diff --git a/public/pdfs/roadmaps/ai-data-scientist.pdf b/public/pdfs/roadmaps/ai-data-scientist.pdf index da6e9a400..eec919034 100644 Binary files a/public/pdfs/roadmaps/ai-data-scientist.pdf and b/public/pdfs/roadmaps/ai-data-scientist.pdf differ diff --git a/src/components/FrameRenderer/FrameRenderer.css b/src/components/FrameRenderer/FrameRenderer.css index a129e5c48..e2c9e0657 100644 --- a/src/components/FrameRenderer/FrameRenderer.css +++ b/src/components/FrameRenderer/FrameRenderer.css @@ -50,6 +50,9 @@ svg .clickable-group:hover > [fill='rgb(255,217,102)'] { svg .done rect { fill: #cbcbcb !important; +} + +svg .done rect[stroke="rgb(255,229,153)"] { stroke: #cbcbcb !important; } diff --git a/src/data/roadmaps/ai-data-scientist/ai-data-scientist.json b/src/data/roadmaps/ai-data-scientist/ai-data-scientist.json index a8972717f..807f12bcc 100644 --- a/src/data/roadmaps/ai-data-scientist/ai-data-scientist.json +++ b/src/data/roadmaps/ai-data-scientist/ai-data-scientist.json @@ -3,15 +3,15 @@ "controls": { "control": [ { - "ID": "1815", + "ID": "1959", "typeID": "Arrow", "zOrder": "0", "w": "1", "h": "318", "measuredW": "150", "measuredH": "100", - "x": "741", - "y": "2009", + "x": "751", + "y": "2019", "properties": { "curvature": "-1", "direction": "bottom", @@ -33,15 +33,15 @@ } }, { - "ID": "1816", + "ID": "1960", "typeID": "__group__", "zOrder": "1", "measuredW": "567", "measuredH": "49", "w": "567", "h": "49", - "x": "824", - "y": "2295", + "x": "834", + "y": "2305", "properties": { "controlName": "ext_link:github.com/gerdm/prml" }, @@ -81,15 +81,15 @@ } }, { - "ID": "1817", + "ID": "1961", "typeID": "Arrow", "zOrder": "2", "w": "660", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "740", - "y": "2011", + "x": "750", + "y": "2021", "properties": { "curvature": "-1", "direction": "bottom", @@ -111,28 +111,28 @@ } }, { - "ID": "1818", + "ID": "1962", "typeID": "Label", "zOrder": "3", - "measuredW": "262", + "measuredW": "302", "measuredH": "40", - "x": "206", - "y": "295", + "x": "201", + "y": "305", "properties": { "size": "32", - "text": "Machine Learning" + "text": "AI and Data Scientist" } }, { - "ID": "1819", + "ID": "1963", "typeID": "Arrow", "zOrder": "4", "w": "1", "h": "82", "measuredW": "150", "measuredH": "100", - "x": "336", - "y": "187", + "x": "346", + "y": "197", "properties": { "curvature": "-1", "direction": "bottom", @@ -155,15 +155,15 @@ } }, { - "ID": "1820", + "ID": "1964", "typeID": "Arrow", "zOrder": "5", "w": "1", "h": "104", "measuredW": "150", "measuredH": "100", - "x": "336", - "y": "367", + "x": "346", + "y": "377", "properties": { "curvature": "-1", "direction": "bottom", @@ -185,15 +185,15 @@ } }, { - "ID": "1821", + "ID": "1965", "typeID": "Arrow", "zOrder": "6", "w": "1110", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "296", - "y": "470", + "x": "306", + "y": "480", "properties": { "curvature": "-1", "direction": "bottom", @@ -215,42 +215,42 @@ } }, { - "ID": "1822", + "ID": "1966", "typeID": "TextArea", "zOrder": "7", "w": "216", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "268", - "y": "447", + "x": "278", + "y": "457", "properties": { "color": "16776960" } }, { - "ID": "1823", + "ID": "1967", "typeID": "Label", "zOrder": "8", "measuredW": "100", "measuredH": "25", - "x": "326", - "y": "460", + "x": "336", + "y": "470", "properties": { "size": "17", "text": "Mathematics" } }, { - "ID": "1824", + "ID": "1968", "typeID": "__group__", "zOrder": "9", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "224", - "y": "536", + "x": "234", + "y": "546", "properties": { "controlName": "check:linear-algebra-calc-mathana" }, @@ -273,15 +273,15 @@ } }, { - "ID": "1825", + "ID": "1969", "typeID": "__group__", "zOrder": "10", "measuredW": "429", "measuredH": "28", "w": "429", "h": "28", - "x": "269", - "y": "537", + "x": "279", + "y": "547", "properties": { "controlName": "linear-algebra-calc-mathana" }, @@ -306,15 +306,15 @@ } }, { - "ID": "1826", + "ID": "1970", "typeID": "__group__", "zOrder": "11", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "269", - "y": "573", + "x": "279", + "y": "583", "properties": { "controlName": "ext_link:coursera.org/specializations/mathematics-machine-learning#courses" }, @@ -369,15 +369,15 @@ } }, { - "ID": "1827", + "ID": "1971", "typeID": "__group__", "zOrder": "12", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "222", - "y": "640", + "x": "232", + "y": "650", "properties": { "controlName": "check:diff-calculus" }, @@ -400,15 +400,15 @@ } }, { - "ID": "1828", + "ID": "1972", "typeID": "__group__", "zOrder": "13", "measuredW": "181", "measuredH": "28", "w": "181", "h": "28", - "x": "268", - "y": "641", + "x": "278", + "y": "651", "properties": { "controlName": "diff-calculus" }, @@ -433,15 +433,15 @@ } }, { - "ID": "1829", + "ID": "1973", "typeID": "__group__", "zOrder": "14", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "268", - "y": "678", + "x": "278", + "y": "688", "properties": { "controlName": "ext_link:coursera.org/learn/algebra-and-differential-calculus-for-data-science#syllabus" }, @@ -495,42 +495,42 @@ } }, { - "ID": "1830", + "ID": "1974", "typeID": "TextArea", "zOrder": "15", "w": "154", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "895", - "y": "444", + "x": "905", + "y": "454", "properties": { "color": "16776960" } }, { - "ID": "1831", + "ID": "1975", "typeID": "Label", "zOrder": "16", "measuredW": "70", "measuredH": "25", - "x": "938", - "y": "456", + "x": "948", + "y": "466", "properties": { "size": "17", "text": "Statistics" } }, { - "ID": "1832", + "ID": "1976", "typeID": "__group__", "zOrder": "17", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "536", + "x": "858", + "y": "546", "properties": { "controlName": "check:stats-clt" }, @@ -553,15 +553,15 @@ } }, { - "ID": "1833", + "ID": "1977", "typeID": "__group__", "zOrder": "18", "measuredW": "127", "measuredH": "28", "w": "127", "h": "28", - "x": "893", - "y": "537", + "x": "903", + "y": "547", "properties": { "controlName": "stats-clt" }, @@ -586,15 +586,15 @@ } }, { - "ID": "1834", + "ID": "1978", "typeID": "__group__", "zOrder": "19", "measuredW": "467", "measuredH": "49", "w": "467", "h": "49", - "x": "893", - "y": "572", + "x": "903", + "y": "582", "properties": { "controlName": "ext_link:coursera.org/learn/stanford-statistics#syllabus" }, @@ -648,15 +648,15 @@ } }, { - "ID": "1835", + "ID": "1979", "typeID": "__group__", "zOrder": "20", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "636", + "x": "858", + "y": "646", "properties": { "controlName": "check:hypothesis-testing" }, @@ -679,15 +679,15 @@ } }, { - "ID": "1836", + "ID": "1980", "typeID": "__group__", "zOrder": "21", "measuredW": "171", "measuredH": "28", "w": "171", "h": "28", - "x": "893", - "y": "637", + "x": "903", + "y": "647", "properties": { "controlName": "hypothesis-testing" }, @@ -712,13 +712,13 @@ } }, { - "ID": "1837", + "ID": "1981", "typeID": "Label", "zOrder": "22", "measuredW": "22", "measuredH": "36", - "x": "645", - "y": "453", + "x": "655", + "y": "463", "properties": { "color": "10027263", "size": "28", @@ -726,15 +726,15 @@ } }, { - "ID": "1838", + "ID": "1982", "typeID": "__group__", "zOrder": "23", "measuredW": "467", "measuredH": "49", "w": "467", "h": "49", - "x": "894", - "y": "676", + "x": "904", + "y": "686", "properties": { "controlName": "ext_link:coursera.org/learn/statistical-analysis-hypothesis-testing-sas#syllabus" }, @@ -788,15 +788,15 @@ } }, { - "ID": "1839", + "ID": "1983", "typeID": "__group__", "zOrder": "24", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "740", + "x": "858", + "y": "750", "properties": { "controlName": "check:probability-sampling" }, @@ -819,15 +819,15 @@ } }, { - "ID": "1840", + "ID": "1984", "typeID": "__group__", "zOrder": "25", "measuredW": "221", "measuredH": "28", "w": "221", "h": "28", - "x": "893", - "y": "741", + "x": "903", + "y": "751", "properties": { "controlName": "probability-sampling" }, @@ -852,15 +852,15 @@ } }, { - "ID": "1841", + "ID": "1985", "typeID": "__group__", "zOrder": "26", "measuredW": "467", "measuredH": "49", "w": "467", "h": "49", - "x": "894", - "y": "782", + "x": "904", + "y": "792", "properties": { "controlName": "ext_link:coursera.org/learn/probability-statistics#syllabus" }, @@ -914,15 +914,15 @@ } }, { - "ID": "1842", + "ID": "1986", "typeID": "__group__", "zOrder": "27", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "852", + "x": "858", + "y": "862", "properties": { "controlName": "check:ab-testing" }, @@ -945,15 +945,15 @@ } }, { - "ID": "1843", + "ID": "1987", "typeID": "__group__", "zOrder": "28", "measuredW": "97", "measuredH": "28", "w": "97", "h": "28", - "x": "893", - "y": "853", + "x": "903", + "y": "863", "properties": { "controlName": "ab-testing" }, @@ -978,15 +978,15 @@ } }, { - "ID": "1844", + "ID": "1988", "typeID": "__group__", "zOrder": "29", "measuredW": "467", "measuredH": "49", "w": "467", "h": "49", - "x": "894", - "y": "894", + "x": "904", + "y": "904", "properties": { "controlName": "ext_link:vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b" }, @@ -1040,15 +1040,15 @@ } }, { - "ID": "1845", + "ID": "1989", "typeID": "__group__", "zOrder": "30", "measuredW": "467", "measuredH": "49", "w": "467", "h": "49", - "x": "894", - "y": "948", + "x": "904", + "y": "958", "properties": { "controlName": "ext_link:towardsdatascience.com/step-by-step-for-planning-an-a-b-test-ef3c93143c0b" }, @@ -1102,15 +1102,15 @@ } }, { - "ID": "1846", + "ID": "1990", "typeID": "__group__", "zOrder": "31", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "1021", + "x": "858", + "y": "1031", "properties": { "controlName": "check:increasing-test-sensitivity" }, @@ -1133,15 +1133,15 @@ } }, { - "ID": "1847", + "ID": "1991", "typeID": "__group__", "zOrder": "32", "measuredW": "233", "measuredH": "28", "w": "233", "h": "28", - "x": "893", - "y": "1022", + "x": "903", + "y": "1032", "properties": { "controlName": "increasing-test-sensitivity" }, @@ -1166,15 +1166,15 @@ } }, { - "ID": "1848", + "ID": "1992", "typeID": "__group__", "zOrder": "33", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "893", - "y": "1066", + "x": "903", + "y": "1076", "properties": { "controlName": "ext_link:splitmetrics.com/resources/minimum-detectable-effect-mde/" }, @@ -1228,15 +1228,15 @@ } }, { - "ID": "1849", + "ID": "1993", "typeID": "__group__", "zOrder": "34", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "893", - "y": "1114", + "x": "903", + "y": "1124", "properties": { "controlName": "ext_link:kdd.org/kdd2016/papers/files/adp0945-xieA.pdf" }, @@ -1290,15 +1290,15 @@ } }, { - "ID": "1850", + "ID": "1994", "typeID": "__group__", "zOrder": "35", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "893", - "y": "1163", + "x": "903", + "y": "1173", "properties": { "controlName": "ext_link:exp-platform.com/Documents/2013-02-CUPED-ImprovingSensitivityOfControlledExperiments.pdf" }, @@ -1352,15 +1352,15 @@ } }, { - "ID": "1851", + "ID": "1995", "typeID": "__group__", "zOrder": "36", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "893", - "y": "1212", + "x": "903", + "y": "1222", "properties": { "controlName": "ext_link:booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d" }, @@ -1414,15 +1414,15 @@ } }, { - "ID": "1852", + "ID": "1996", "typeID": "__group__", "zOrder": "37", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "893", - "y": "1260", + "x": "903", + "y": "1270", "properties": { "controlName": "ext_link:doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/" }, @@ -1476,15 +1476,15 @@ } }, { - "ID": "1853", + "ID": "1997", "typeID": "__group__", "zOrder": "38", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "848", - "y": "1381", + "x": "858", + "y": "1391", "properties": { "controlName": "check:ratio-metrics" }, @@ -1507,15 +1507,15 @@ } }, { - "ID": "1854", + "ID": "1998", "typeID": "__group__", "zOrder": "39", "measuredW": "121", "measuredH": "28", "w": "121", "h": "28", - "x": "893", - "y": "1384", + "x": "903", + "y": "1394", "properties": { "controlName": "ratio-metrics" }, @@ -1540,15 +1540,15 @@ } }, { - "ID": "1855", + "ID": "1999", "typeID": "__group__", "zOrder": "40", "measuredW": "459", "measuredH": "51", "w": "459", "h": "51", - "x": "893", - "y": "1425", + "x": "903", + "y": "1435", "properties": { "controlName": "ext_link:arxiv.org/pdf/1803.06336.pdf" }, @@ -1602,15 +1602,15 @@ } }, { - "ID": "1856", + "ID": "2000", "typeID": "__group__", "zOrder": "41", "measuredW": "459", "measuredH": "51", "w": "459", "h": "51", - "x": "893", - "y": "1481", + "x": "903", + "y": "1491", "properties": { "controlName": "ext_link:stat.cmu.edu/~hseltman/files/ratio.pdf" }, @@ -1664,15 +1664,15 @@ } }, { - "ID": "1857", + "ID": "2001", "typeID": "Arrow", "zOrder": "42", "w": "1", "h": "1092", "measuredW": "150", "measuredH": "100", - "x": "1405", - "y": "471", + "x": "1415", + "y": "481", "properties": { "curvature": "-1", "direction": "bottom", @@ -1694,13 +1694,13 @@ } }, { - "ID": "1858", + "ID": "2002", "typeID": "Label", "zOrder": "43", "measuredW": "22", "measuredH": "36", - "x": "1214", - "y": "452", + "x": "1224", + "y": "462", "properties": { "color": "10027263", "size": "28", @@ -1708,13 +1708,13 @@ } }, { - "ID": "1859", + "ID": "2003", "typeID": "Label", "zOrder": "44", "measuredW": "14", "measuredH": "36", - "x": "1399", - "y": "575", + "x": "1409", + "y": "585", "properties": { "size": "28", "text": "v", @@ -1722,13 +1722,13 @@ } }, { - "ID": "1860", + "ID": "2004", "typeID": "Label", "zOrder": "45", "measuredW": "14", "measuredH": "36", - "x": "1399", - "y": "1165", + "x": "1409", + "y": "1175", "properties": { "size": "28", "text": "v", @@ -1736,15 +1736,15 @@ } }, { - "ID": "1861", + "ID": "2005", "typeID": "Arrow", "zOrder": "46", "w": "591", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "815", - "y": "1563", + "x": "825", + "y": "1573", "properties": { "curvature": "-1", "direction": "bottom", @@ -1766,13 +1766,13 @@ } }, { - "ID": "1862", + "ID": "2006", "typeID": "Label", "zOrder": "47", "measuredW": "20", "measuredH": "36", - "x": "1124", - "y": "1544", + "x": "1134", + "y": "1554", "properties": { "color": "10027263", "size": "28", @@ -1780,15 +1780,15 @@ } }, { - "ID": "1863", + "ID": "2007", "typeID": "Arrow", "zOrder": "48", "w": "1", "h": "739", "measuredW": "150", "measuredH": "100", - "x": "815", - "y": "824", + "x": "825", + "y": "834", "properties": { "curvature": "-1", "direction": "bottom", @@ -1810,13 +1810,13 @@ } }, { - "ID": "1864", + "ID": "2008", "typeID": "Label", "zOrder": "49", "measuredW": "17", "measuredH": "40", - "x": "806", - "y": "1206", + "x": "816", + "y": "1216", "properties": { "color": "10027263", "size": "32", @@ -1824,15 +1824,15 @@ } }, { - "ID": "1865", + "ID": "2009", "typeID": "Arrow", "zOrder": "50", "w": "638", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "178", - "y": "824", + "x": "188", + "y": "834", "properties": { "curvature": "-1", "direction": "bottom", @@ -1854,13 +1854,13 @@ } }, { - "ID": "1866", + "ID": "2010", "typeID": "Label", "zOrder": "51", "measuredW": "20", "measuredH": "36", - "x": "609", - "y": "805", + "x": "619", + "y": "815", "properties": { "color": "10027263", "size": "28", @@ -1868,42 +1868,42 @@ } }, { - "ID": "1867", + "ID": "2011", "typeID": "TextArea", "zOrder": "52", "w": "169", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "259", - "y": "798", + "x": "269", + "y": "808", "properties": { "color": "16776960" } }, { - "ID": "1868", + "ID": "2012", "typeID": "Label", "zOrder": "53", "measuredW": "107", "measuredH": "25", - "x": "291", - "y": "811", + "x": "301", + "y": "821", "properties": { "size": "17", "text": "Econometrics" } }, { - "ID": "1869", + "ID": "2013", "typeID": "__group__", "zOrder": "54", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "214", - "y": "887", + "x": "224", + "y": "897", "properties": { "controlName": "check:econometrics-pre-req" }, @@ -1926,15 +1926,15 @@ } }, { - "ID": "1870", + "ID": "2014", "typeID": "__group__", "zOrder": "55", "measuredW": "281", "measuredH": "28", "w": "281", "h": "28", - "x": "259", - "y": "888", + "x": "269", + "y": "898", "properties": { "controlName": "econometrics-pre-req" }, @@ -1959,15 +1959,15 @@ } }, { - "ID": "1871", + "ID": "2015", "typeID": "__group__", "zOrder": "56", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "924", + "x": "269", + "y": "934", "properties": { "controlName": "ext_link:bookdown.org/ts_robinson1994/10EconometricTheorems/" }, @@ -2021,15 +2021,15 @@ } }, { - "ID": "1872", + "ID": "2016", "typeID": "__group__", "zOrder": "57", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "214", - "y": "997", + "x": "224", + "y": "1007", "properties": { "controlName": "check:regression-time-series-fitting-distr" }, @@ -2052,15 +2052,15 @@ } }, { - "ID": "1873", + "ID": "2017", "typeID": "__group__", "zOrder": "58", "measuredW": "396", "measuredH": "28", "w": "396", "h": "28", - "x": "259", - "y": "998", + "x": "269", + "y": "1008", "properties": { "controlName": "regression-time-series-fitting-distr" }, @@ -2085,15 +2085,15 @@ } }, { - "ID": "1874", + "ID": "2018", "typeID": "__group__", "zOrder": "59", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1037", + "x": "269", + "y": "1047", "properties": { "controlName": "ext_link:academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small" }, @@ -2147,15 +2147,15 @@ } }, { - "ID": "1875", + "ID": "2019", "typeID": "__group__", "zOrder": "60", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1092", + "x": "269", + "y": "1102", "properties": { "controlName": "ext_link:coursera.org/learn/erasmus-econometrics#syllabus" }, @@ -2209,15 +2209,15 @@ } }, { - "ID": "1876", + "ID": "2020", "typeID": "__group__", "zOrder": "61", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1167", + "x": "269", + "y": "1177", "properties": { "controlName": "ext_link:kaggle.com/learn/time-series" }, @@ -2271,15 +2271,15 @@ } }, { - "ID": "1877", + "ID": "2021", "typeID": "__group__", "zOrder": "62", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1221", + "x": "269", + "y": "1231", "properties": { "controlName": "ext_link:kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series" }, @@ -2333,15 +2333,15 @@ } }, { - "ID": "1878", + "ID": "2022", "typeID": "__group__", "zOrder": "63", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1275", + "x": "269", + "y": "1285", "properties": { "controlName": "ext_link:machinelearningmastery.com/arima-for-time-series-forecasting-with-python" }, @@ -2395,15 +2395,15 @@ } }, { - "ID": "1879", + "ID": "2023", "typeID": "__group__", "zOrder": "64", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1329", + "x": "269", + "y": "1339", "properties": { "controlName": "ext_link:machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/" }, @@ -2457,15 +2457,15 @@ } }, { - "ID": "1880", + "ID": "2024", "typeID": "__group__", "zOrder": "65", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1383", + "x": "269", + "y": "1393", "properties": { "controlName": "ext_link:github.com/stalkermustang/bcdc_ds_takehome" }, @@ -2519,15 +2519,15 @@ } }, { - "ID": "1881", + "ID": "2025", "typeID": "__group__", "zOrder": "66", "measuredW": "495", "measuredH": "49", "w": "495", "h": "49", - "x": "259", - "y": "1454", + "x": "269", + "y": "1464", "properties": { "controlName": "ext_link:coursera.org/learn/linear-regression-business-statistics#about" }, @@ -2581,15 +2581,15 @@ } }, { - "ID": "1882", + "ID": "2026", "typeID": "Arrow", "zOrder": "67", "w": "1", "h": "803", "measuredW": "150", "measuredH": "100", - "x": "179", - "y": "825", + "x": "189", + "y": "835", "properties": { "curvature": "-1", "direction": "bottom", @@ -2611,13 +2611,13 @@ } }, { - "ID": "1883", + "ID": "2027", "typeID": "Label", "zOrder": "68", "measuredW": "14", "measuredH": "36", - "x": "172", - "y": "973", + "x": "182", + "y": "983", "properties": { "size": "28", "text": "v", @@ -2625,13 +2625,13 @@ } }, { - "ID": "1884", + "ID": "2028", "typeID": "Label", "zOrder": "69", "measuredW": "14", "measuredH": "36", - "x": "172", - "y": "1268", + "x": "182", + "y": "1278", "properties": { "size": "28", "text": "v", @@ -2639,15 +2639,15 @@ } }, { - "ID": "1885", + "ID": "2029", "typeID": "Arrow", "zOrder": "70", "w": "1221", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "179", - "y": "1630", + "x": "189", + "y": "1640", "properties": { "curvature": "-1", "direction": "bottom", @@ -2669,42 +2669,42 @@ } }, { - "ID": "1886", + "ID": "2030", "typeID": "TextArea", "zOrder": "71", "w": "183", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "243", - "y": "1605", + "x": "253", + "y": "1615", "properties": { "color": "16776960" } }, { - "ID": "1887", + "ID": "2031", "typeID": "Label", "zOrder": "72", "measuredW": "55", "measuredH": "25", - "x": "307", - "y": "1618", + "x": "317", + "y": "1628", "properties": { "size": "17", "text": "Coding" } }, { - "ID": "1888", + "ID": "2032", "typeID": "__group__", "zOrder": "73", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "201", - "y": "1695", + "x": "211", + "y": "1705", "properties": { "controlName": "check:learn-python" }, @@ -2727,15 +2727,15 @@ } }, { - "ID": "1889", + "ID": "2033", "typeID": "__group__", "zOrder": "74", "measuredW": "343", "measuredH": "28", "w": "343", "h": "28", - "x": "246", - "y": "1696", + "x": "256", + "y": "1706", "properties": { "controlName": "learn-python" }, @@ -2760,15 +2760,15 @@ } }, { - "ID": "1890", + "ID": "2034", "typeID": "__group__", "zOrder": "75", "measuredW": "444", "measuredH": "49", "w": "444", "h": "49", - "x": "245", - "y": "1744", + "x": "255", + "y": "1754", "properties": { "controlName": "ext_link:kaggle.com/learn/python" }, @@ -2822,15 +2822,15 @@ } }, { - "ID": "1891", + "ID": "2035", "typeID": "__group__", "zOrder": "76", "measuredW": "444", "measuredH": "49", "w": "444", "h": "49", - "x": "245", - "y": "1798", + "x": "255", + "y": "1808", "properties": { "controlName": "ext_link:developers.google.com/edu/python" }, @@ -2884,15 +2884,15 @@ } }, { - "ID": "1892", + "ID": "2036", "typeID": "__group__", "zOrder": "77", "measuredW": "441", "measuredH": "49", "w": "441", "h": "49", - "x": "245", - "y": "1916", + "x": "255", + "y": "1926", "properties": { "controlName": "ext_link:leetcode.com/explore/learn/" }, @@ -2946,15 +2946,15 @@ } }, { - "ID": "1893", + "ID": "2037", "typeID": "__group__", "zOrder": "78", "measuredW": "444", "measuredH": "49", "w": "444", "h": "49", - "x": "245", - "y": "1971", + "x": "255", + "y": "1981", "properties": { "controlName": "ext_link:leetcode.com/studyplan" }, @@ -3008,15 +3008,15 @@ } }, { - "ID": "1894", + "ID": "2038", "typeID": "__group__", "zOrder": "79", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "245", - "y": "2027", + "x": "255", + "y": "2037", "properties": { "controlName": "ext_link:coursera.org/specializations/algorithms#courses" }, @@ -3070,15 +3070,15 @@ } }, { - "ID": "1895", + "ID": "2039", "typeID": "__group__", "zOrder": "80", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "201", - "y": "1875", + "x": "211", + "y": "1885", "properties": { "controlName": "check:learn-dsa" }, @@ -3101,15 +3101,15 @@ } }, { - "ID": "1896", + "ID": "2040", "typeID": "__group__", "zOrder": "81", "measuredW": "363", "measuredH": "28", "w": "363", "h": "28", - "x": "246", - "y": "1876", + "x": "256", + "y": "1886", "properties": { "controlName": "learn-dsa" }, @@ -3134,15 +3134,15 @@ } }, { - "ID": "1897", + "ID": "2041", "typeID": "__group__", "zOrder": "82", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "201", - "y": "2103", + "x": "211", + "y": "2113", "properties": { "controlName": "check:learn-sql" }, @@ -3165,15 +3165,15 @@ } }, { - "ID": "1898", + "ID": "2042", "typeID": "__group__", "zOrder": "83", "measuredW": "97", "measuredH": "28", "w": "97", "h": "28", - "x": "246", - "y": "2104", + "x": "256", + "y": "2114", "properties": { "controlName": "learn-sql" }, @@ -3198,15 +3198,15 @@ } }, { - "ID": "1899", + "ID": "2043", "typeID": "__group__", "zOrder": "84", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "245", - "y": "2144", + "x": "255", + "y": "2154", "properties": { "controlName": "ext_link:sqltutorial.org" }, @@ -3260,13 +3260,13 @@ } }, { - "ID": "1900", + "ID": "2044", "typeID": "Label", "zOrder": "85", "measuredW": "22", "measuredH": "36", - "x": "603", - "y": "1613", + "x": "613", + "y": "1623", "properties": { "color": "10027263", "size": "28", @@ -3274,42 +3274,42 @@ } }, { - "ID": "1901", + "ID": "2045", "typeID": "TextArea", "zOrder": "86", "w": "301", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "801", - "y": "1604", + "x": "811", + "y": "1614", "properties": { "color": "16776960" } }, { - "ID": "1902", + "ID": "2046", "typeID": "Label", "zOrder": "87", "measuredW": "244", "measuredH": "25", - "x": "830", - "y": "1617", + "x": "840", + "y": "1627", "properties": { "size": "17", "text": "Exploratory Data Analysis (EDA)" } }, { - "ID": "1903", + "ID": "2047", "typeID": "__group__", "zOrder": "88", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "754", - "y": "1695", + "x": "764", + "y": "1705", "properties": { "controlName": "check:data-understanding" }, @@ -3332,15 +3332,15 @@ } }, { - "ID": "1904", + "ID": "2048", "typeID": "__group__", "zOrder": "89", "measuredW": "430", "measuredH": "28", "w": "430", "h": "28", - "x": "799", - "y": "1696", + "x": "809", + "y": "1706", "properties": { "controlName": "data-understanding" }, @@ -3365,15 +3365,15 @@ } }, { - "ID": "1905", + "ID": "2049", "typeID": "__group__", "zOrder": "90", "measuredW": "546", "measuredH": "49", "w": "546", "h": "49", - "x": "800", - "y": "1739", + "x": "810", + "y": "1749", "properties": { "controlName": "ext_link:coursera.org/projects/exploratory-data-analysis-python-pandas" }, @@ -3427,15 +3427,15 @@ } }, { - "ID": "1906", + "ID": "2050", "typeID": "__group__", "zOrder": "91", "measuredW": "546", "measuredH": "49", "w": "546", "h": "49", - "x": "801", - "y": "1794", + "x": "811", + "y": "1804", "properties": { "controlName": "ext_link:coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning#syllabus" }, @@ -3489,15 +3489,15 @@ } }, { - "ID": "1907", + "ID": "2051", "typeID": "__group__", "zOrder": "92", "measuredW": "546", "measuredH": "49", "w": "546", "h": "49", - "x": "801", - "y": "1850", + "x": "811", + "y": "1860", "properties": { "controlName": "ext_link:coursera.org/projects/exploratory-data-analysis-seaborn" }, @@ -3551,13 +3551,13 @@ } }, { - "ID": "1908", + "ID": "2052", "typeID": "Label", "zOrder": "93", "measuredW": "22", "measuredH": "36", - "x": "1237", - "y": "1613", + "x": "1247", + "y": "1623", "properties": { "color": "10027263", "size": "28", @@ -3565,15 +3565,15 @@ } }, { - "ID": "1909", + "ID": "2053", "typeID": "Arrow", "zOrder": "94", "w": "1", "h": "378", "measuredW": "150", "measuredH": "100", - "x": "1401", - "y": "1630", + "x": "1411", + "y": "1640", "properties": { "curvature": "-1", "direction": "bottom", @@ -3595,13 +3595,13 @@ } }, { - "ID": "1910", + "ID": "2054", "typeID": "Label", "zOrder": "95", "measuredW": "14", "measuredH": "36", - "x": "1395", - "y": "1805", + "x": "1405", + "y": "1815", "properties": { "size": "28", "text": "v", @@ -3609,40 +3609,40 @@ } }, { - "ID": "1911", + "ID": "2055", "typeID": "TextArea", "zOrder": "96", "w": "208", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "822", - "y": "1986", + "x": "832", + "y": "1996", "properties": { "color": "16776960" } }, { - "ID": "1912", + "ID": "2056", "typeID": "Label", "zOrder": "97", "measuredW": "140", "measuredH": "25", - "x": "856", - "y": "1999", + "x": "866", + "y": "2009", "properties": { "size": "17", "text": "Machine Learning" } }, { - "ID": "1913", + "ID": "2057", "typeID": "Label", "zOrder": "98", "measuredW": "20", "measuredH": "36", - "x": "1212", - "y": "1993", + "x": "1222", + "y": "2003", "properties": { "color": "10027263", "size": "28", @@ -3650,15 +3650,15 @@ } }, { - "ID": "1914", + "ID": "2058", "typeID": "__group__", "zOrder": "99", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "777", - "y": "2071", + "x": "787", + "y": "2081", "properties": { "controlName": "check:classic-advanced-ml" }, @@ -3681,15 +3681,15 @@ } }, { - "ID": "1915", + "ID": "2059", "typeID": "__group__", "zOrder": "100", "measuredW": "562", "measuredH": "28", "w": "562", "h": "28", - "x": "824", - "y": "2073", + "x": "834", + "y": "2083", "properties": { "controlName": "classic-advanced-ml" }, @@ -3714,15 +3714,15 @@ } }, { - "ID": "1916", + "ID": "2060", "typeID": "__group__", "zOrder": "101", "measuredW": "569", "measuredH": "49", "w": "569", "h": "49", - "x": "824", - "y": "2121", + "x": "834", + "y": "2131", "properties": { "controlName": "ext_link:mlcourse.ai/book/topic01/topic01_intro.html" }, @@ -3776,15 +3776,15 @@ } }, { - "ID": "1917", + "ID": "2061", "typeID": "__group__", "zOrder": "102", "measuredW": "567", "measuredH": "49", "w": "567", "h": "49", - "x": "824", - "y": "2175", + "x": "834", + "y": "2185", "properties": { "controlName": "ext_link:coursera.org/specializations/machine-learning-introduction#courses" }, @@ -3838,15 +3838,15 @@ } }, { - "ID": "1918", + "ID": "2062", "typeID": "__group__", "zOrder": "103", "measuredW": "567", "measuredH": "49", "w": "567", "h": "49", - "x": "824", - "y": "2242", + "x": "834", + "y": "2252", "properties": { "controlName": "ext_link:microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf" }, @@ -3900,13 +3900,13 @@ } }, { - "ID": "1919", + "ID": "2063", "typeID": "Label", "zOrder": "104", "measuredW": "14", "measuredH": "36", - "x": "735", - "y": "2175", + "x": "745", + "y": "2185", "properties": { "size": "28", "text": "v", @@ -3914,15 +3914,15 @@ } }, { - "ID": "1920", + "ID": "2064", "typeID": "Arrow", "zOrder": "105", "w": "562", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "178", - "y": "2328", + "x": "188", + "y": "2338", "properties": { "curvature": "-1", "direction": "bottom", @@ -3944,42 +3944,42 @@ } }, { - "ID": "1921", + "ID": "2065", "typeID": "TextArea", "zOrder": "106", "w": "192", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "257", - "y": "2302", + "x": "267", + "y": "2312", "properties": { "color": "16776960" } }, { - "ID": "1922", + "ID": "2066", "typeID": "Label", "zOrder": "107", "measuredW": "115", "measuredH": "25", - "x": "295", - "y": "2315", + "x": "305", + "y": "2325", "properties": { "size": "17", "text": "Deep Learning" } }, { - "ID": "1923", + "ID": "2067", "typeID": "__group__", "zOrder": "108", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "214", - "y": "2385", + "x": "224", + "y": "2395", "properties": { "controlName": "check:fully-connected-nn" }, @@ -4002,15 +4002,15 @@ } }, { - "ID": "1924", + "ID": "2068", "typeID": "__group__", "zOrder": "109", "measuredW": "642", "measuredH": "28", "w": "642", "h": "28", - "x": "258", - "y": "2387", + "x": "268", + "y": "2397", "properties": { "controlName": "fully-connected-nn" }, @@ -4035,15 +4035,15 @@ } }, { - "ID": "1925", + "ID": "2069", "typeID": "__group__", "zOrder": "110", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "258", - "y": "2435", + "x": "268", + "y": "2445", "properties": { "controlName": "ext_link:coursera.org/specializations/deep-learning#courses" }, @@ -4097,15 +4097,15 @@ } }, { - "ID": "1926", + "ID": "2070", "typeID": "__group__", "zOrder": "111", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "258", - "y": "2491", + "x": "268", + "y": "2501", "properties": { "controlName": "ext_link:deeplearningbook.org" }, @@ -4159,15 +4159,15 @@ } }, { - "ID": "1927", + "ID": "2071", "typeID": "__group__", "zOrder": "112", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "258", - "y": "2548", + "x": "268", + "y": "2558", "properties": { "controlName": "ext_link:arxiv.org/pdf/1706.03762.pdf" }, @@ -4221,15 +4221,15 @@ } }, { - "ID": "1928", + "ID": "2072", "typeID": "__group__", "zOrder": "113", "measuredW": "444", "measuredH": "51", "w": "444", "h": "51", - "x": "258", - "y": "2605", + "x": "268", + "y": "2615", "properties": { "controlName": "ext_link:jalammar.github.io/illustrated-transformer/" }, @@ -4283,15 +4283,15 @@ } }, { - "ID": "1929", + "ID": "2073", "typeID": "Arrow", "zOrder": "114", "w": "1", "h": "387", "measuredW": "150", "measuredH": "100", - "x": "179", - "y": "2329", + "x": "189", + "y": "2339", "properties": { "curvature": "-1", "direction": "bottom", @@ -4313,13 +4313,13 @@ } }, { - "ID": "1930", + "ID": "2074", "typeID": "Label", "zOrder": "115", "measuredW": "14", "measuredH": "36", - "x": "173", - "y": "2534", + "x": "183", + "y": "2544", "properties": { "size": "28", "text": "v", @@ -4327,15 +4327,15 @@ } }, { - "ID": "1931", + "ID": "2075", "typeID": "Arrow", "zOrder": "116", "w": "649", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "178", - "y": "2716", + "x": "188", + "y": "2726", "properties": { "curvature": "-1", "direction": "bottom", @@ -4357,15 +4357,15 @@ } }, { - "ID": "1932", + "ID": "2076", "typeID": "Arrow", "zOrder": "117", "w": "1", "h": "228", "measuredW": "150", "measuredH": "100", - "x": "829", - "y": "2488", + "x": "839", + "y": "2498", "properties": { "curvature": "-1", "direction": "bottom", @@ -4387,13 +4387,13 @@ } }, { - "ID": "1933", + "ID": "2077", "typeID": "Label", "zOrder": "118", "measuredW": "22", "measuredH": "36", - "x": "492", - "y": "2698", + "x": "502", + "y": "2708", "properties": { "color": "10027263", "size": "28", @@ -4401,13 +4401,13 @@ } }, { - "ID": "1934", + "ID": "2078", "typeID": "Label", "zOrder": "119", "measuredW": "20", "measuredH": "36", - "x": "608", - "y": "2309", + "x": "618", + "y": "2319", "properties": { "color": "10027263", "size": "28", @@ -4415,13 +4415,13 @@ } }, { - "ID": "1935", + "ID": "2079", "typeID": "Label", "zOrder": "120", "measuredW": "17", "measuredH": "40", - "x": "821", - "y": "2613", + "x": "831", + "y": "2623", "properties": { "color": "10027263", "size": "32", @@ -4429,15 +4429,15 @@ } }, { - "ID": "1936", + "ID": "2080", "typeID": "Arrow", "zOrder": "121", "w": "553", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "829", - "y": "2488", + "x": "839", + "y": "2498", "properties": { "curvature": "-1", "direction": "bottom", @@ -4459,42 +4459,42 @@ } }, { - "ID": "1937", + "ID": "2081", "typeID": "TextArea", "zOrder": "122", "w": "126", "h": "50", "measuredW": "200", "measuredH": "140", - "x": "879", - "y": "2463", + "x": "889", + "y": "2473", "properties": { "color": "16776960" } }, { - "ID": "1938", + "ID": "2082", "typeID": "Label", "zOrder": "123", "measuredW": "56", "measuredH": "25", - "x": "914", - "y": "2475", + "x": "924", + "y": "2485", "properties": { "size": "17", "text": "MLOps" } }, { - "ID": "1939", + "ID": "2083", "typeID": "__group__", "zOrder": "124", "measuredW": "30", "measuredH": "30", "w": "30", "h": "30", - "x": "881", - "y": "2549", + "x": "891", + "y": "2559", "properties": { "controlName": "check:deployment-models" }, @@ -4517,15 +4517,15 @@ } }, { - "ID": "1940", + "ID": "2084", "typeID": "__group__", "zOrder": "125", "measuredW": "247", "measuredH": "28", "w": "247", "h": "28", - "x": "928", - "y": "2551", + "x": "938", + "y": "2561", "properties": { "controlName": "deployment-models" }, @@ -4550,15 +4550,15 @@ } }, { - "ID": "1941", + "ID": "2085", "typeID": "__group__", "zOrder": "126", "measuredW": "408", "measuredH": "49", "w": "408", "h": "49", - "x": "928", - "y": "2596", + "x": "938", + "y": "2606", "properties": { "controlName": "ext_link:coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses" }, @@ -4612,15 +4612,15 @@ } }, { - "ID": "1942", + "ID": "2086", "typeID": "Arrow", "zOrder": "127", "w": "1", "h": "332", "measuredW": "150", "measuredH": "100", - "x": "1382", - "y": "2488", + "x": "1392", + "y": "2498", "properties": { "curvature": "-1", "direction": "bottom", @@ -4642,13 +4642,13 @@ } }, { - "ID": "1943", + "ID": "2087", "typeID": "Label", "zOrder": "128", "measuredW": "14", "measuredH": "36", - "x": "1376", - "y": "2648", + "x": "1386", + "y": "2658", "properties": { "size": "28", "text": "v", @@ -4656,15 +4656,15 @@ } }, { - "ID": "1944", + "ID": "2088", "typeID": "Arrow", "zOrder": "129", "w": "649", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "733", - "y": "2820", + "x": "743", + "y": "2830", "properties": { "curvature": "-1", "direction": "bottom", @@ -4686,14 +4686,14 @@ } }, { - "ID": "1945", + "ID": "2089", "typeID": "Arrow", "zOrder": "130", "w": "1", "measuredW": "150", "measuredH": "100", - "x": "733", - "y": "2820", + "x": "743", + "y": "2830", "properties": { "curvature": "-1", "direction": "bottom", @@ -4715,13 +4715,13 @@ } }, { - "ID": "1946", + "ID": "2090", "typeID": "Label", "zOrder": "131", "measuredW": "22", "measuredH": "36", - "x": "1159", - "y": "2471", + "x": "1169", + "y": "2481", "properties": { "color": "10027263", "size": "28", @@ -4729,13 +4729,13 @@ } }, { - "ID": "1947", + "ID": "2091", "typeID": "Label", "zOrder": "132", "measuredW": "20", "measuredH": "36", - "x": "1018", - "y": "2802", + "x": "1028", + "y": "2812", "properties": { "color": "10027263", "size": "28", @@ -4743,28 +4743,28 @@ } }, { - "ID": "1948", + "ID": "2092", "typeID": "Label", "zOrder": "133", "measuredW": "187", "measuredH": "36", - "x": "651", - "y": "2935", + "x": "661", + "y": "2945", "properties": { "size": "28", "text": "Keep Learning" } }, { - "ID": "1949", + "ID": "2093", "typeID": "Arrow", "zOrder": "134", "w": "1", "h": "63", "measuredW": "150", "measuredH": "100", - "x": "732", - "y": "2987", + "x": "742", + "y": "2997", "properties": { "curvature": "-1", "direction": "bottom", @@ -4787,15 +4787,15 @@ } }, { - "ID": "1950", + "ID": "2094", "typeID": "__group__", "zOrder": "135", "measuredW": "365", "measuredH": "141", "w": "365", "h": "141", - "x": "1044", - "y": "230", + "x": "1054", + "y": "240", "children": { "controls": { "control": [ @@ -4891,15 +4891,15 @@ } }, { - "ID": "1951", + "ID": "2095", "typeID": "__group__", "zOrder": "136", "measuredW": "473", "measuredH": "140", "w": "473", "h": "140", - "x": "554", - "y": "231", + "x": "564", + "y": "241", "children": { "controls": { "control": [ @@ -4945,15 +4945,15 @@ } }, { - "ID": "1952", + "ID": "2096", "typeID": "__group__", "zOrder": "137", "measuredW": "151", "measuredH": "26", "w": "151", "h": "26", - "x": "570", - "y": "324", + "x": "580", + "y": "334", "properties": { "controlName": "ext_link:twitter.com/mohamadtweets" }, @@ -4979,15 +4979,15 @@ } }, { - "ID": "1953", + "ID": "2097", "typeID": "__group__", "zOrder": "138", "measuredW": "128", "measuredH": "26", "w": "128", "h": "26", - "x": "769", - "y": "324", + "x": "779", + "y": "334", "properties": { "controlName": "ext_link:twitter.com/BulatShkanov" }, @@ -5013,28 +5013,28 @@ } }, { - "ID": "1954", + "ID": "2098", "typeID": "Label", "zOrder": "139", "measuredW": "31", "measuredH": "26", - "x": "729", - "y": "324", + "x": "739", + "y": "334", "properties": { "size": "18", "text": "and" } }, { - "ID": "1955", + "ID": "2099", "typeID": "__group__", "zOrder": "140", "measuredW": "248", "measuredH": "26", "w": "248", "h": "26", - "x": "724", - "y": "252", + "x": "734", + "y": "262", "properties": { "controlName": "ext_link:bit.ly/careem-ai-ds" }, @@ -5060,15 +5060,15 @@ } }, { - "ID": "1956", + "ID": "2100", "typeID": "__group__", "zOrder": "141", "measuredW": "16", "measuredH": "26", "w": "16", "h": "26", - "x": "977", - "y": "252", + "x": "987", + "y": "262", "properties": { "controlName": "ext_link:bit.ly/careem-ai-ds" }, @@ -5093,15 +5093,15 @@ } }, { - "ID": "1957", + "ID": "2101", "typeID": "__group__", "zOrder": "142", "measuredW": "467", "measuredH": "43", "w": "467", "h": "43", - "x": "894", - "y": "1309", + "x": "904", + "y": "1319", "properties": { "controlName": "ext_link:researchgate.net/publication/305997925_Improving_the_Sensitivity_of_Online_Controlled_Experiments_Case_Studies_at_Netflix" }, @@ -5156,15 +5156,15 @@ } }, { - "ID": "1958", + "ID": "2102", "typeID": "Arrow", "zOrder": "143", "w": "87", "h": "1", "measuredW": "150", "measuredH": "100", - "x": "689", - "y": "3076", + "x": "699", + "y": "3086", "properties": { "curvature": "-1", "direction": "bottom", @@ -5198,8 +5198,8 @@ "resourceID": "BFEFC928-2756-4044-9FAF-3CD2D8DBA3C9", "mockupH": "2890", "mockupW": "1241", - "measuredW": "1413", - "measuredH": "3077", + "measuredW": "1423", + "measuredH": "3087", "version": "1.0" }, "groupOffset": {