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These are the user uploaded subtitles that are being translated: 1 00:00:00,270 --> 00:00:03,040 Recently, you've been learning about how 2 00:00:03,040 --> 00:00:05,635 businesses use data to guide decision-making, 3 00:00:05,635 --> 00:00:08,090 answer questions, and solve problems. 4 00:00:08,090 --> 00:00:11,470 In this video, we'll investigate how nonprofits to 5 00:00:11,470 --> 00:00:15,235 use data analysis to pursue their unique goals. 6 00:00:15,235 --> 00:00:18,100 Nonprofit groups are created to further 7 00:00:18,100 --> 00:00:20,995 a social cause or provide benefit to the public. 8 00:00:20,995 --> 00:00:22,585 As the name suggests, 9 00:00:22,585 --> 00:00:25,120 their main purpose is not about profit, 10 00:00:25,120 --> 00:00:26,980 but to foster a collective, 11 00:00:26,980 --> 00:00:29,080 public or social advantage. 12 00:00:29,080 --> 00:00:32,200 There are some very rewarding and inspiring opportunities 13 00:00:32,200 --> 00:00:34,770 for data professionals in the nonprofit sector. 14 00:00:34,770 --> 00:00:36,640 In particular, data can be 15 00:00:36,640 --> 00:00:38,680 applied in order to help these groups 16 00:00:38,680 --> 00:00:40,630 more effectively anticipate and 17 00:00:40,630 --> 00:00:43,400 respond to the greatest areas of need. 18 00:00:43,400 --> 00:00:47,030 For instance, maybe a US charity that provides 19 00:00:47,030 --> 00:00:48,890 bicycles for children would like to 20 00:00:48,890 --> 00:00:51,360 determine which neighborhoods are most in need. 21 00:00:51,360 --> 00:00:53,360 They could ask their data professional to 22 00:00:53,360 --> 00:00:55,825 access the US Census Bureau. 23 00:00:55,825 --> 00:00:57,500 The professional would use 24 00:00:57,500 --> 00:00:59,945 their talents to navigate the census database, 25 00:00:59,945 --> 00:01:02,330 identify key metrics, and summarize 26 00:01:02,330 --> 00:01:05,345 findings with analysis and data visualizations. 27 00:01:05,345 --> 00:01:07,085 This report would highlight 28 00:01:07,085 --> 00:01:08,750 where there are larger numbers of 29 00:01:08,750 --> 00:01:10,670 school-age children in need who would 30 00:01:10,670 --> 00:01:13,490 benefit from the resources of this program. 31 00:01:13,490 --> 00:01:16,610 There you go, data insights lead to inform 32 00:01:16,610 --> 00:01:18,830 decisions about where this nonprofit 33 00:01:18,830 --> 00:01:20,215 can do the most good. 34 00:01:20,215 --> 00:01:23,445 Now, nonprofits do more than use data. 35 00:01:23,445 --> 00:01:25,290 Many of them also collect it. 36 00:01:25,290 --> 00:01:26,945 As you likely know, 37 00:01:26,945 --> 00:01:29,330 public entities and government agencies can 38 00:01:29,330 --> 00:01:31,940 be excellent resources for useful data. 39 00:01:31,940 --> 00:01:35,945 Much of it is open data that's available for general use. 40 00:01:35,945 --> 00:01:38,360 As you likely know, open data 41 00:01:38,360 --> 00:01:40,840 is data that is available to the public. 42 00:01:40,840 --> 00:01:44,090 It's free to use, and guidance is provided to help 43 00:01:44,090 --> 00:01:47,380 navigate the data sets and acknowledge the source. 44 00:01:47,380 --> 00:01:49,340 While sourcing Open Data is 45 00:01:49,340 --> 00:01:51,400 a good way to interact with data on your own. 46 00:01:51,400 --> 00:01:53,450 There are other opportunities that enable 47 00:01:53,450 --> 00:01:56,095 you to refine your skills while helping others. 48 00:01:56,095 --> 00:01:59,390 Data volunteers contribute to many projects that help 49 00:01:59,390 --> 00:02:02,885 nonprofits benefit communities all over the world. 50 00:02:02,885 --> 00:02:04,850 To find out more, here are 51 00:02:04,850 --> 00:02:07,175 some organizations to check out. 52 00:02:07,175 --> 00:02:11,090 First, the Data Science for Social Good foundation was 53 00:02:11,090 --> 00:02:15,080 launched at the University of Chicago back in 2013. 54 00:02:15,080 --> 00:02:18,860 In 2020, they joined forces with UNICEF to analyze 55 00:02:18,860 --> 00:02:20,975 various aspects of air pollution 56 00:02:20,975 --> 00:02:24,100 around the world to help monitor children's health. 57 00:02:24,100 --> 00:02:26,750 DataKind launched in 2011 in 58 00:02:26,750 --> 00:02:29,300 New York City with chapters and the United Kingdom, 59 00:02:29,300 --> 00:02:33,160 Bengaluru, San Francisco, Singapore, and Washington DC. 60 00:02:33,160 --> 00:02:35,450 This organization analyzes the cost 61 00:02:35,450 --> 00:02:36,950 of environmental cleanup in 62 00:02:36,950 --> 00:02:38,870 different underserved communities 63 00:02:38,870 --> 00:02:41,080 to guide restorative efforts. 64 00:02:41,080 --> 00:02:43,520 You can view both foundations lays 65 00:02:43,520 --> 00:02:44,720 efforts through the links 66 00:02:44,720 --> 00:02:46,385 and the transcript for this video. 67 00:02:46,385 --> 00:02:47,960 Another option for putting 68 00:02:47,960 --> 00:02:50,530 your data skills to good use are hackathons. 69 00:02:50,530 --> 00:02:53,480 A hackathon is an event where data professionals and 70 00:02:53,480 --> 00:02:55,040 programmers come together and 71 00:02:55,040 --> 00:02:57,215 collaborate on a particular project. 72 00:02:57,215 --> 00:02:59,270 The goal is to create a solution to 73 00:02:59,270 --> 00:03:01,610 an existing problem using technology. 74 00:03:01,610 --> 00:03:03,380 Some examples include developing 75 00:03:03,380 --> 00:03:05,935 better tools for predicting extreme weather events, 76 00:03:05,935 --> 00:03:07,235 creating tech to help 77 00:03:07,235 --> 00:03:10,130 elementary school kids learn important reading skills, 78 00:03:10,130 --> 00:03:11,270 and identifying ways that 79 00:03:11,270 --> 00:03:13,190 community development groups can use 80 00:03:13,190 --> 00:03:14,930 their data to advance 81 00:03:14,930 --> 00:03:17,510 home accessibility and affordability. 82 00:03:17,510 --> 00:03:19,700 Volunteering your data skills to 83 00:03:19,700 --> 00:03:22,010 public projects is an excellent way to 84 00:03:22,010 --> 00:03:24,230 contribute to the greater good while gaining 85 00:03:24,230 --> 00:03:27,275 experience and networking with others in your field. 86 00:03:27,275 --> 00:03:30,995 Coming up, we'll take a deeper look at some data 87 00:03:30,995 --> 00:03:33,530 oriented projects in the public sector and 88 00:03:33,530 --> 00:03:37,080 how they're making an impact around the world.6457

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