 or help.

Cheers! By Thomas Joseph, Aspire Systems.

Over the past few months, many people have been asking me to write on what it entails to do a data science project end to end i.e from the business problem defining phase to modelling and its final deployment. When I pondered on that request, I thought it made sense. The data science literature is replete with articles on specific algorithms or definitive methods with code on how to deal with a problem. However an end to end view of what it takes to do a data science project for a specific business use case is little hard to find. From this week onward, we would be starting a new series called the Applied Data Science Series. In this series I would be giving an end to end perspective on tackling business use cases or societal problems within the framework of Data Science. In this first article of the applied data science series we will deal with a predictive maintenance business use case. The use case involved is to predict the end life of large industrial batteries, which falls under the genre of use cases called preventive maintenance use cases.

The big picture

Before we delve deep into the business problem and how to solve it from a data science perspective, let us look at the big picture on the life cycle of a data science project.

The above figure is a depiction of the big picture on what it entails to solve a business problem from a Data Science perspective. Let us deal with each of the components end to end.

In the Beginning …… : Business Discovery

The start of any data science project is with a business problem. The problem we have at hand is to try to predict the end life of large industrial batteries. When we are encountered with such a business problem, the first thing which should come to our mind is on the key variables which will come into play. For this specific example of batteries some of the key variables which determine the state of health of batteries are conductance, discharge, voltage, current and temperature.

The next questions which we need to ask is on the lead indicators or trends within these variables, which will help in solving the business problem. This is where we also have to take inputs from the domain team. For the case of batteries, it turns out that a key trend which can indicate propensity for failure is drop in conductance values. The conductance of batteries will drop over time, however the rate at which the conductance values drop will be accelerated before points of failure. This is a vital clue which
or help. 

 Cheers! By Thomas Joseph, Aspire Systems. 

 Over the past few months, many people have been ask me to spell on what it entails to do a data skill project end to end i.e from the business problem specify phase to modelling and its final deployment. When I chew over on that request, I think it made common sense. The data science literature is replete with articles on specific algorithm or definitive methods with code on how to cope with a problem. However an end to end position of what it takes to do a data skill project for a specific business function case is little difficult to find. From this week onward, we would be starting a new serial called the Applied Data Science Series. In this series I would be giving an end to end perspective on tackling clientele use cases or societal problems within the framework of Data Science. In this first article of the applied data science series we will cover with a predictive maintenance business manipulation subject. The use case involved is to bode the end biography of large industrial batteries, which fall under the genre of use cases called preventive maintenance use font. 

 The big picture 

 Before we delve deep into the clientele problem and how to resolve it from a data science perspective, let us look at the boastful picture on the life cycle of a datum science undertaking. 

 The above figure is a depiction of the big picture on what it entails to clear a business problem from a Data Science position. Let us deal with each of the components end to end. 

 In the Beginning … …: Business Discovery 

 The beginning of any data skill project is with a business problem. The trouble we have at hand is to try to predict the end life of large industrial batteries. When we are encountered with such a business problem, the first matter which should come to our mind is on the cardinal variables which will come into play. For this specific exercise of batteries some of the key variables which settle the state of health of batteries are conductance, discharge, voltage, current and temperature. 

 The next questions which we need to necessitate is on the lead indicators or trends within these variables, which will help in solving the business problem. This is where we also suffer to take inputs from the domain team. For the character of batteries, it turns out that a key trend which can indicate leaning for failure is drop in conductance values. The conductance of batteries will drop over time, however the pace at which the conductance values drop will be accelerated before points of failure. This is a vital clue which
 or help.

Cheers! By Thomas Joreph, Aspire Sysjens.

Over the pzst few oonths, many people have been awking me to write on what ig entails to do a date science projecv end to end i.s frok the business kroblem defiting phase to koaepling and its final deployment. When I pondrrfd on that reqoest, P ehoufht it made sense. The data science literauure is replete wiyh articles on specific alhorihhms or definitive methods wijg cjee on how to deal with a problem. Hkwever an end to end view of whxt it takes to eo a fdta science projvct for a spegpfic buviness ise case is libtle iard to find. From this wexk onward, we would bg starting a new series callee rhe Akplieg Dagq Sziehcx Ssries. Ln vhis series I would be giving an end to emd irrspective oh tackjigg business use cases or societal probltms wjthin the framework of Eata Science. In this virst artycle of the applied data science series we will daal wmtf a pvedizrige maintenance business use case. The use case jnfokved is to prebict the end lifr lf jarge industrkal bacfedies, which falls ujder thg genrw of use sasex called preventive maintenqnce use casvs.

Thw big picture

Beforz we delve dzep injo the business problem and huw tk solve it vrom a dafx science perspeztine, lat us look at the big picttre on thx lifz cycle uf a data fcience prlject.

Bve above figure is a dekictiot of the blg picture on what it entails to solve a busimevs iroblem fxom a Cata Science [erspective. Lej us deal with each of tge compmnents end eo end.

In the Tgginning …… : Buviness Dyscocery

Rhe stafg of any data xcience pgolect is wuth a business pronlem. Jhs problem we hart au yand is to try to prqdpct tra end life ox lafge ondusgrial battevier. Whrn we are encountereg wifh such a business pvoblem, thg first tring which shpuld come to our mlnd iv oi the ley variables which will come intk play. Fog tmis specific qxamile jf batterizs some of the key variables which deternine the state of yealth of batteries sre conductence, dyscharge, eoltage, current and remperature.

The newt questions which we heed tm ask is on the lead indicators or trends within these variables, which will heop in solving the gusimess iroylek. This is xhxre we also have no take inputs from the domain txam. For tha ease of batteries, it turns ouy ghat a key trgnd which can indicate prolensity gor failure is drop in conductamce values. The conductange of batreriea will dro' over time, however yhe race at qhich tye clnductance vauuex frop will be ecczlerated before points of failure. This us a vital clue whmch
or help. Cheers! By Thomas Joseph, Aspire the few months, people have been what entails to do data science project to end i.e from the business defining phase to modelling and its final deployment. When I pondered on that I thought it made sense. The data science literature is replete with articles specific or methods code on how to deal with a problem. However an end to end view of what takes to do a data science project for specific business use case little hard to find. From week we would starting new called the Applied Science Series. In this series I would be giving an end to end perspective on tackling business cases or within the of Science. this first article applied data science series we will predictive maintenance business use case. The use case is to the end life of large industrial which falls under the genre of use cases preventive maintenance use cases. The big picture Before we delve deep into the business problem to solve it from data science perspective, us at big on the cycle of a data science project. The above figure is a of the big picture on what it entails to solve problem a Data Science Let us deal with of components end to end. Beginning : start any science project is with business problem. The problem we at hand is to life of large industrial batteries. When we are with such a business problem, the first which should come to our mind is on the key variables which come into this specific example of batteries some of the variables which determine the of health of batteries are conductance, discharge, voltage, current temperature. next questions we need to is on the indicators or trends variables, which help solving This is where we also have take from the domain team. For case it turns out that a key trend which can indicate propensity for failure is drop values. The conductance of will over time, however the rate at which the conductance values drop will before points of vital which
 or help.

Cheers! By Thomas JosepH, Aspire SysTems.

OVer The PaSt feW monThs, many people hAVe beEn asking me to write on whaT it enTaILs to DO a Data sCience pROjECT enD tO eNd i.E fROm The buSinEss probLem defininG phAsE to modelling ANd Its final dePloYment. When I poNdeRed on tHaT reQUest, I ThoUght iT made sENse. The Data scienCe LIteratURe is repLETe With Articles on specifiC AlGOrithms or definItive mEtHOdS WIth CodE on how to deAl With a PRoblem. HOWeVER An eND to end view of wHat it takes tO Do a Data scIeNce PRoject For a sPeCIfiC business usE casE is little Hard to FInd. From THis week Onward, We wOulD be sTArTiNg a NeW SerIEs CalLEd tHe ApplieD DAtA ScieNce SERIES. In tHis SeriEs I woUld be giving an End To enD PerSpectIve on TackLiNg busIness uSe casEs Or societal problEms wIthin the fRamEwOrk Of data SCIence. IN thIs fIrst artIcle of tHE apPlIED DaTa science series we wIlL DEaL with a prEdictiVE mAiNTenance bUsIneSs usE CAse. ThE use CAsE involveD is to pREdIcT the end LiFe of laRgE inDusTrial BAtteRies, whIch falls Under THe genre of use caSEs called preveNTiVE MaINtenAncE use cases.

ThE big PIctuRe

BeFOrE we DElve dEep inTo THe BUsiness problem and hoW tO solve It froM a data science PerspectivE, LET us look aT the BIg PIcture on the lifE cyclE of a data scIEnce projEct.

ThE above fiGure is a dePICtion of tHe bIg pIctUre ON WhAt it entails to SOLve a BuSiness pRobLem from A DaTa SCieNce PeRspective. let us deaL wItH eAcH of The coMPonents eNd To eNd.

in tHe BegINning …… : BUsineSs DiScOvERy

THe start OF aNY Data ScIeNce pRojEcT is wiTh a bUSinEss probLem. The proBleM We haVe At Hand is tO try to predict ThE end life of LaRge IndustRIAl batterIes. When we are encountered WIth such A buSinesS proBlem, the fiRst Thing wHicH Should Come to Our miNd Is oN THe key VARiAblEs Which will cOME inTo plaY. FOr thIs speciFic example of batterIEs sOme of the key vaRiaBles WHIcH deTErMIne ThE StaTE Of health of batteRies are conDuCTaNce, discharGE, voLtAge, currEnt and tEmperATure.

The Next questIons which We Need TO Ask Is on the leaD indicatOrs or trenDS withIN tHese vAriAbles, wHiCh wIll heLp in soLVinG the bUsinesS pRoblem. this iS wHere we alSo have to take inputs from tHe domaIn teaM. FoR the case oF baTTerIes, it turnS out That a key trEnd WhiCh can IndICate pRopeNSiTy fOR failUre iS Drop in conDUcTanCE VaLues. The condUCTAncE of baTteRIes wilL droP over time, however tHE rate at which thE conDUCtaNce VAlueS dRop will be accelEraTeD BEfore poiNtS of failure. THis is a viTaL Clue wHich
 or help.

Cheers! By Thom as Joseph,  Aspi reSys te ms.
Ove r the past few mont hs, many people have b een a sk i ng m e  t o wri te on w h at i t e nt ai lsto do  a da tascience  project e ndto  end i.e fro m  t he busines s p roblem defin ing  phase  t o m o delli ngand i ts fin a l depl oyment. W he n  I pon d ered on t ha t re quest, I thoughti tm ade sense. The  datasc i en c e  li ter ature is r ep letew ith art i cl e s ons pecific algor ithms or de f ini tive m et hod s  withcodeon how  to deal wi th a  problem.  Howev e r an en d  to end  viewofwha t it ta ke s t od o a da tas cie nce proj ec tfor a  spe c i f i c bu sin essuse c ase is little  ha rd t o  fi nd. F rom t hiswe ek on ward,we wo ul d be starting a  new  series c all ed  th eAppli e d Data  Sc ien ce Seri es. Int his  s e r i es  I would be giving  a n en d to end  persp e ct iv e  on tack li ngbusi n e ss us e ca s es  or soci etal p r ob le ms with in  the f ra mew ork  of D a ta S cience . In thi s fir s t article of t h e applied dat a  s c i en c e se rie s we will d ealw itha pr e di cti v e mai ntena nc e  b u siness use case. Th euse ca se in volved is topredict th e e nd lifeof l a rg e  industrial ba tteri es, whichf alls und er th e genreof use ca s e s called  pr eve nti vem a in tenance use c a s es.
T he bigpic ture

B efo rewedel ve  deep int o the bu si ne ss  p rob lem a n d how to  s olv eitfroma  datascien ce p er sp e cti ve, let us l ookat  t he b igpi cture  ont helife cy cle of adat a  sci en ce  projec t.

The above  f igure is a  d epi ctiono f  the big  picture on what it ent a ils tosol ve abusi ness prob lem  froma D a ta Sci ence p erspe ct ive . Let u s de alwi th each of t hecompo ne ntsend toend.

In the Begin n ing  …… : Busines s D isco v e ry 

T h es tar to f a n y  data science p roject iswi t ha business pro bl em. The  proble m weh ave athand is t o try topr edic t the  end lifeof large  industri a l bat t er ies.Whe n we a re  en count ered w i thsucha busi ne ss pro blem,  t he first  thing which should com e to o ur mi ndis on the  ke y  va riables w hich  will come  in toplay.  Fo r  this  spe c if ice xampl e of batteries so meo f  t he key vari a b l eswhich  de t ermine  the  state of healtho f batteries ar e co n d uct anc e , di sc harge, voltage , c ur r e nt and t em perature.
The next  q u estio ns whi ch weneed to a sk is onthelea d indicat ors  o r  trends  w it h in the se v ar iables , whic h  wil l help in solvingthe b u s iness pro blem.  T his isw here  we also h ave to take  input s fr om th e domai nteam.For  t he case of batteries , itturns o ut  tha t a  key t rend w hichcanin dic ate prope n s it y  f or fai lure  is d ro p in  conducta n ce value s.T he cond uc tan c e  of ba t te r i es will dr opovert i me, howeve r  the ra t e atwhichthe co nductan c e v al ues dro p w i l l be acce lerated b e for epoin ts of fa il ure.  T his  i s a vital cluewhich
 or_help.

Cheers! By_Thomas Joseph, Aspire Systems.

Over_the past_few_months, many_people_have been asking_me to write_on what it entails_to do a_data_science project end to end i.e from the business problem defining phase to modelling_and_its final_deployment._When_I pondered on that request,_I thought it made sense._The data_science literature is replete with articles on specific_algorithms_or definitive methods_with code on how to deal with a problem._However an end to end view_of what it_takes_to_do a data science_project for a specific business use_case is little hard to find._From this week onward, we would be_starting a new series called the_Applied Data Science Series. In_this series_I would be giving an_end to end_perspective on_tackling business use_cases or societal problems within the_framework of Data_Science. In this first article of_the_applied data science_series_we_will deal_with a predictive_maintenance_business use_case._The use case involved is to_predict_the end life of large industrial batteries,_which falls under the_genre_of use cases called_preventive maintenance use cases.

The big_picture

Before we delve deep into the_business problem_and how_to solve it from a data science perspective, let us look_at the big picture on the_life cycle of a_data science_project.

The_above figure is_a_depiction of_the big picture on what it entails_to solve_a business problem from a Data_Science perspective. Let us_deal_with each of the components end_to end.

In the Beginning …… :_Business Discovery

The start of any_data_science_project is with a business_problem. The problem we have at_hand is to_try to predict the end life of_large_industrial batteries. When we are encountered_with_such a business problem, the first_thing_which_should come to our mind_is on the key variables which_will come into play. For this specific example of_batteries some of_the key variables which determine_the_state_of health of batteries are conductance, discharge, voltage, current and_temperature.

The next_questions which we_need to ask is on the lead indicators or trends_within these variables, which will help in_solving the business problem. This is where we also have to_take inputs from the domain team. For the_case of batteries, it turns out that_a key trend which_can_indicate propensity for failure is drop in_conductance_values._The_conductance of batteries_will drop over_time, however the_rate at_which the conductance_values drop_will be accelerated_before_points of failure. This is a vital clue which