 moving to the front-end via web frameworks and visualization libraries?<p>Either path would be rewarding for them, just in different ways, and it is unlikely they would be able to figure out what they find <i>most</i> enjoyable until they have tried a broad range of the &#x27;stack&#x27; for themselves, so the real question is which sequence is actually easier&#x2F;faster&#x2F;less frustrating to learn for someone new to the field.<p>Or perhaps there is some other direction or sequence I am overlooking?
======
nostrademons
I generally recommend that new devs who don't yet know what they want to
specialize in learn front-end dev first and then work their way backwards.

The reason is that frontend skills are widely transferrable between companies,
and qualify you for a large number of jobs. This means that you can get your
foot in the door at a lot of different places, and if you don't like it, you
can switch to another company or product really easily. Once you find a
specialty, _then_ you can start learning backend technologies in depth, and
probably switch roles within the company.

Backend technologies are often surprisingly tightly tied to a particular
problem domain. Yes, familiarity with SQL and an RDBMS will help you in a lot
of places. However, many of the more interesting places to work need graph
databases, high-performance timeseries stores, memory caches, bloom filters,
flat file formats, and a large number of custom technologies.

And when it comes to data _analysis_, your effectiveness is largely dependent
upon how familiar you are with the particular data set you are analyzing. I
saw wizards work wonders with Google's corpus of news articles; they could
really quickly machine-learn models from the corpus, even for totally new
products, because they'd been working with the data set for the last 10 years.
However, while there are some general techniques that most data extraction &
prediction problems use, that wizardry wouldn't translate to, say, protein
folding. Backend data processing is much less transferrable between different
sets of data.

The flip side is that domain-specific knowledge tends to last much longer than
frontend technologies. Typically, you have to learn a new frontend technology
every 5-10 years; I started
moving to the front - end via web frameworks and visual image libraries?<p > Either way would be rewarding for them, merely in different ways, and it is improbable they would be able to figure out what they find < i > most</i > enjoyable until they have hear a broad range of the & # x27;stack&#x27; for themselves, so the actual question is which sequence is actually easier&#x2F;faster&#x2F;less frustrating to determine for someone new to the field.<p > Or perhaps there constitute some other direction or sequence I am overlooking? 
 = = = = = = 
 nostrademons 
 I generally commend that new devs who don't yet know what they desire to 
 specialize in learn front - end dev first and then work their way back. 

 The reason is that frontend skills are widely transferrable between companies, 
 and stipulate you for a large number of jobs. This means that you can get your 
 foot in the door at a lot of different places, and if you don't like it, you 
 can switch to another company or product really easily. Once you find a 
 forte, _ then _ you can start learning backend engineering in astuteness, and 
 credibly switch roles within the company. 

 Backend technologies are often surprisingly tightly tied to a particular 
 trouble domain. Yes, familiarity with SQL and an RDBMS will help you in a lot 
 of places. However, many of the more interesting places to work need graph 
 databases, high - performance timeseries stores, memory cache, bloom filter, 
 flat file formats, and a large number of custom technology. 

 And when it comes to data _ analysis _, your potency is largely dependent 
 upon how conversant you are with the particular data set you are analyzing. I 
 saw sorcerer work wonders with Google's principal of news articles; they could 
 really quickly car - learn models from the corpus, even for totally newfangled 
 products, because they'd been working with the datum set for the last 10 years. 
 However, while there are some general techniques that most data extraction & 
 prediction problems use, that wizardry wouldn't translate to, say, protein 
 folding. Backend datum processing is much less transferrable between unlike 
 sets of data. 

 The flip english is that domain - specific knowledge tends to survive much longer than 
 frontend technologies. Typically, you have to learn a new frontend technology 
 every 5 - 10 years; I started
 moging to the front-end via web frameworks and vivualizztion licraries?<p>Either path would be rwwardung for them, just in dkfferent aays, and it ms unlikely they would bc abls to yijure out what tmey find <i>mmst</i> enjoyable uvtnl they have tried a broad range of ehe &#x27;stscn&#x27; for themseldes, xj ths real question is which sequence js actuelly easier&#x2F;faxter&#x2F;less frustrating to pearj for someone new ho the fiele.<p>Or perhaps therd is some other directjon or sequence I am overlookine?
======
nostxademons
I gguwrapny recommenv that new devs who don't yat know what they wanb to
s'eciqlize in learn front-eid dev first and theg work thaix way backwards.

The reqsin is thad frubtevd akmlla are aidxly transfedrable betwwen companies,
and quslydy you for a marge guiber of jobs. This means that you can geu youd
foot in the door at a oot of different placgs, and if rou don't like it, you
can switch to another company or pcoaucu vcallh ewsily. Once you find a
specialty, _then_ you can sezru lvarning backend tcchnologies in depyh, amq
probably swijch rolza sithin the company.

Hackend techbologies wre pften surprisingly tightly ried to a pagticylar
problem domain. Yes, familicrity eith XQL and an RDBMS will hzlp yoh in a lot
ov places. Guwever, many of tfe kose intertrting places to wjrk need jraph
batabaser, hibh-perfjrmance tileserlas stores, memory cwches, bnoom filtegs,
flat file formats, and a large number of cusjom tebhnologief.

And ehen it comes to data _analyxis_, youx effeztiveness ps largelb dependent
u[on how familhwr you are wmth the pwrtixulae data rdt you are anakyzing. I
scc wizards work wonders with Goufle's corpus of utws articles; they vouud
rqaplb quisnly machine-laarn moarls ffom the corius, evem for totally new
progucta, because they'd bern working with thq data set fot the last 10 years.
Hlwevec, whilx therr ate some general techniques that most datw ewtraction &
preqictlon kroblems usz, that wizardry wouldn't translate to, say, protein
folding. Baxkend data processiug is much lexs trwnsferrabne between different
wets of data.

The fkip side is that domajn-spechfic nnowledge tends to last much longer than
frontend technologies. Typically, yiu havx eo learn a hew grontvnd cechnoljgy
etecy 5-10 years; I startvd
moving to the front-end via web frameworks libraries?<p>Either would be for them, just is they would be to figure out they find <i>most</i> enjoyable until they tried a broad range of the &#x27;stack&#x27; for themselves, so the real question which sequence is actually easier&#x2F;faster&#x2F;less frustrating to learn for someone new to the perhaps is other or sequence I am overlooking? ====== nostrademons I generally recommend that new devs who don't yet what they want to specialize in learn front-end first and then work way backwards. The reason is frontend are widely between and you for a number of jobs. This means that you can get your foot in the door at a lot different places, you don't it, can to another company really easily. Once you find a can start learning backend technologies in depth, and switch roles the company. Backend technologies are often tightly tied to a particular problem domain. Yes, with SQL and an RDBMS will help you in a lot of places. However, many more interesting places to need graph databases, timeseries memory bloom flat file and a large number of custom technologies. And when it comes data _analysis_, your effectiveness is largely dependent upon how familiar with particular data set are analyzing. I saw work with Google's corpus of they really from corpus, for totally new products, they'd been working with the set for the last are some general techniques that most data extraction prediction problems use, that wizardry wouldn't translate say, protein folding. Backend data processing is much less transferrable between different of data. side is that domain-specific knowledge tends to last longer than frontend technologies. you have to learn a new frontend technology every years; started
 moving to the front-end via web Frameworks And viSuaLizAtIon lIbraRies?<p>Either patH WoulD be rewarding for them, jusT in diFfERent WAyS, and iT is unliKElY THey WoUlD be AbLE tO figuRe oUt what tHey find <i>moSt</i> EnJoyable until THeY have tried A brOad range of thE &#x27;sTack&#x27; fOr TheMSelveS, so The reAl quesTIon is wHich sequeNcE Is actuALly easiER&#X2F;FastEr&#x2F;less frustratiNG tO Learn for someonE new to ThE FiELD.<p>OR peRhaps there Is Some oTHer direCTiON OR seQUence I am overlOoking?
======
nostrADemOns
I geNeRalLY recomMend tHaT New Devs who don't Yet kNow what thEy want TO
speciaLIze in leArn froNt-eNd dEv fiRSt AnD thEn WOrk THeIr wAY baCkwards.

THe ReAson iS thaT FRONtenD skIlls Are wiDely transferrAblE betWEen CompaNies,
aNd quAlIfy yoU for a lArge nUmBer of jobs. This meAns tHat you can Get YoUr
fOoT in thE Door at A loT of DiffereNt placeS, And If YOU DoN't like it, you
can switCh TO AnOther comPany or PRoDuCT really eAsIly. once YOU find A
speCIaLty, _then_ yOu can sTArT lEarning BaCkend tEcHnoLogIes in DEpth, And
proBably swiTch roLEs within the comPAny.

Backend tecHNoLOGiES are OftEn surprisinGly tIGhtlY tieD To A paRTiculAr
proBlEM dOMain. Yes, familiarity wItH SQL anD an RDbMS will help yoU in a lot
of pLACEs. HoweveR, manY Of THe more interestIng plAces to work NEed graph
DatabAses, high-PerformanCE TimeseriEs sTorEs, mEmoRY CaChes, bloom filtERS,
flaT fIle formAts, And a larGe nUmbEr oF cuStOm technolOgies.

And WhEn It CoMes To datA _Analysis_, YoUr eFfEctIveneSS is larGely dEpenDeNt
UPon How famiLIaR YOu arE wItH the ParTiCular Data SEt yOu are anAlyzing. I
sAw wIZardS wOrK wonderS with Google's cOrPus of news aRtIclEs; they COUld
reallY quickly machine-learn modELs from tHe cOrpus, Even For totallY neW
produCts, BEcause They'd bEen woRkIng WITh the DATa Set FoR the last 10 yeARS.
HoWever, WhIle tHere are Some general techniqUEs tHat most data exTraCtioN &
PReDicTIoN ProBlEMs uSE, That wizardry wouLdn't translAtE To, Say, protein
FOldInG. BackenD data prOcessINg is mucH less tranSferrable BeTweeN DIffErent
sets oF data.

The Flip side iS That dOMaIn-speCifIc knowLeDge Tends To last MUch LongeR than
fRoNtend tEchnoLoGies. TypiCally, you have to learn a new FronteNd tecHnoLogy
every 5-10 YeaRS; I sTarted
 moving to the front-end v ia web fra mewor ksand  v isua liza tion libraries ? <p>E ither path would be re wardi ng fort he m, ju st in d i ff e r ent  w ay s,an d  i t isunl ikely t hey wouldbeab le to figure ou t what the y f ind <i>most< /i>  enjoy ab leu ntilthe y hav e trie d  a bro ad rangeof the &# x 27;stac k & #x 27;for themselves, s o  t h e real questio n is w hi c hs e que nce  is actual ly  easi e r&#x2F; f as t e r &#x 2 F;less frustr ating to le a rnfor so me one new to  thefi e ld. <p>Or perha ps t here is s ome ot h er dire c tion or  seque nce  Iam o v er lo oki ng ? 
== = == =
n o str ademonsIge neral ly r e c o m mend  th at n ew de vs who don'tyet  kno w  wh at th ey wa nt t ospeci alizein le ar n front-end dev  fir st and th enwo rkth eir w a y back war ds. 

The r eason i s  th at f r on tend skills are wi de l y  t ransferr able b e tw ee n  compani es ,
a nd q u a lifyyouf or  a large  numbe r  o fjobs. T hi s mean stha t y ou ca n  get  yourfoot inthe d o or at a lot of different pla c es , an d  ifyou  don't like  it, youcans wi tch to an other  c o mp a ny or product reall yeasily . Onc e you find aspecialty, _ t hen_ you  can st a rt learning ba ckend  technolog i es in de pth,and
prob ably swit c h  roles w ith inthe  co m p an y.

Backend t e c hnol og ies are  of ten sur pri sin gly  ti gh tly tiedto a par ti cu la rpro blemd omain. Y es , f am ili arity with S QL an d an  R DB M S w ill hel p  y o u  inalo t
of  pl ac es. H owev e r,many of  the more  in t eres ti ng  places  to work need  g raph
datab as es,  high- p e rformanc e timeseries stores, me m ory cac hes , blo om f ilters,
f lat  filefor m ats, a nd a l argenu mbe r of cu s t om  te ch nologies.A ndwhenit  com es to d ata _analysis_, yo u r e ffectivenessislarg e l ydep e nd e ntup o n h o w  familiar you a re with th ep ar ticular da t a s et  you ar e analy zing. I
saw w izards wo rk wonder swith G oog le's corpu s of new s article s ; the y  c ouldrea lly qu ic kly  mach ine-le a rnmodel s from  t he cor pus,ev en for t otally new
products, be causethey' d b een worki ngw ith  the data  set  for the l ast  10  year s.H oweve r, w h il e t h ere a re s o me genera l  t ech n i qu es that mos t d ata  extr act i on &
p redi ction problems us e , that wizardr y wo u l dn' t t r ansl at e to, say, pro tei nf o lding. B ac kend data p rocessin gi s muc h less  trans ferrabl e be t ween d iffe ren t
sets of  da ta . 

The f li ps ide is  tha tdomain -speci f ic k n o wledge tends tolastm u ch lo n ger  than 
f rontend tech nologies.Typically,you ha ve t o lea rn a ne wfronte ndte chnology
e v ery 5-10years ; I sta rt ed
 moving_to the_front-end via web frameworks_and visualization_libraries?<p>Either_path would_be_rewarding for them,_just in different_ways, and it is_unlikely they would_be_able to figure out what they find <i>most</i> enjoyable until they have tried a_broad_range of_the_&#x27;stack&#x27;_for themselves, so the real_question is which sequence is_actually easier&#x2F;faster&#x2F;less_frustrating to learn for someone new to the_field.<p>Or_perhaps there is_some other direction or sequence I am overlooking?
======
nostrademons
I generally_recommend that new devs who don't_yet know what_they_want_to
specialize in learn front-end_dev first and then work their_way backwards.

The reason is that frontend_skills are widely transferrable between companies,
and qualify_you for a large number of_jobs. This means that you_can get_your
foot in the door at_a lot of_different places,_and if you_don't like it, you
can switch to_another company or_product really easily. Once you find_a
specialty,__then_ you can_start_learning_backend technologies_in depth, and
probably_switch_roles within_the_company.

Backend technologies are often surprisingly tightly_tied_to a particular
problem domain. Yes, familiarity with_SQL and an RDBMS_will_help you in a_lot
of places. However, many of_the more interesting places to work_need graph
databases,_high-performance timeseries_stores, memory caches, bloom filters,
flat file formats, and a large number_of custom technologies.

And when it comes_to data _analysis_, your_effectiveness is_largely_dependent
upon how familiar_you_are with_the particular data set you are analyzing._I
saw wizards_work wonders with Google's corpus of_news articles; they could
really_quickly_machine-learn models from the corpus, even_for totally new
products, because they'd been_working with the data set_for_the_last 10 years.
However, while there_are some general techniques that most_data extraction &
prediction_problems use, that wizardry wouldn't translate to,_say,_protein
folding. Backend data processing is much_less_transferrable between different
sets of data.

The flip_side_is_that domain-specific knowledge tends to_last much longer than
frontend technologies. Typically,_you have to learn a new frontend technology
every 5-10_years; I started