪】【物】【的】【杀】【戮】【不】【带】【任】【何】【言】【语】【和】【态】【度】，【那】【些】【都】【是】【渺】【小】【战】【士】
“【动】【手】【的】【人】【是】【谁】？”【穆】【悠】【然】【问】【道】。 【天】【马】【行】【空】【的】【问】【题】，【但】【是】【电】【话】【那】【边】【的】【周】【启】【明】【反】【应】【很】【快】。 “【一】【个】【熟】【人】。”【周】【启】【明】【道】，【虽】【然】【没】【看】【到】【人】，【但】【是】【光】【听】【语】【气】【就】【知】【道】【他】【此】【刻】【正】【慵】【懒】【的】【躺】【在】【某】【个】【柔】【软】【的】【沙】【发】【上】，【懒】【散】【而】【又】【闲】【适】，【漫】【不】【经】【心】【的】【样】【子】，【像】【只】【犯】【困】【的】【猫】【儿】。 【庄】【算】【看】【看】【前】【面】【的】【路】，【在】【看】【看】【穆】【悠】【然】，【她】【的】【眉】【头】【微】【微】【皱】【起】
“【报】，【喜】【事】【了】”，【邪】【头】【正】【在】【焦】【虑】【之】【际】，【守】【门】【的】【来】【报】。 “【喜】【从】【何】【来】？【快】【快】【说】【来】”。 “【小】【弟】【远】【远】【地】【瞧】【见】【少】【主】【人】【回】【来】【了】，【特】【地】【跑】【来】【让】【主】【上】【知】【道】” “【你】【莫】【不】【是】【看】【花】【眼】【了】，【少】【主】【人】【远】【在】【天】【边】，【怎】【可】【突】【然】【就】【回】【来】【了】？”，【话】【音】【未】【落】，【蓝】【天】【大】【步】【走】【了】【进】【来】。 “【孩】【儿】，【给】【父】【亲】【请】【安】【了】”，【半】【年】【未】【见】，【蓝】【天】【看】【起】【来】【成】【熟】【不】【少】