From b1344edab7e630bd6e0d017574f340a87219e2ff Mon Sep 17 00:00:00 2001 From: github-actions-bot Date: Fri, 8 Nov 2024 10:44:59 +0000 Subject: [PATCH] Updates --- 404.html | 2 +- actualites.html | 2 +- .../{index-D-pW0gIw.js => index-1k-BeK2_.js} | 110 +++++++----------- collaboration.html | 2 +- contribuer.html | 2 +- datalab.html | 2 +- formation.html | 2 +- index.html | 2 +- 8 files changed, 50 insertions(+), 74 deletions(-) rename assets/{index-D-pW0gIw.js => index-1k-BeK2_.js} (89%) diff --git a/404.html b/404.html index 59e07df..20d4e26 100644 --- a/404.html +++ b/404.html @@ -85,7 +85,7 @@ })(); - +
diff --git a/actualites.html b/actualites.html index 59e07df..20d4e26 100644 --- a/actualites.html +++ b/actualites.html @@ -85,7 +85,7 @@ })(); - +
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s=Math.min(1-t,1-n,1-r),a=(1-t-s)/(1-s)||0,i=(1-n-s)/(1-s)||0,o=(1-r-s)/(1-s)||0,[a*100,i*100,o*100,s*100]};function cV(e,t){return Math.pow(e[0]-t[0],2)+Math.pow(e[1]-t[1],2)+Math.pow(e[2]-t[2],2)}Ae.rgb.keyword=function(e){var t=IB[e];if(t)return t;var n=1/0,r;for(var a in Ns)if(Ns.hasOwnProperty(a)){var i=Ns[a],o=cV(e,i);o.04045?Math.pow((t+.055)/1.055,2.4):t/12.92,n=n>.04045?Math.pow((n+.055)/1.055,2.4):n/12.92,r=r>.04045?Math.pow((r+.055)/1.055,2.4):r/12.92;var a=t*.4124+n*.3576+r*.1805,i=t*.2126+n*.7152+r*.0722,o=t*.0193+n*.1192+r*.9505;return[a*100,i*100,o*100]};Ae.rgb.lab=function(e){var t=Ae.rgb.xyz(e),n=t[0],r=t[1],a=t[2],i,o,s;return n/=95.047,r/=100,a/=108.883,n=n>.008856?Math.pow(n,1/3):7.787*n+16/116,r=r>.008856?Math.pow(r,1/3):7.787*r+16/116,a=a>.008856?Math.pow(a,1/3):7.787*a+16/116,i=116*r-16,o=500*(n-r),s=200*(r-a),[i,o,s]};Ae.hsl.rgb=function(e){var t=e[0]/360,n=e[1]/100,r=e[2]/100,a,i,o,s,l;if(n===0)return l=r*255,[l,l,l];r<.5?i=r*(1+n):i=r+n-r*n,a=2*r-i,s=[0,0,0];for(var u=0;u<3;u++)o=t+1/3*-(u-1),o<0&&o++,o>1&&o--,6*o<1?l=a+(i-a)*6*o:2*o<1?l=i:3*o<2?l=a+(i-a)*(2/3-o)*6:l=a,s[u]=l*255;return s};Ae.hsl.hsv=function(e){var t=e[0],n=e[1]/100,r=e[2]/100,a=n,i=Math.max(r,.01),o,s;return r*=2,n*=r<=1?r:2-r,a*=i<=1?i:2-i,s=(r+n)/2,o=r===0?2*a/(i+a):2*n/(r+n),[t,o*100,s*100]};Ae.hsv.rgb=function(e){var t=e[0]/60,n=e[1]/100,r=e[2]/100,a=Math.floor(t)%6,i=t-Math.floor(t),o=255*r*(1-n),s=255*r*(1-n*i),l=255*r*(1-n*(1-i));switch(r*=255,a){case 0:return[r,l,o];case 1:return[s,r,o];case 2:return[o,r,l];case 3:return[o,s,r];case 4:return[l,o,r];case 5:return[r,o,s]}};Ae.hsv.hsl=function(e){var t=e[0],n=e[1]/100,r=e[2]/100,a=Math.max(r,.01),i,o,s;return s=(2-n)*r,i=(2-n)*a,o=n*a,o/=i<=1?i:2-i,o=o||0,s/=2,[t,o*100,s*100]};Ae.hwb.rgb=function(e){var t=e[0]/360,n=e[1]/100,r=e[2]/100,a=n+r,i,o,s,l;a>1&&(n/=a,r/=a),i=Math.floor(6*t),o=1-r,s=6*t-i,i&1&&(s=1-s),l=n+s*(o-n);var u,c,f;switch(i){default:case 6:case 0:u=o,c=l,f=n;break;case 1:u=l,c=o,f=n;break;case 2:u=n,c=o,f=l;break;case 3:u=n,c=l,f=o;break;case 4:u=l,c=n,f=o;break;case 5:u=o,c=n,f=l;break}return[u*255,c*255,f*255]};Ae.cmyk.rgb=function(e){var t=e[0]/100,n=e[1]/100,r=e[2]/100,a=e[3]/100,i,o,s;return i=1-Math.min(1,t*(1-a)+a),o=1-Math.min(1,n*(1-a)+a),s=1-Math.min(1,r*(1-a)+a),[i*255,o*255,s*255]};Ae.xyz.rgb=function(e){var t=e[0]/100,n=e[1]/100,r=e[2]/100,a,i,o;return a=t*3.2406+n*-1.5372+r*-.4986,i=t*-.9689+n*1.8758+r*.0415,o=t*.0557+n*-.204+r*1.057,a=a>.0031308?1.055*Math.pow(a,1/2.4)-.055:a*12.92,i=i>.0031308?1.055*Math.pow(i,1/2.4)-.055:i*12.92,o=o>.0031308?1.055*Math.pow(o,1/2.4)-.055:o*12.92,a=Math.min(Math.max(0,a),1),i=Math.min(Math.max(0,i),1),o=Math.min(Math.max(0,o),1),[a*255,i*255,o*255]};Ae.xyz.lab=function(e){var t=e[0],n=e[1],r=e[2],a,i,o;return t/=95.047,n/=100,r/=108.883,t=t>.008856?Math.pow(t,1/3):7.787*t+16/116,n=n>.008856?Math.pow(n,1/3):7.787*n+16/116,r=r>.008856?Math.pow(r,1/3):7.787*r+16/116,a=116*n-16,i=500*(t-n),o=200*(n-r),[a,i,o]};Ae.lab.xyz=function(e){var t=e[0],n=e[1],r=e[2],a,i,o;i=(t+16)/116,a=n/500+i,o=i-r/200;var s=Math.pow(i,3),l=Math.pow(a,3),u=Math.pow(o,3);return i=s>.008856?s:(i-16/116)/7.787,a=l>.008856?l:(a-16/116)/7.787,o=u>.008856?u:(o-16/116)/7.787,a*=95.047,i*=100,o*=108.883,[a,i,o]};Ae.lab.lch=function(e){var t=e[0],n=e[1],r=e[2],a,i,o;return a=Math.atan2(r,n),i=a*360/2/Math.PI,i<0&&(i+=360),o=Math.sqrt(n*n+r*r),[t,o,i]};Ae.lch.lab=function(e){var t=e[0],n=e[1],r=e[2],a,i,o;return o=r/360*2*Math.PI,a=n*Math.cos(o),i=n*Math.sin(o),[t,a,i]};Ae.rgb.ansi16=function(e){var t=e[0],n=e[1],r=e[2],a=1 in arguments?arguments[1]:Ae.rgb.hsv(e)[2];if(a=Math.round(a/50),a===0)return 30;var i=30+(Math.round(r/255)<<2|Math.round(n/255)<<1|Math.round(t/255));return a===2&&(i+=60),i};Ae.hsv.ansi16=function(e){return Ae.rgb.ansi16(Ae.hsv.rgb(e),e[2])};Ae.rgb.ansi256=function(e){var t=e[0],n=e[1],r=e[2];if(t===n&&n===r)return t<8?16:t>248?231:Math.round((t-8)/247*24)+232;var a=16+36*Math.round(t/255*5)+6*Math.round(n/255*5)+Math.round(r/255*5);return a};Ae.ansi16.rgb=function(e){var t=e%10;if(t===0||t===7)return e>50&&(t+=3.5),t=t/10.5*255,[t,t,t];var n=(~~(e>50)+1)*.5,r=(t&1)*n*255,a=(t>>1&1)*n*255,i=(t>>2&1)*n*255;return[r,a,i]};Ae.ansi256.rgb=function(e){if(e>=232){var t=(e-232)*10+8;return[t,t,t]}e-=16;var n,r=Math.floor(e/36)/5*255,a=Math.floor((n=e%36)/6)/5*255,i=n%6/5*255;return[r,a,i]};Ae.rgb.hex=function(e){var t=((Math.round(e[0])&255)<<16)+((Math.round(e[1])&255)<<8)+(Math.round(e[2])&255),n=t.toString(16).toUpperCase();return"000000".substring(n.length)+n};Ae.hex.rgb=function(e){var t=e.toString(16).match(/[a-f0-9]{6}|[a-f0-9]{3}/i);if(!t)return[0,0,0];var n=t[0];t[0].length===3&&(n=n.split("").map(function(s){return s+s}).join(""));var r=parseInt(n,16),a=r>>16&255,i=r>>8&255,o=r&255;return[a,i,o]};Ae.rgb.hcg=function(e){var t=e[0]/255,n=e[1]/255,r=e[2]/255,a=Math.max(Math.max(t,n),r),i=Math.min(Math.min(t,n),r),o=a-i,s,l;return o<1?s=i/(1-o):s=0,o<=0?l=0:a===t?l=(n-r)/o%6:a===n?l=2+(r-t)/o:l=4+(t-n)/o+4,l/=6,l%=1,[l*360,o*100,s*100]};Ae.hsl.hcg=function(e){var t=e[1]/100,n=e[2]/100,r=1,a=0;return n<.5?r=2*t*n:r=2*t*(1-n),r<1&&(a=(n-.5*r)/(1-r)),[e[0],r*100,a*100]};Ae.hsv.hcg=function(e){var t=e[1]/100,n=e[2]/100,r=t*n,a=0;return r<1&&(a=(n-r)/(1-r)),[e[0],r*100,a*100]};Ae.hcg.rgb=function(e){var t=e[0]/360,n=e[1]/100,r=e[2]/100;if(n===0)return[r*255,r*255,r*255];var a=[0,0,0],i=t%1*6,o=i%1,s=1-o,l=0;switch(Math.floor(i)){case 0:a[0]=1,a[1]=o,a[2]=0;break;case 1:a[0]=s,a[1]=1,a[2]=0;break;case 2:a[0]=0,a[1]=1,a[2]=o;break;case 3:a[0]=0,a[1]=s,a[2]=1;break;case 4:a[0]=o,a[1]=0,a[2]=1;break;default:a[0]=1,a[1]=0,a[2]=s}return l=(1-n)*r,[(n*a[0]+l)*255,(n*a[1]+l)*255,(n*a[2]+l)*255]};Ae.hcg.hsv=function(e){var t=e[1]/100,n=e[2]/100,r=t+n*(1-t),a=0;return r>0&&(a=t/r),[e[0],a*100,r*100]};Ae.hcg.hsl=function(e){var t=e[1]/100,n=e[2]/100,r=n*(1-t)+.5*t,a=0;return r>0&&r<.5?a=t/(2*r):r>=.5&&r<1&&(a=t/(2*(1-r))),[e[0],a*100,r*100]};Ae.hcg.hwb=function(e){var t=e[1]/100,n=e[2]/100,r=t+n*(1-t);return[e[0],(r-t)*100,(1-r)*100]};Ae.hwb.hcg=function(e){var t=e[1]/100,n=e[2]/100,r=1-n,a=r-t,i=0;return a<1&&(i=(r-a)/(1-a)),[e[0],a*100,i*100]};Ae.apple.rgb=function(e){return[e[0]/65535*255,e[1]/65535*255,e[2]/65535*255]};Ae.rgb.apple=function(e){return[e[0]/255*65535,e[1]/255*65535,e[2]/255*65535]};Ae.gray.rgb=function(e){return[e[0]/100*255,e[0]/100*255,e[0]/100*255]};Ae.gray.hsl=Ae.gray.hsv=function(e){return[0,0,e[0]]};Ae.gray.hwb=function(e){return[0,100,e[0]]};Ae.gray.cmyk=function(e){return[0,0,0,e[0]]};Ae.gray.lab=function(e){return[e[0],0,0]};Ae.gray.hex=function(e){var t=Math.round(e[0]/100*255)&255,n=(t<<16)+(t<<8)+t,r=n.toString(16).toUpperCase();return"000000".substring(r.length)+r};Ae.rgb.gray=function(e){var t=(e[0]+e[1]+e[2])/3;return[t/255*100]};var BB=MB.exports,vv=BB;function dV(){for(var e={},t=Object.keys(vv),n=t.length,r=0;r1&&(n=Array.prototype.slice.call(arguments)),e(n))};return"conversion"in e&&(t.conversion=e.conversion),t}function bV(e){var t=function(n){if(n==null)return n;arguments.length>1&&(n=Array.prototype.slice.call(arguments));var r=e(n);if(typeof r=="object")for(var a=r.length,i=0;i>16&255,e>>8&255,e&255],this.valpha=1;else{this.valpha=1;var o=Object.keys(e);"alpha"in e&&(o.splice(o.indexOf("alpha"),1),this.valpha=typeof e.alpha=="number"?e.alpha:0);var s=o.sort().join("");if(!(s in hS))throw new Error("Unable to parse color from object: "+JSON.stringify(e));this.model=hS[s];var l=Or[this.model].labels,u=[];for(n=0;nn?(t+.05)/(n+.05):(n+.05)/(t+.05)},level:function(e){var t=this.contrast(e);return t>=7.1?"AAA":t>=4.5?"AA":""},isDark:function(){var e=this.rgb().color,t=(e[0]*299+e[1]*587+e[2]*114)/1e3;return t<128},isLight:function(){return!this.isDark()},negate:function(){for(var e=this.rgb(),t=0;t<3;t++)e.color[t]=255-e.color[t];return e},lighten:function(e){var t=this.hsl();return t.color[2]+=t.color[2]*e,t},darken:function(e){var t=this.hsl();return t.color[2]-=t.color[2]*e,t},saturate:function(e){var t=this.hsl();return t.color[1]+=t.color[1]*e,t},desaturate:function(e){var t=this.hsl();return t.color[1]-=t.color[1]*e,t},whiten:function(e){var t=this.hwb();return t.color[1]+=t.color[1]*e,t},blacken:function(e){var t=this.hwb();return t.color[2]+=t.color[2]*e,t},grayscale:function(){var e=this.rgb().color,t=e[0]*.3+e[1]*.59+e[2]*.11;return En.rgb(t,t,t)},fade:function(e){return this.alpha(this.valpha-this.valpha*e)},opaquer:function(e){return this.alpha(this.valpha+this.valpha*e)},rotate:function(e){var t=this.hsl(),n=t.color[0];return n=(n+e)%360,n=n<0?360+n:n,t.color[0]=n,t},mix:function(e,t){if(!e||!e.rgb)throw new Error('Argument to "mix" was not a Color instance, but rather an instance of '+typeof e);var n=e.rgb(),r=this.rgb(),a=t===void 0?.5:t,i=2*a-1,o=n.alpha()-r.alpha(),s=((i*o===-1?i:(i+o)/(1+i*o))+1)/2,l=1-s;return En.rgb(s*n.red()+l*r.red(),s*n.green()+l*r.green(),s*n.blue()+l*r.blue(),n.alpha()*a+r.alpha()*(1-a))}};Object.keys(Or).forEach(function(e){if(LB.indexOf(e)===-1){var t=Or[e].channels;En.prototype[e]=function(){if(this.model===e)return new En(this);if(arguments.length)return new En(arguments,e);var n=typeof arguments[t]=="number"?t:this.valpha;return new En(CV(Or[this.model][e].raw(this.color)).concat(n),e)},En[e]=function(n){return typeof n=="number"&&(n=mS(RC.call(arguments),t)),new En(n,e)}}});function SV(e,t){return Number(e.toFixed(t))}function xV(e){return function(t){return SV(t,e)}}function Vt(e,t,n){return e=Array.isArray(e)?e:[e],e.forEach(function(r){(gv[r]||(gv[r]=[]))[t]=n}),e=e[0],function(r){var a;return arguments.length?(n&&(r=n(r)),a=this[e](),a.color[t]=r,a):(a=this[e]().color[t],n&&(a=n(a)),a)}}function fn(e){return function(t){return Math.max(0,Math.min(e,t))}}function CV(e){return Array.isArray(e)?e:[e]}function mS(e,t){for(var n=0;n({fontFamily:"sans-serif",rootFontSizePx:s,variants:{"display heading":Object.assign({htmlComponent:"h1",fontWeight:(0,n.id)("bold")},o>=r.breakpointsValues.xl?{fontSizeRem:2.8,lineHeightRem:3.25}:o>=r.breakpointsValues.lg?{fontSizeRem:2.25,lineHeightRem:2.5}:{fontSizeRem:1.75,lineHeightRem:2.5}),"page heading":Object.assign({htmlComponent:"h2",fontWeight:(0,n.id)("bold")},o>=r.breakpointsValues.xl?{fontSizeRem:2.25,lineHeightRem:2.5}:o>=r.breakpointsValues.lg?{fontSizeRem:1.75,lineHeightRem:2.25}:{fontSizeRem:1.125,lineHeightRem:1.25}),subtitle:Object.assign({htmlComponent:"h3",fontWeight:(0,n.id)("normal")},o>=r.breakpointsValues.xl?{fontSizeRem:1.75,lineHeightRem:2.25}:o>=r.breakpointsValues.lg?{fontSizeRem:1.5,lineHeightRem:2}:{fontSizeRem:1,lineHeightRem:1.475}),"section heading":Object.assign({htmlComponent:"h4",fontWeight:(0,n.id)("bold")},o>=r.breakpointsValues.xl?{fontSizeRem:1.5,lineHeightRem:2}:o>=r.breakpointsValues.lg?{fontSizeRem:1.25,lineHeightRem:1.7}:{fontSizeRem:.875,lineHeightRem:1.28}),"object heading":Object.assign({htmlComponent:"h5",fontWeight:(0,n.id)(600)},o>=r.breakpointsValues.xl?{fontSizeRem:1.25,lineHeightRem:1.7}:o>=r.breakpointsValues.lg?{fontSizeRem:1.125,lineHeightRem:1.25}:{fontSizeRem:.875,lineHeightRem:1.28}),"navigation label":Object.assign({htmlComponent:"h5",fontWeight:(0,n.id)(500)},o>=r.breakpointsValues.xl?{fontSizeRem:1.25,lineHeightRem:1.7}:o>=r.breakpointsValues.lg?{fontSizeRem:1.125,lineHeightRem:1.25}:{fontSizeRem:.875,lineHeightRem:1.28}),"label 1":Object.assign({htmlComponent:"h6",fontWeight:(0,n.id)(500)},o>=r.breakpointsValues.xl?{fontSizeRem:1,lineHeightRem:1.475}:o>=r.breakpointsValues.lg?{fontSizeRem:.875,lineHeightRem:1.28}:{fontSizeRem:.75,lineHeightRem:1}),"label 2":Object.assign({htmlComponent:"h6",fontWeight:(0,n.id)(500)},o>=r.breakpointsValues.xl?{fontSizeRem:.875,lineHeightRem:1.28}:o>=r.breakpointsValues.lg?{fontSizeRem:.75,lineHeightRem:1}:{fontSizeRem:.625,lineHeightRem:.69}),"body 1":Object.assign({htmlComponent:"p",fontWeight:(0,n.id)("normal")},o>=r.breakpointsValues.xl?{fontSizeRem:1,lineHeightRem:1.475}:o>=r.breakpointsValues.lg?{fontSizeRem:.875,lineHeightRem:1.28}:{fontSizeRem:.75,lineHeightRem:1}),"body 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NhkHR2QTZWgXJunFyes70PjPeBHDI7pfDQ+9O8RCMcWwVMEEBGBUzCbfju7+LhxFsh1IcwmAzaTNScQhJieD30uWv8yu7Lzt8jaEKJprZmBxMqmSxCdBP5PFP0CSNoJJraa1eYAAAAASUVORK5CYII=",a2e="data:image/png;base64,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",i2e="data:image/png;base64,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",o2e="data:image/png;base64,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",s2e="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABoAAAAcCAYAAAB/E6/TAAAABGdBTUEAALGOfPtRkwAAACBjSFJNAACHDwAAjA8AAP1SAACBQAAAfXkAAOmLAAA85QAAGcxzPIV3AAAKOWlDQ1BQaG90b3Nob3AgSUNDIHByb2ZpbGUAAEjHnZZ3VFTXFofPvXd6oc0wAlKG3rvAANJ7k15FYZgZYCgDDjM0sSGiAhFFRJoiSFDEgNFQJFZEsRAUVLAHJAgoMRhFVCxvRtaLrqy89/Ly++Osb+2z97n77L3PWhcAkqcvl5cGSwGQyhPwgzyc6RGRUXTsAIABHmCAKQBMVka6X7B7CBDJy82FniFyAl8EAfB6WLwCcNPQM4BOB/+fpFnpfIHomAARm7M5GSwRF4g4JUuQLrbPipgalyxmGCVmvihBEcuJOWGRDT77LLKjmNmpPLaIxTmns1PZYu4V8bZMIUfEiK+ICzO5nCwR3xKxRoowlSviN+LYVA4zAwAUSWwXcFiJIjYRMYkfEuQi4uUA4EgJX3HcVyzgZAvEl3JJS8/hcxMSBXQdli7d1NqaQffkZKVwBALDACYrmcln013SUtOZvBwAFu/8WTLi2tJFRbY0tba0NDQzMv2qUP91829K3NtFehn4uWcQrf+L7a/80hoAYMyJarPziy2uCoDOLQDI3fti0zgAgKSobx3Xv7oPTTwviQJBuo2xcVZWlhGXwzISF/QP/U+Hv6GvvmckPu6P8tBdOfFMYYqALq4bKy0lTcinZ6QzWRy64Z+H+B8H/nUeBkGceA6fwxNFhImmjMtLELWbx+YKuGk8Opf3n5r4D8P+pMW5FonS+BFQY4yA1HUqQH7tBygKESDR+8Vd/6NvvvgwIH554SqTi3P/7zf9Z8Gl4iWDm/A5ziUohM4S8jMX98TPEqABAUgCKpAHykAd6ABDYAasgC1wBG7AG/iDEBAJVgMWSASpgA+yQB7YBApBMdgJ9oBqUAcaQTNoBcdBJzgFzoNL4Bq4AW6D+2AUTIBnYBa8BgsQBGEhMkSB5CEVSBPSh8wgBmQPuUG+UBAUCcVCCRAPEkJ50GaoGCqDqqF6qBn6HjoJnYeuQIPQXWgMmoZ+h97BCEyCqbASrAUbwwzYCfaBQ+BVcAK8Bs6FC+AdcCXcAB+FO+Dz8DX4NjwKP4PnEIAQERqiihgiDMQF8UeikHiEj6xHipAKpAFpRbqRPuQmMorMIG9RGBQFRUcZomxRnqhQFAu1BrUeVYKqRh1GdaB6UTdRY6hZ1Ec0Ga2I1kfboL3QEegEdBa6EF2BbkK3oy+ib6Mn0K8xGAwNo42xwnhiIjFJmLWYEsw+TBvmHGYQM46Zw2Kx8lh9rB3WH8vECrCF2CrsUexZ7BB2AvsGR8Sp4Mxw7rgoHA+Xj6vAHcGdwQ3hJnELeCm8Jt4G749n43PwpfhGfDf+On4Cv0CQJmgT7AghhCTCJkIloZVwkfCA8JJIJKoRrYmBRC5xI7GSeIx4mThGfEuSIemRXEjRJCFpB+kQ6RzpLuklmUzWIjuSo8gC8g5yM/kC+RH5jQRFwkjCS4ItsUGiRqJDYkjiuSReUlPSSXK1ZK5kheQJyeuSM1J4KS0pFymm1HqpGqmTUiNSc9IUaVNpf+lU6RLpI9JXpKdksDJaMm4ybJkCmYMyF2TGKQhFneJCYVE2UxopFykTVAxVm+pFTaIWU7+jDlBnZWVkl8mGyWbL1sielh2lITQtmhcthVZKO04bpr1borTEaQlnyfYlrUuGlszLLZVzlOPIFcm1yd2WeydPl3eTT5bfJd8p/1ABpaCnEKiQpbBf4aLCzFLqUtulrKVFS48vvacIK+opBimuVTyo2K84p6Ss5KGUrlSldEFpRpmm7KicpFyufEZ5WoWiYq/CVSlXOavylC5Ld6Kn0CvpvfRZVUVVT1Whar3qgOqCmrZaqFq+WpvaQ3WCOkM9Xr1cvUd9VkNFw08jT6NF454mXpOhmai5V7NPc15LWytca6tWp9aUtpy2l3audov2Ax2yjoPOGp0GnVu6GF2GbrLuPt0berCehV6iXo3edX1Y31Kfq79Pf9AAbWBtwDNoMBgxJBk6GWYathiOGdGMfI3yjTqNnhtrGEcZ7zLuM/5oYmGSYtJoct9UxtTbNN+02/R3Mz0zllmN2S1zsrm7+QbzLvMXy/SXcZbtX3bHgmLhZ7HVosfig6WVJd+y1XLaSsMq1qrWaoRBZQQwShiXrdHWztYbrE9Zv7WxtBHYHLf5zdbQNtn2iO3Ucu3lnOWNy8ft1OyYdvV2o/Z0+1j7A/ajDqoOTIcGh8eO6o5sxybHSSddpySno07PnU2c+c7tzvMuNi7rXM65Iq4erkWuA24ybqFu1W6P3NXcE9xb3Gc9LDzWepzzRHv6eO7yHPFS8mJ5NXvNelt5r/Pu9SH5BPtU+zz21fPl+3b7wX7efrv9HqzQXMFb0ekP/L38d/s/DNAOWBPwYyAmMCCwJvBJkGlQXlBfMCU4JvhI8OsQ55DSkPuhOqHC0J4wybDosOaw+XDX8LLw0QjjiHUR1yIVIrmRXVHYqLCopqi5lW4r96yciLaILoweXqW9KnvVldUKq1NWn46RjGHGnIhFx4bHHol9z/RnNjDn4rziauNmWS6svaxnbEd2OXuaY8cp40zG28WXxU8l2CXsTphOdEisSJzhunCruS+SPJPqkuaT/ZMPJX9KCU9pS8Wlxqae5Mnwknm9acpp2WmD6frphemja2zW7Fkzy/fhN2VAGasyugRU0c9Uv1BHuEU4lmmfWZP5Jiss60S2dDYvuz9HL2d7zmSue+63a1FrWWt78lTzNuWNrXNaV78eWh+3vmeD+oaCDRMbPTYe3kTYlLzpp3yT/LL8V5vDN3cXKBVsLBjf4rGlpVCikF84stV2a9021DbutoHt5turtn8sYhddLTYprih+X8IqufqN6TeV33zaEb9joNSydP9OzE7ezuFdDrsOl0mX5ZaN7/bb3VFOLy8qf7UnZs+VimUVdXsJe4V7Ryt9K7uqNKp2Vr2vTqy+XeNc01arWLu9dn4fe9/Qfsf9rXVKdcV17w5wD9yp96jvaNBqqDiIOZh58EljWGPft4xvm5sUmoqbPhziHRo9HHS4t9mqufmI4pHSFrhF2DJ9NProje9cv+tqNWytb6O1FR8Dx4THnn4f+/3wcZ/jPScYJ1p/0Pyhtp3SXtQBdeR0zHYmdo52RXYNnvQ+2dNt293+o9GPh06pnqo5LXu69AzhTMGZT2dzz86dSz83cz7h/HhPTM/9CxEXbvUG9g5c9Ll4+ZL7pQt9Tn1nL9tdPnXF5srJq4yrndcsr3X0W/S3/2TxU/uA5UDHdavrXTesb3QPLh88M+QwdP6m681Lt7xuXbu94vbgcOjwnZHokdE77DtTd1PuvriXeW/h/sYH6AdFD6UeVjxSfNTws+7PbaOWo6fHXMf6Hwc/vj/OGn/2S8Yv7ycKnpCfVEyqTDZPmU2dmnafvvF05dOJZ+nPFmYKf5X+tfa5zvMffnP8rX82YnbiBf/Fp99LXsq/PPRq2aueuYC5R69TXy/MF72Rf3P4LeNt37vwd5MLWe+x7ys/6H7o/ujz8cGn1E+f/gUDmPP8usTo0wAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAcBJREFUSEu9lKtOA0EUhhdMHZIXwCFxVTQIkgqSmr4ASQ2OYKoQKAzBEEwTPME0QTRBkKKKQiJIeAFwuFXQb3b/MJ3ObWnCl0x2dufM+c9lZte+5xT/QGOhbrdbzyomk0k9i9NICJHj7bJ+q7h8bWWJrdfPbHY7nYWRS1JIpXKzaR2cmjE+ai/YhIgKsdF2FCNlm106tzfl/ZkZ4K75aNyjp+m0nlW47yGSQjiiJJwuwTcNYA2bmGjyeFNzlWb/4tE8H072zNN9jx31qJDdWInhLDQXPrFg6RDpj1pmgKIH9w5pTfa+kxft0fnsN0rAoX3CmNsBgLtHRIW2br7MRjtKHOsgSETZY8seH1k9ej/cKIbtsrgblEv194n8qUeI4ACREKxhg20Ir5AbpRzYJQTZgQKib7aNCGZElFfPn/Vb5YhvEpMI3yQCoT96UIjI2MSNlxNqj+PxaNMM5uqHsu5dz5r1yOWtf+stCd9YA4n58AoRETed6Bi2A8rVG3yY4Tomc182EMzILolKJxDTIcgl66cKduOFAtAahDLKFkox3Jn/fl7CQsnDENooEGCkSGZkk8ouFlQjoVXIvkerURQ/Tasi/v9G1RsAAAAASUVORK5CYII=",l2e="data:image/png;base64,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to Pandas, the reference library for processing tabular data in Python."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20manipulation%20pandas%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20manipulation%20pandas%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Introduction à la visualisation de données",en:"Introduction to Data Visualization"},abstract:{fr:"Exploration des bases de la visualisation de données avec Python : les graphiques simples de Pandas et les visualisations esthétiques de Seaborn.",en:"Exploration of the basics of data visualization with Python: simple graphs with Pandas and aesthetic visualizations with Seaborn."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20manipulation%20dataviz%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20manipulation%20dataviz%C2%BB&security.allowlist.enabled=false"}}]},{name:{fr:"Projets",en:"Projects"},abstract:{fr:"Des projets de code pour mettre en application les enseignements de la formation",en:"Coding projects to apply the teachings of the training"},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Projet 1 - Puissance 4",en:"Project 1 - Connect Four"},abstract:{fr:"Un projet de code pour mettre en pratique les éléments fondamentaux de Python : structures de données, boucles, fonctions, conditions.",en:"A coding project to practice the fundamental elements of Python: data structures, loops, functions, conditions."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20puissance4%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20puissance4%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Projet 2 - Prédictions météorologiques",en:"Project 2 - Wheather forecast"},abstract:{fr:"Ce projet vise à introduire au requêtage d'API et à la manipulation de données qui en sont issues, en utilisant des fonctions afin de faciliter la reproductibilité des analyses.",en:"This project aims to introduce API querying and the manipulation of resulting data, using functions to facilitate reproducibility of analyses."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20meteo%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20meteo%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Projet 3 - Estimations de population à partir du recensement",en:"Project 3 - Population estimation from census data"},abstract:{fr:"Ce projet propose de reproduire une analyse standard de statistique publique. Il repose sur l'utilisation de la librairie pandas et des librairies de visualisation usuelles (matplotlib, seaborn).",en:"This project aims to reproduce a standard analysis from official statistics. It relies on the use of the Pandas library and common visualization libraries (matplotlib, seaborn)."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20RP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20RP%C2%BB&security.allowlist.enabled=false"}}]}]},{name:"Python pour la data science",abstract:{fr:"Approfondissement de Python pour la data science : manipulation de données, visualisation, modélisation, traitement du langage naturel",en:"Consolidating skills in Python for data science: data manipulation, visualization, modeling, natural language processing"},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:"Introduction",abstract:{fr:"Cette introduction présente l'objectif du cours, les partis-pris pédagogiques, le fil conducteur de cet enseignement ainsi que les modalités pratiques de celui-ci. Elle propose aussi une introduction aux enjeux scientifiques et opérationnels de la _data science_.",en:"This introduction presents the course objective, pedagogical approach, the main theme of this of the course, as well as the practical practical details. It also provides an introduction to the scientific and operational challenges of data science."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"Introduction du cours pour expliquer les modalités du cours et les outils utilisés.",en:"Introduction to the course to explain how the course is run and the tools used."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:15,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/",en:"https://pythonds.linogaliana.fr/en/content/getting-started/"}},{name:{fr:"Avoir un environnement Python fonctionnel pour la data science",en:"A functional Python environment for data science"},abstract:{fr:"Ce chapitre introduit les bases de l'environnement `Python` pour la _data science_ en mettant l'accent sur la modularité du langage et l'utilisation des _notebooks_ `Jupyter`. Il présente les éléments essentiels pour configurer un environnement Python fonctionnel, explique les avantages des IDE comme `VSCode`, et propose une prise en main des _notebooks_ interactifs. Ce chapitre aborde également la gestion des erreurs et l'importance de la formation continue en `Python`, en fournissant des ressources utiles pour rester à jour dans cet écosystème dynamique.",en:"This chapter introduces the basics of the `Python` environment for _data science_, focusing on the modularity of the language and the use of `Jupyter` _notebooks_. It presents the essentials for setting up a functional Python environment, explains the advantages of IDEs such as `VSCode`, and offers a hands-on introduction to interactive _notebooks_. The chapter also discusses error handling and the importance of ongoing training in `Python`, providing useful resources for staying up to date in this dynamic ecosystem."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",timeRequired:20,articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/01_environment.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/01_environment.html"}},{name:{fr:"Démarche à adopter face à un jeu de données",en:"How to deal with a data set"},abstract:{fr:"Quelques éléments pour adopter une démarche scientifique et éthique face à un jeu de données.",en:"A few guidelines for adopting a scientific and ethical approach to data sets."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",timeRequired:25,imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/02_data_analysis.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/02_data_analysis.html"}},{name:{fr:"Quelques exercices de rappels pour se remettre en selle",en:"A few refresher exercises to get back in the saddle"},abstract:{fr:"Un chapitre consacré à divers exercices pour réviser les principes de base de la syntaxe `Python` et des objets utilisés par le langage.",en:"A chapter devoted to various exercises to review the basics of `Python` syntax and the objects used by the language."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:45,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/03_revisions.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/03_revisions.html"}}]},{name:"Manipulation de données",abstract:{fr:"Manipulation et récupération automatisée de données",en:"Automated data wrangling and retrieval"},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"`Python` s'est imposé comme une alternative très crédible à `R` dans la manipulation de données. L'écosystème `Pandas` a permis de démocratiser l'utilisation des DataFrames dans `Python` et faciliter la manipulation de données structurées grâce à la philosophie `SQL`. `Python` reste également le langage le plus pratique pour récupérer et manipuler des données moins structurées (_webscraping_, API). `Python` tend à devenir, grâce au développement d'API vers d'autres langages (`C`, `Spark`, `Postgres`, `ElasticSearch`...), le langage \"_one to rule them all_\"",en:'Python has established itself as a credible alternative to R for data manipulation. The `Pandas` ecosystem has made it possible to democratize the use of DataFrames in `Python` and make it easier to manipulate of structured data thanks to the `SQL` philosophy. `Python` is also the most practical language for retrieving and manipulating unstructured data (_webscraping_, APIs). Python is tending to become, thanks to the development of APIs for other languages (`C`, `Spark`, `Postgres`, `ElasticSearch`...), the _"one to rule them all_" language.'},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:5,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/manipulation/",en:"https://pythonds.linogaliana.fr/en/content/manipulation/"}},{name:{fr:"Numpy, la brique de base de la data science",en:"Numpy, the foundation of data science"},abstract:{fr:"`Numpy` constitue la brique de base de l'écosystème de la _data science_ en `Python`. Toutes les librairies de manipulation de données, de modélisation et de visualisation reposent, de manière plus ou moins directe, sur `Numpy`. Il est donc indispensable de revoir quelques notions sur ce package avant d'aller plus loin.",en:"`Numpy` is the cornerstone of the _data science_ ecosystem in `Python`. All data manipulation, modeling, and visualization libraries rely, directly or indirectly, on `Numpy`. It is therefore essential to review some concepts of this package before moving forward."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2001_numpy%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2001_numpy%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2001_numpy%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?name=jupyter-python&shared=false&version=1.13.44&autoLaunch=true&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2001_numpy%C2%BB"}}},{name:{fr:"Introduction à Pandas",en:"Introduction to Pandas"},abstract:{fr:"`Pandas` est l'élément central de l'écosystème `Python` pour la _data science_. Ce chapitre présente les premières manipulations de données qu'on peut faire grâce à `Pandas` pour explorer la structure de son jeu de données.",en:"`Pandas` is the central piece of the `Python` ecosystem for data science. This chapter presents the first data exploration that can be performed with `Pandas` in order to explore the structure of a dataset."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:120,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Statistiques par groupe et association de plusieurs jeux de données avec Pandas",en:"Analyzing multiple datasets and producing summary statistics with Pandas"},abstract:{fr:"Le chapitre d'introduction à `Pandas` a permis de présenter le principe de données organisées sous une forme de _DataFrame_ et la praticité de l'écosystème `Pandas` pour effectuer des opérations simples sur un jeu de données. Ce chapitre consolide ces principes en présentant deux types de traitements classiques de la boite à outil des _data scientists_ : statistiques par groupe et associations de données.",en:"The introductory chapter to `Pandas` presented how data were organized as _DataFrames_ and how the `Pandas` ecosystem can be useful to perform simple operations on datasets. This chapter consolidates these principles by introducing two classic types of operations from the data scientist's toolbox: group statistics and data merging."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Introduction aux données spatiales avec Geopandas",en:"Introduction to spatial data with Geopandas"},abstract:{fr:"Les données géolocalisées se sont multipliées depuis quelques années, qu’il s’agisse de données open-data ou de traces numériques géolocalisées de type _big-data_. Pour les données spatiales, le package `GeoPandas` étend les fonctionalités de l’écosystème `Pandas` afin de permettre de manipuler des données géographiques complexes de manière simple.",en:"Geocoded data have been more and more used these recent years in research, public policies or business decisions. Data scientists use them a lot, whether they come from open data or geocoded digital traces. For spatial data, the `GeoPandas` package extends the functionalities of the `Pandas` ecosystem to enable handling complex geographical data in a simple manner. This chapter presents the challenge of handling spatial data with `Python`."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:180,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Web scraping avec Python",en:"Web scraping with Python"},abstract:{fr:'`Python` permet de facilement récupérer une page web pour en extraire des données à restructurer. Le _web scraping_, que les Canadiens nomment _"moissonnage du web"_, est une manière de plus en plus utilisée de récupérer une grande masse d\'information en temps réel. Ce chapitre présente les deux principaux paradigmes par le biais de `BeautifulSoup` et `Selenium` et les principaux défis du _web scraping_.',en:"`Python` allows for easy retrieval of a web page to extract and restructure data. Web scraping is an increasingly popular method for gathering large amounts of information in real-time. This chapter introduces the two main paradigms through `BeautifulSoup` and `Selenium` and discusses the key challenges of web scraping."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Récupérer des données avec des API depuis Python",en:"Retrieve data with APIs from Python"},abstract:{fr:"Les __API__ (_Application Programming Interface_) sont un mode d'accès aux données en expansion. Grâce aux API, l'automatisation de scripts est facilitée puisqu'il n'est plus nécessaire de stocker un fichier, et gérer ses différentes versions, mais uniquement de requêter une base et laisser au producteur de données le soin de gérer les mises à jour de la base.",en:"__APIs__ (_Application Programming Interface_) are an expanding way of accessing data. Thanks to APIs, script automation is facilitated since it is no longer necessary to store a file and manage its different versions, but only to query a database and let the data producer handle the updates."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Maîtriser les expressions régulières",en:"Mastering regular expressions"},abstract:{fr:"Les expressions régulières fournissent un cadre très pratique pour manipuler de manière flexible des données textuelles. Elles sont très utiles notamment pour les tâches de traitement naturel du langage (__NLP__) ou le nettoyage de données textuelles.",en:"Regular expressions provide a very practical framework for flexibly manipulating textual data. 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La visualisation de données est importante à la fois dans une démarche exploratoire pour comprendre la structure des phénomènes étudiés mais aussi dans une phase de communication de résultats à des publics n'ayant pas forcément accès à la donnée brute et devant se contenter de synthèses. Cette partie du cours est une introduction à ce vaste sujet par le biais de la pratique à travers la construction de graphiques descriptifs et de cartes.",en:"Data scientists need to be able to synthesize the information contained in a dataset through graphical representation, because the human brain understands information better through figures than through tables. Data visualization is important both as part of an exploratory approach to understanding the structure of the phenomena under study, but also as part of a phase of communicating results to audiences who don't necessarily have access to raw data and need to make do with summaries. This part of the course is an introduction to this vast subject through the practical construction of descriptive graphs and maps."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:5,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/visualisation/",en:"https://pythonds.linogaliana.fr/en/content/visualisation/"}},{name:"De beaux graphiques avec python: mise en pratique",abstract:`Une partie essentielle du travail du -_data-scientist_ est d'être en mesure -de synthétiser une information dans des -représentations graphiques percutantes. Ce -chapitre permet de découvrir -les fonctionalités graphiques de \`matplotlib\`, -\`seaborn\` et \`plotly\` pour représenter des statistiques -sur les décomptes de vélo à Paris. -`,authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-datascience&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20matplotlib%C2%BB&security.allowlist.enabled=false"},{name:"De belles cartes avec python: mise en pratique",abstract:`La cartographie est un excellent moyen de diffuser -une connaissance, y compris à des publics peu -familiers de la statistique. Ce chapitre permet -de découvrir la manière dont on peut -utiliser \`Python\` pour construire des -cartes standards (avec \`geopandas\`) ou -réactives (\`folium\`). Cela se fera -à travers un exercice permettant -de visualiser la fréquentation par les -vélos des routes parisiennes. -`,authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-datascience&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20maps%C2%BB&security.allowlist.enabled=false"}]},{name:"Modélisation",abstract:"Preprocessing, apprentissage supervisé et non supervisé, évaluation de modèles",authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:"Préparation des données pour construire un modèle",abstract:`Afin d'avoir des données cohérentes avec les hypothèses de modélisation, -il est absolument fondamental de prendre le temps de -préparer les données à fournir à un modèle. La qualité de la prédiction -dépend fortement de ce travail préalable qu'on appelle _preprocessing_. -Beaucoup de méthodes sont disponibles dans \`scikit\`, ce qui rend ce travail -moins fastidieux et plus fiable. -`,authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-datascience&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%200_preprocessing%C2%BB&security.allowlist.enabled=false"},{name:"Evaluer la qualité d'un modèle",abstract:`Faire preuve de méthode pour évaluer la qualité d'un modèle 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NhkHR2QTZWgXJunFyes70PjPeBHDI7pfDQ+9O8RCMcWwVMEEBGBUzCbfju7+LhxFsh1IcwmAzaTNScQhJieD30uWv8yu7Lzt8jaEKJprZmBxMqmSxCdBP5PFP0CSNoJJraa1eYAAAAASUVORK5CYII=",a2e="data:image/png;base64,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",i2e="data:image/png;base64,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",o2e="data:image/png;base64,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",s2e="data:image/png;base64,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",l2e="data:image/png;base64,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to Pandas, the reference library for processing tabular data in Python."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20manipulation%20pandas%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20manipulation%20pandas%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Introduction à la visualisation de données",en:"Introduction to Data Visualization"},abstract:{fr:"Exploration des bases de la visualisation de données avec Python : les graphiques simples de Pandas et les visualisations esthétiques de Seaborn.",en:"Exploration of the basics of data visualization with Python: simple graphs with Pandas and aesthetic visualizations with Seaborn."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20manipulation%20dataviz%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20manipulation%20dataviz%C2%BB&security.allowlist.enabled=false"}}]},{name:{fr:"Projets",en:"Projects"},abstract:{fr:"Des projets de code pour mettre en application les enseignements de la formation",en:"Coding projects to apply the teachings of the training"},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Projet 1 - Puissance 4",en:"Project 1 - Connect Four"},abstract:{fr:"Un projet de code pour mettre en pratique les éléments fondamentaux de Python : structures de données, boucles, fonctions, conditions.",en:"A coding project to practice the fundamental elements of Python: data structures, loops, functions, conditions."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20puissance4%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20puissance4%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Projet 2 - Prédictions météorologiques",en:"Project 2 - Wheather forecast"},abstract:{fr:"Ce projet vise à introduire au requêtage d'API et à la manipulation de données qui en sont issues, en utilisant des fonctions afin de faciliter la reproductibilité des analyses.",en:"This project aims to introduce API querying and the manipulation of resulting data, using functions to facilitate reproducibility of analyses."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20meteo%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20meteo%C2%BB&security.allowlist.enabled=false"}},{name:{fr:"Projet 3 - Estimations de population à partir du recensement",en:"Project 3 - Population estimation from census data"},abstract:{fr:"Ce projet propose de reproduire une analyse standard de statistique publique. Il repose sur l'utilisation de la librairie pandas et des librairies de visualisation usuelles (matplotlib, seaborn).",en:"This project aims to reproduce a standard analysis from official statistics. It relies on the use of the Pandas library and common visualization libraries (matplotlib, seaborn)."},authors:["inseefrlab"],contributors:["Romain Avouac"],types:["Notebook Python"],tags:["discover","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABfr%20projects%20RP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-initiation&init.personalInit=%C2%ABhttps://raw.githubusercontent.com/InseeFrLab/formation-python-initiation/main/utils/init_onyxia.sh%C2%BB&init.personalInitArgs=%C2%ABen%20projects%20RP%C2%BB&security.allowlist.enabled=false"}}]}]},{name:"Python pour la data science",abstract:{fr:"Approfondissement de Python pour la data science : manipulation de données, visualisation, modélisation, traitement du langage naturel",en:"Consolidating skills in Python for data science: data manipulation, visualization, modeling, natural language processing"},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:"Introduction",abstract:{fr:"Cette introduction présente l'objectif du cours, les partis-pris pédagogiques, le fil conducteur de cet enseignement ainsi que les modalités pratiques de celui-ci. Elle propose aussi une introduction aux enjeux scientifiques et opérationnels de la _data science_.",en:"This introduction presents the course objective, pedagogical approach, the main theme of this of the course, as well as the practical practical details. It also provides an introduction to the scientific and operational challenges of data science."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"Introduction du cours pour expliquer les modalités du cours et les outils utilisés.",en:"Introduction to the course to explain how the course is run and the tools used."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:15,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/",en:"https://pythonds.linogaliana.fr/en/content/getting-started/"}},{name:{fr:"Avoir un environnement Python fonctionnel pour la data science",en:"A functional Python environment for data science"},abstract:{fr:"Ce chapitre introduit les bases de l'environnement `Python` pour la _data science_ en mettant l'accent sur la modularité du langage et l'utilisation des _notebooks_ `Jupyter`. Il présente les éléments essentiels pour configurer un environnement Python fonctionnel, explique les avantages des IDE comme `VSCode`, et propose une prise en main des _notebooks_ interactifs. Ce chapitre aborde également la gestion des erreurs et l'importance de la formation continue en `Python`, en fournissant des ressources utiles pour rester à jour dans cet écosystème dynamique.",en:"This chapter introduces the basics of the `Python` environment for _data science_, focusing on the modularity of the language and the use of `Jupyter` _notebooks_. It presents the essentials for setting up a functional Python environment, explains the advantages of IDEs such as `VSCode`, and offers a hands-on introduction to interactive _notebooks_. The chapter also discusses error handling and the importance of ongoing training in `Python`, providing useful resources for staying up to date in this dynamic ecosystem."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",timeRequired:20,articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/01_environment.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/01_environment.html"}},{name:{fr:"Démarche à adopter face à un jeu de données",en:"How to deal with a data set"},abstract:{fr:"Quelques éléments pour adopter une démarche scientifique et éthique face à un jeu de données.",en:"A few guidelines for adopting a scientific and ethical approach to data sets."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",timeRequired:25,imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/02_data_analysis.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/02_data_analysis.html"}},{name:{fr:"Quelques exercices de rappels pour se remettre en selle",en:"A few refresher exercises to get back in the saddle"},abstract:{fr:"Un chapitre consacré à divers exercices pour réviser les principes de base de la syntaxe `Python` et des objets utilisés par le langage.",en:"A chapter devoted to various exercises to review the basics of `Python` syntax and the objects used by the language."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:45,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/getting-started/03_revisions.html",en:"https://pythonds.linogaliana.fr/en/content/getting-started/03_revisions.html"}}]},{name:"Manipulation de données",abstract:{fr:"Manipulation et récupération automatisée de données",en:"Automated data wrangling and retrieval"},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"`Python` s'est imposé comme une alternative très crédible à `R` dans la manipulation de données. L'écosystème `Pandas` a permis de démocratiser l'utilisation des DataFrames dans `Python` et faciliter la manipulation de données structurées grâce à la philosophie `SQL`. `Python` reste également le langage le plus pratique pour récupérer et manipuler des données moins structurées (_webscraping_, API). `Python` tend à devenir, grâce au développement d'API vers d'autres langages (`C`, `Spark`, `Postgres`, `ElasticSearch`...), le langage \"_one to rule them all_\"",en:'Python has established itself as a credible alternative to R for data manipulation. The `Pandas` ecosystem has made it possible to democratize the use of DataFrames in `Python` and make it easier to manipulate of structured data thanks to the `SQL` philosophy. `Python` is also the most practical language for retrieving and manipulating unstructured data (_webscraping_, APIs). Python is tending to become, thanks to the development of APIs for other languages (`C`, `Spark`, `Postgres`, `ElasticSearch`...), the _"one to rule them all_" language.'},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:5,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/manipulation/",en:"https://pythonds.linogaliana.fr/en/content/manipulation/"}},{name:{fr:"Numpy, la brique de base de la data science",en:"Numpy, the foundation of data science"},abstract:{fr:"`Numpy` constitue la brique de base de l'écosystème de la _data science_ en `Python`. Toutes les librairies de manipulation de données, de modélisation et de visualisation reposent, de manière plus ou moins directe, sur `Numpy`. Il est donc indispensable de revoir quelques notions sur ce package avant d'aller plus loin.",en:"`Numpy` is the cornerstone of the _data science_ ecosystem in `Python`. All data manipulation, modeling, and visualization libraries rely, directly or indirectly, on `Numpy`. It is therefore essential to review some concepts of this package before moving forward."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2001_numpy%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2001_numpy%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB01_numpy%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2001_numpy%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?name=jupyter-python&shared=false&version=1.13.44&autoLaunch=true&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2001_numpy%C2%BB"}}},{name:{fr:"Introduction à Pandas",en:"Introduction to Pandas"},abstract:{fr:"`Pandas` est l'élément central de l'écosystème `Python` pour la _data science_. Ce chapitre présente les premières manipulations de données qu'on peut faire grâce à `Pandas` pour explorer la structure de son jeu de données.",en:"`Pandas` is the central piece of the `Python` ecosystem for data science. This chapter presents the first data exploration that can be performed with `Pandas` in order to explore the structure of a dataset."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:120,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2002_pandas_intro%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Statistiques par groupe et association de plusieurs jeux de données avec Pandas",en:"Analyzing multiple datasets and producing summary statistics with Pandas"},abstract:{fr:"Le chapitre d'introduction à `Pandas` a permis de présenter le principe de données organisées sous une forme de _DataFrame_ et la praticité de l'écosystème `Pandas` pour effectuer des opérations simples sur un jeu de données. Ce chapitre consolide ces principes en présentant deux types de traitements classiques de la boite à outil des _data scientists_ : statistiques par groupe et associations de données.",en:"The introductory chapter to `Pandas` presented how data were organized as _DataFrames_ and how the `Pandas` ecosystem can be useful to perform simple operations on datasets. This chapter consolidates these principles by introducing two classic types of operations from the data scientist's toolbox: group statistics and data merging."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB02_pandas_suite%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2002_pandas_suite%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Introduction aux données spatiales avec Geopandas",en:"Introduction to spatial data with Geopandas"},abstract:{fr:"Les données géolocalisées se sont multipliées depuis quelques années, qu’il s’agisse de données open-data ou de traces numériques géolocalisées de type _big-data_. Pour les données spatiales, le package `GeoPandas` étend les fonctionalités de l’écosystème `Pandas` afin de permettre de manipuler des données géographiques complexes de manière simple.",en:"Geocoded data have been more and more used these recent years in research, public policies or business decisions. Data scientists use them a lot, whether they come from open data or geocoded digital traces. For spatial data, the `GeoPandas` package extends the functionalities of the `Pandas` ecosystem to enable handling complex geographical data in a simple manner. This chapter presents the challenge of handling spatial data with `Python`."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:180,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB03_geopandas_intro%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen%2Fmanipulation%2003_geopandas_intro%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Web scraping avec Python",en:"Web scraping with Python"},abstract:{fr:'`Python` permet de facilement récupérer une page web pour en extraire des données à restructurer. Le _web scraping_, que les Canadiens nomment _"moissonnage du web"_, est une manière de plus en plus utilisée de récupérer une grande masse d\'information en temps réel. Ce chapitre présente les deux principaux paradigmes par le biais de `BeautifulSoup` et `Selenium` et les principaux défis du _web scraping_.',en:"`Python` allows for easy retrieval of a web page to extract and restructure data. Web scraping is an increasingly popular method for gathering large amounts of information in real-time. This chapter introduces the two main paradigms through `BeautifulSoup` and `Selenium` and discusses the key challenges of web scraping."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04a_webscraping_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004a_webscraping_TP%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Récupérer des données avec des API depuis Python",en:"Retrieve data with APIs from Python"},abstract:{fr:"Les __API__ (_Application Programming Interface_) sont un mode d'accès aux données en expansion. Grâce aux API, l'automatisation de scripts est facilitée puisqu'il n'est plus nécessaire de stocker un fichier, et gérer ses différentes versions, mais uniquement de requêter une base et laisser au producteur de données le soin de gérer les mises à jour de la base.",en:"__APIs__ (_Application Programming Interface_) are an expanding way of accessing data. Thanks to APIs, script automation is facilitated since it is no longer necessary to store a file and manage its different versions, but only to query a database and let the data producer handle the updates."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04c_API_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004c_API_TP%C2%BB&security.allowlist.enabled=false"}}},{name:{fr:"Maîtriser les expressions régulières",en:"Mastering regular expressions"},abstract:{fr:"Les expressions régulières fournissent un cadre très pratique pour manipuler de manière flexible des données textuelles. Elles sont très utiles notamment pour les tâches de traitement naturel du langage (__NLP__) ou le nettoyage de données textuelles.",en:"Regular expressions provide a very practical framework for flexibly manipulating textual data. They are especially useful for natural language processing (__NLP__) tasks or cleaning textual data."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:60,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04b_regex_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004b_regex_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04b_regex_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/manipulation%2004b_regex_TP%C2%BB&security.allowlist.enabled=false"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04b_regex_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004b_regex_TP%C2%BB&security.allowlist.enabled=false",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&onyxia.friendlyName=%C2%AB04b_regex_TP%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmanipulation%2004b_regex_TP%C2%BB&security.allowlist.enabled=false"}}}]},{name:"Visualisation de données",abstract:"Graphiques, cartes, et visualisations interactives",authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"Les _data scientists_ doivent être en mesure de synthétiser l'information présente dans un jeu de données par le biais de la représentation graphique, car le cerveau humain comprend mieux les informations à travers des figures que des tableaux. La visualisation de données est importante à la fois dans une démarche exploratoire pour comprendre la structure des phénomènes étudiés mais aussi dans une phase de communication de résultats à des publics n'ayant pas forcément accès à la donnée brute et devant se contenter de synthèses. Cette partie du cours est une introduction à ce vaste sujet par le biais de la pratique à travers la construction de graphiques descriptifs et de cartes.",en:"Data scientists need to be able to synthesize the information contained in a dataset through graphical representation, because the human brain understands information better through figures than through tables. Data visualization is important both as part of an exploratory approach to understanding the structure of the phenomena under study, but also as part of a phase of communicating results to audiences who don't necessarily have access to raw data and need to make do with summaries. This part of the course is an introduction to this vast subject through the practical construction of descriptive graphs and maps."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:5,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/visualisation/",en:"https://pythonds.linogaliana.fr/en/content/visualisation/"}},{name:"Construire des graphiques avec Python",abstract:"Une partie essentielle du travail du _data scientist_ est d'être en mesure de synthétiser une information dans des représentations graphiques percutantes. Ce chapitre permet de découvrir les enjeux de la représentation de données avec `Python`, l'écosystème pour faire ceci. Il ouvre également à la représentation interactive de données avec `Plotly`.",authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&name=%C2%ABmatplotlib%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20matplotlib%20correction%C2%BB",jupyter:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=%C2%ABmatplotlib%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20matplotlib%20correction%C2%BB"}},{name:"De belles cartes avec python: mise en pratique",abstract:"La cartographie est un excellent moyen de diffuser de la connaissance sur les données, y compris à des publics peu familiers de la statistique. Ce chapitre permet de découvrir le défi de la cartographie et la manière dont on peut utiliser `Python` pour construire des cartes.",authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&name=%C2%ABmaps%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20maps%20correction%C2%BB",jupyter:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=%C2%ABmaps%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABvisualisation%20maps%20correction%C2%BB"}}]},{name:{fr:"Modélisation",en:"Modeling"},abstract:{fr:"La facilité à modéliser des processus très diverses a grandement participé au succès de `Python` dans les années 2010. La popularité de `Python` est indissociable de l'essor du _machine learning_ comme technique de modélisation. Cette partie vise à introduire aux problématiques spécifiques en présentant principalement la librairie `Scikit Learn` qui permet d'avoir un _pipeline_ de _machine learning_ opérationnel très rapidement.",en:"The ability to model a wide range of processes has been a major factor in the success of `Python` in the 2010s. The popularity of `Python` is inseparable from the rise of _machine learning_ as a modeling technique. This section aims to provide an introduction to the specific issues involved by presenting the `Scikit Learn` library, which provides a _pipeline_ of _machine learning_. machine learning _pipeline_ up and running very quickly."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:{fr:"Introduction",en:"Introduction"},abstract:{fr:"La facilité à modéliser des processus très diverses a grandement participé au succès de `Python` dans les années 2010. La popularité de `Python` est indissociable de l'essor du _machine learning_ comme technique de modélisation. Cette partie vise à introduire aux problématiques spécifiques en présentant principalement la librairie `Scikit Learn` qui permet d'avoir un _pipeline_ de _machine learning_ opérationnel très rapidement.",en:"The ability to model a wide range of processes has been a major factor in the success of `Python` in the 2010s. The popularity of `Python` is inseparable from the rise of _machine learning_ as a modeling technique. This section aims to provide an introduction to the specific issues involved by presenting the `Scikit Learn` library, which provides a _pipeline_ of _machine learning_. machine learning _pipeline_ up and running very quickly."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:15,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",articleUrl:{fr:"https://pythonds.linogaliana.fr/content/modelisation/",en:"https://pythonds.linogaliana.fr/en/content/modelisation/"}},{name:{fr:"Préparation des données pour construire un modèle",en:"Preprocessing before building machine learning models"},abstract:{fr:"Afin d'avoir des données cohérentes avec les hypothèses de modélisation, il est fondamental de prendre le temps de préparer les données à fournir à un modèle. La qualité de la prédiction dépend fortement de ce travail préalable qu'on appelle _preprocessing_. Ce chapitre présente les enjeux et les illustre par le biais de la librairie `Scikit Learn`, qui rend ce travail moins fastidieux et plus fiable.",en:"In order to obtain data that is consistent with modeling assumptions, it is essential to take the time to prepare the data to be supplied to a model. The quality of the prediction depends heavily on this preliminary work, known as _preprocessing_. This chapter presents the issues involved and illustrates them using the `Scikit Learn` library, which makes this work less tedious and more reliable."},authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:{vscode:{fr:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&name=%C2%AB0_preprocessing%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%200_preprocessing%20correction%C2%BB",en:"https://datalab.sspcloud.fr/launcher/ide/vscode-python?autoLaunch=true&name=%C2%AB0_preprocessing%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%ABen/modelisation%200_preprocessing%20correction%C2%BB"},jupyter:{fr:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=%C2%AB0_preprocessing%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABmodelisation%200_preprocessing%20correction%C2%BB",en:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=%C2%AB0_preprocessing%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmain%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABen/modelisation%200_preprocessing%20correction%C2%BB"}}},{name:"Evaluer la qualité d'un modèle",abstract:`Faire preuve de méthode pour évaluer la qualité d'un modèle permet de proposer des prédictions plus robustes, ayant de meilleures performances sur un nouveau jeu de données (prédictions _out-of-sample_). Décomposer @@ -318,22 +294,22 @@ Parmi ces méthodes, on va notamment explorer le modèle \`Word2Vec\`, qui permet d'exploiter les structures latentes d'un texte en construisant des _word embeddings_ (plongements de mots). `,authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-datascience&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2004_word2vec%C2%BB&security.allowlist.enabled=false"},{name:"Exercices supplémentaires",abstract:`Des exercices supplémentaires pour pratiquer les concepts du NLP -`,authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],timeRequired:90,category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",deploymentUrl:"https://datalab.sspcloud.fr/launcher/ide/jupyter-python?autoLaunch=true&name=python-datascience&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%ABNLP%2005_exo_supp%C2%BB&security.allowlist.enabled=false"}]},{name:"Utiliser Git dans ses projets Python",abstract:"Introduction pratique à Git pour les projets Python",authors:["Lino Galiana"],types:["Notebook Python"],tags:["consolidate","learn"],category:"training courses with python",imageUrl:"https://raw.githubusercontent.com/InseeFrLab/www.sspcloud.fr/main/src/assets/img/python.jpg",parts:[{name:"Découvrir Git par la pratique: la gymnastique quotidienne",abstract:"`Git` est un système de contrôle de version qui facilite la sauvegarde, la gestion des évolutions et le partage d'un projet informatique. 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diff --git a/contribuer.html b/contribuer.html index 59e07df..20d4e26 100644 --- a/contribuer.html +++ b/contribuer.html @@ -85,7 +85,7 @@ })(); - +
diff --git a/datalab.html b/datalab.html index 59e07df..20d4e26 100644 --- a/datalab.html +++ b/datalab.html @@ -85,7 +85,7 @@ })(); - +
diff --git a/formation.html b/formation.html index 59e07df..20d4e26 100644 --- a/formation.html +++ b/formation.html @@ -85,7 +85,7 @@ })(); - +
diff --git a/index.html b/index.html index 59e07df..20d4e26 100644 --- a/index.html +++ b/index.html @@ -85,7 +85,7 @@ })(); - +