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KabishasBachelorettePartyWeekend-July2014 100%

(Priya and I are trying to look for a really nice Day Spa with good group rates.

https://www.pdf-archive.com/2014/01/17/kabishasbachelorettepartyweekend-july2014/

17/01/2014 www.pdf-archive.com

crossword (1) 87%

Samira Kanwar feels that makeup artist Priya Gonsalves is ideal for this type of bride (7) 8 Dn &

https://www.pdf-archive.com/2015/09/04/crossword-1/

04/09/2015 www.pdf-archive.com

15 P 254-priyagupta Mar16 87%

10.11591/eei.v5i1.628  126 Leakage Immune 9T-SRAM Cell in Sub-threshold Region Priya Gupta1, Anu Gupta2, Abhijit Asati3 Department of Electrical and Electronics Engineering Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India e-mail:

https://www.pdf-archive.com/2016/09/25/15-p-254-priyagupta-mar16/

25/09/2016 www.pdf-archive.com

Merit List Comm-Category Rankv1 81%

EE No Rank Name 95399 1 MOHIDEEN ASHRAF A 97835 2 ARTHI A 92210 3 BHARATHI PRIYA R 96549 4 SIVARAJ S 94374 5 GOMATHI G 92126 6 SANKARALINGAM U 94691 7 SARANYA S R 98309 8 ARVIND MANOJ K K 92026 9 MEGHANAA J KUMAR 97523 10 THANGALAKSHMI A 96032 11 PRASANNA P 94288 12 BASKARAN V M 92688 13 PADMAVATHI A 95559 14 RADHAKRISHNAN R 95271 15 SUGANYA M 90590 16 ELANCHEZHIAN E 96668 17 JERRY SAM 98014 18 SABARISH S S 94677 19 KRISHNAKUMAR C 96538 20 VIDHYA M 94834 21 MUTHU SUBASH E M V 92264 22 ARULMOZHI V 96172 23 SANTHI K 91612 24 THANGAM V 98177 25 ARULARASAN S 97245 26 DHANASEKARAN R 97492 27 VIDYA SAGAR R 92543 28 GOPINATH G 95696 29 RAJ SANTAN K 90860 30 MAHALAKSHMI M 93633 31 SARAVANAN S K 96092 32 SUMITHRA K 98137 33 FELIX CORDELIA M J 95004 34 YASHWANTH RAJ T 95377 35 VASANTHI T 97508 36 SUGANTH N G 93767 37 UMA MAHESWARI S 93298 38 SWATHY T M 97284 39 LAKSHMI MADHUMATHI M 90232 40 ARUNA S 97989 41 RAJA M 94710 42 MUTHUKUMAR P 97362 43 SYED IBRAHIM SHA J A M 96524 44 KEERTHANA J 97868 45 KARTHIKEYAN S 94912 46 NANDHINI B 92448 47 MEGALADEVI P 98269 48 ARUN KUMAR S 92801 49 MANIKANDAN P Comm.

https://www.pdf-archive.com/2014/03/21/merit-list-comm-category-rankv1/

21/03/2014 www.pdf-archive.com

result 74%

XII First Name Last Name Sec Roll No TOTAL Manisha Ayantika Srija Deblina Srija Puja Soumi Mrittika Shreya Meghna Puja Aditi Maitrayee Priyakshi Lina Priya Suparna Sayari Ankita Dipanwita Sampurna Debasree Shawani Poulomi Sanchary Shreya Debanjali Ritika Ritayan Souranil Akash Arnab Yash Nath Kajori Das Gupta Ray Mukherjee Sarkar Bose Mukherjee Samanta Ganguly Nag Biswas Sarkar Chakraborty Singha Das Sil Chowdhury Biswas Mukherjee Sanyal Saha Ghosh Kumar Biswas Bose Karmakar Banik Roy Biswas Bhunia Nath Sadhukhan B B B B B B B B B B B B B B B B B B B B B B B B B B B B C C C C C 2 3 4 5 6 8 9 10 11 12 13 14 15 16 18 19 20 21 29 30 31 32 44 45 46 49 53 54 1 2 3 4 5 297 303 335 374 292 283 304 306 297 269 300 310 301 283 289 239 285 320 236 243 245 254 318 318 313 218 310 302 382 380 326 296 292 Srayan Ray C 6 310 Dip Arghyadeep Bhattacharyya Banerjee C C 7 8 291 320 Page 4 of 15 R.

https://www.pdf-archive.com/2015/11/09/result/

09/11/2015 www.pdf-archive.com

Telugu Festival PressNote 2015 69%

Classical Fusion composed by Anusha Naidu was well appreciated and performed by Malishka Ambati, Hanitha Sharma Puranam, Geetha Priya,Sreehamsi Koganti, Namashritha Koganti, Asritha Tunguntla, Mahitha Pamulapati and Anusha Aravind.

https://www.pdf-archive.com/2015/08/07/telugu-festival-pressnote-2015/

07/08/2015 www.pdf-archive.com

eranklist 63%

1/389 Applno Name Rank 1100226 ANUSREE B A 28735 21350 1100223 4851 1100227 42002 2302 19130 2977 16608 24721 54595 9573 22772 3738 1223 51784 10522 12697 23674 53952 8059 20337 13343 14293 5882 39692 57350 14102 60806 7344 13627 396 13050 10214 6708 31546 26100 1964 8688 16525 49430 12948 31759 30887 22504 4334 28049 16376 5939 28641 7369 22110 34378 9694 1100228 1100235 1100237 1100239 1100246 1100248 1100249 1100250 1100252 1100256 1100257 1100258 1100261 1100262 1100263 1100267 1100269 1100270 1100271 1100273 1100276 1100277 1100278 1100279 1100280 1100283 1100284 1100285 1100286 1100287 1100291 1100292 1100293 1100295 1100297 1100298 1100299 1100301 1100311 1100313 1100314 1100315 1100318 1100319 1100320 1100321 1100323 1100324 1100327 1100328 ABHINAND V RAMESH GAYATHRI C AMRITA M VARMA ABRAHAM TONY ITTY ANJITHA NAIR ANAKH SANTHOSH KUMAR NASREENA KABEER RUSSELL R AMINA MASOOD AATHIRA BIJU SAHAL SHAJI NIKHIL JAYAKUMAR FAYAS K PAULS JOJI ARUNIKA SAJITH BREVON BASIL K A GOVIND SREEKUMAR DARSHAN S MILINA ELIZA GEORGE THEJAS S PRINCE PETERS AKSHAY AUGUSTINE SHEB ALINA MATHEW ASWAJITH K BABU JISHNU KRISHNA MENON RABDA JABBAR SREEROOP PRASAD SALIM K GOPIKA G DATH MIKUN M PRANAV PRAVEEN AISWARYA S SARATH STANLY AMIEL ANTONY BENEDICT HAFIS AHAMMED T AMRITA V NIKHILU BAISAN SREE LAKSHMI KRISHNAN ALTHAF HUZZAIN K A ANURANJAN A AMAL RASHEED K GREESHMA RAJENDRAN ASLAM ALTHAF PRAMIT P PANTHAYIL REJAA JEWAD AADARSH T BIJU ANUPAMA HARI PRIYA ELIZABETH RAJU SEFIN SEBASTIAN KRISHNANUNNI A K KEERTHY PRAKSH RAVEENA R 24223 2320 12268 37843 25381 38138 36255 33648 4293 16824 7356 19316 29380 2655 28542 2035 27121 17199 11370 518 22015 11227 38358 2884 55511 10194 58756 44605 25252 22706 15054 4510 Withheld 4066 23591 27784 37721 18853 37600 8301 5633 30218 502 9037 51437 19024 39487 10088 2143 16148 41281 60347 Office of the Commissioner for Entrance Examinations, Kerala ENGINEERING RANK LIST 2017 Applno 1100330 1100331 1100334 1100335 1100336 1100337 1100338 1100340 1100342 1100344 1100345 1100349 1100351 1100353 1100354 1100356 1100363 1100367 1100368 1100369 1100370 1100371 1100372 1100373 1100374 1100377 1100378 1100386 1100387 1100388 1100389 1100392 1100393 1100396 1100397 1100398 1100403 1100404 1100406 1100407 1100408 1100409 1100410 1100412 1100413 1100414 1100415 1100419 1100420 1100425 1100427 1100434 1100437 Name Rank MOHAMMED SAHIL M 4588 SHARAFIYYA M K LAKSHMI T CHARUPRIYA MENON SHAHLA JASMIN N BHADRESHA K S IMMANUEL JEYA KUMAR ABHIJITH JS SANDRA S THEERTHA T REENU JACOB MOHAMMED ARFAN ALTHAF YOUSAF C T SANDRA PETER SAMEEHA MOHAMED ALI JUMAANA RUZZEL MUHAMM AISWARYA SURESH ANURANJ S D RISHABH BENOY AMAL KRISHNAN R A ANUSHA HASHIM GOPIKA RANJITH MUHAMMED RAFI M VRINDA VENU ANN MARY TOMY LISHANA K HARIKRISHNAN J SHRI HARI S AFNAN K APARNA P M NAVYA K SAURAV JAYAKRISHNAN C SUMIN SUNIL ABAY KRISHNA A S RITTA JERRARD NESMA NUJUM NIYAZ FATHIMATHUL HAMARA ANN GEORGE HIBA FATHIMA C ALKA V N RIBIN BABY AMAL K ROBIN ALMA K S VYSHNAVI RAJ ASWATHI R SNEHA RAJENDRA PRASAD ABHIJITH MAROTTIKUNNA NIMA VINCENT FATHIMA SHIRIN A N MOHMMED YASHIK L ROSHAN PRAKASH GOVIND V SHENOY ARUN T A Published on 20.06.2017 Applno 22047 1100439 47731 1100441 26997 19234 14544 51175 56393 38195 3037 22516 48848 23489 8401 4023 13635 3162 24312 44473 30448 12678 1265 30561 42515 53412 10734 36523 42580 22665 2965 10501 37725 17667 9911 14243 40935 14011 23428 11313 6555 35072 13101 27470 53828 27973 37595 44380 3602 6134 10584 50401 7717 44209 1100440 1100444 1100445 1100447 1100450 1100453 1100457 1100458 1100460 1100461 1100463 1100464 1100465 1100468 1100471 1100477 1100478 1100484 1100489 1100492 1100493 1100495 1100498 1100501 1100503 1100505 1100507 1100509 1100510 1100511 1100512 1100513 1100514 1100515 1100517 1100518 1100520 1100522 1100525 1100526 1100529 1100530 1100531 1100533 1100534 1100538 1100541 1100542 1100543 1100544 1100545 Name Rank ABHISHEK RAJAN 2223 APARNA S GEETHIKA T R SIDHARTH SATHEESH SHIKHA M BIJU MALAVIKA T S JOHAN SAM THOMAS AKSHAYA VENUGOPAL HARIKRISHNA V SWATHI H JISON MATHEW ALITTA FLORINA REBELL VANDANA T AJUNA B RAJ REETTA SARA GEORGE VISHNU SAJI ABHIJITH PRADEEP GEETHU KRISHNAN S DANIEL GEORGE KRISHNA SANKAR CHANDANA K KARTHIKA CHANDRAN SNEHA JEROMI ABIN A C AKSHAY M ADWAITH J SAI NOEL MATHEW JACOB MATHEW JOHNSON GEETIKA GOPINATH FATHIMA AFRA KT RAJATH P PRANOY S P ANAND S AJNA VAHID SALMA SHAJI ATHUL ANAND M P ADARSH C S AKASH SUNOJ ANUSREE K U ANNMARY JOSEPH SANKEERTHANA M AISWARYA SANAL MEGHA MARY BIJU PARVATHY U KRISHNAN SHILPA G FEMI JOY JIB MATHEW A SHIFY ROSS AMARKRISHNA A S PRAVEENA MEEMBAT SHADA PARVEEN AJAY REJOY J G Page :

https://www.pdf-archive.com/2017/06/20/eranklist/

20/06/2017 www.pdf-archive.com

The Dartmouth Review 8.11.2008 Volume 28, Issue 14 63%

Patrick Raleigh ‘61 n Priya Re-Hash n Language Lapse n A Passage to Hanover Sir— Sir— Sir— I can’t imagine how Priya Venkatesan ‘90 was ever hired.

https://www.pdf-archive.com/2014/04/19/the-dartmouth-review-8-11-2008-volume-28-issue-14/

19/04/2014 www.pdf-archive.com

The Dartmouth Review 5.16.2008 Volume 28, Issue 12 62%

Uphold 1891 Page The Dartmouth Review May 16, 2008 The Week In Review Venkatesan’s Class Given Pass/Fail The college notified students of Priya Venkatesan’s winter Writing 5 class that they will be given the option of simply receiving credit or keeping their original grade for the class.

https://www.pdf-archive.com/2014/04/19/the-dartmouth-review-5-16-2008-volume-28-issue-12/

19/04/2014 www.pdf-archive.com

DAND AMA 11.29.17 51%

Udacity​ ​Data​ ​Analyst​ ​Nanodegree​ ​Program:  Ask​ ​Me​ ​Anything,​ ​with​ ​Data​ ​Analyst​ ​ND​ ​Alum,​ D ​ ylan​ ​Lennard  This​ ​event​ ​was​ ​hosted​ ​in​ ​the​ ​Data​ ​Analyst​ ​ND​ ​Student​ ​Slack​ ​community​ ​on​ ​November​ ​29,​ ​2017.      Transcript​:    Eric​ ​Elliott,​ C ​ ommunity​ ​Manager,​ ​School​ ​of​ ​Data:  Welcome​ ​to​ ​Ask​ ​Me​ ​Anything!    Our​ ​guest​ ​for​ ​this​ ​event​ ​is​ ​Dylan​ ​Lennard,​ ​a​ ​graduate​ ​of​ ​the​ ​Data​ ​Analyst​ ​Nanodegree​ ​Program.     Dylan​ ​is​ ​currently​ ​working​ ​with​ ​Udacity​ ​as​ ​a​ ​Session​ ​Lead​ ​for​ ​the​ ​in​ ​person​ ​Connect​ ​Session​ ​for  DAND.​ ​He​ ​has​ ​also​ ​worked​ ​in​ ​product​ ​analytics​ ​in​ ​the​ ​San​ ​Francisco​ ​Bay​ ​Area​ ​for​ ​the​ ​past  couple​ ​of​ ​years.​ ​He​ ​graduated​ ​from​ ​University​ ​of​ ​California—Davis​ ​in​ ​2015​ ​with​ ​a​ ​bachelor’s  degree​ ​in​ ​Economics​ ​and​ ​Minor​ ​in​ ​Statistics.​ ​In​ ​his​ ​spare​ ​time​ ​Dylan​ ​loves​ ​to​ ​ride​ ​his​ ​bike,  workout,​ ​explore​ ​the​ ​hills​ ​and​ ​nature​ ​of​ ​San​ ​Francisco,​ ​and​ ​work​ ​on​ ​various​ ​projects​ ​such​ ​as  building​ ​apps,​ ​expanding​ ​his​ ​skills​ ​as​ ​a​ ​data​ ​worker,​ ​and​ ​spending​ ​time​ ​with​ ​friends.    Dylan​ ​is​ ​here​ ​to​ ​discuss​ ​the​ ​opportunities​ ​and​ ​challenges​ ​he​ ​encountered​ ​after​ ​graduating​ ​from  the​ ​Data​ ​Analyst​ ​Nanodegree​ ​Program,​ ​and​ ​provide​ ​some​ ​tips​ ​and​ ​insights​ ​for​ ​all​ ​of​ ​you​ ​future  DAND​ ​graduates.    If​ ​you​ ​have​ ​questions​ ​on​ ​these​ ​topics​ ​or​ ​anything​ ​related​ ​to​ ​the​ ​transition​ ​from​ ​Data​ ​Analyst  student​ ​to​ ​Data​ ​Analyst​ ​job​ ​seeker,​ ​or​ ​about​ ​applying​ ​the​ ​skills​ ​you’re​ ​learning​ ​at​ ​Udacity​ ​in​ ​your  current​ ​job,​ ​please​ ​post​ ​them​ ​and​ ​Dylan​ ​will​ ​try​ ​to​ ​answer​ ​as​ ​many​ ​as​ ​possible.    Dylan​ ​Lennard​:  Hey​ ​Everyone,​ ​happy​ ​to​ ​be​ ​here!​ ​Thanks​ ​Eric​ ​for​ ​having​ ​me.​ ​I​ ​think​ ​he​ ​did​ ​a​ ​good​ ​job  introducing​ ​so​ ​ask​ ​away!      Robert​ ​Manriquez​:  Hey​ ​everyone!​ ​ ​I've​ ​got​ ​a​ ​few​ ​career​ ​guidance​ ​questions,​ ​I'll​ ​post​ ​a​ ​few​ ​here.​ ​ ​Please​ ​feel​ ​free​ ​to  join​ ​the​ ​conversation!​ ​ ​Here​ ​are​ ​mine:  1)​ ​ ​What​ ​are​ ​some​ ​effective​ ​ways​ ​to​ ​leverage​ ​your​ ​Udacity​ ​Nanodegree​ ​into​ ​landing​ ​an  interview?  2)​ ​ ​Is​ ​there​ ​any​ ​job​ ​placement​ ​data​ ​for​ ​udacity​ ​graduates?  3)​ ​ ​For​ ​students​ ​who​ ​are​ ​transitioning​ ​into​ ​the​ ​tech​ ​industry,​ ​what​ ​qualities​ ​or​ ​strategies​ ​are  there​ ​to​ ​prove​ ​you're​ ​job​ ​ready​ ​with​ ​just​ ​a​ ​nanodegree​ ​and​ ​portfolio?      Dylan​ ​Lennard​:  Hey​ ​Robert.     2)​ ​We’ll​ ​have​ ​someone​ ​look​ ​into​ ​that,​ ​I​ ​don’t​ ​have​ ​any​ ​data​ ​there.     1)​ ​That’s​ ​a​ ​tricky​ ​question.​ ​Cover​ ​letters​ ​are​ ​probably​ ​your​ ​best​ ​friend​ ​here,​ ​speaking​ ​to​ ​the  difficulty,​ ​the​ ​rigor,​ ​the​ ​time​ ​commitment,​ ​and​ ​the​ ​interest​ ​that​ ​gave​ ​you​ ​the​ ​will​ ​to​ ​do​ ​this  while​ ​also​ ​working​ ​and​ ​going​ ​forward​ ​with​ ​other​ ​commitments.​ ​It’s​ ​best​ ​if​ ​you​ ​can​ ​use​ ​it  alongside​ ​other​ ​work​ ​experience​ ​to​ ​tell​ ​the​ ​story​ ​of​ ​someone​ ​who​ ​is​ ​up​ ​and​ ​coming​ ​and  looking​ ​to​ ​prove​ ​themselves​ ​as​ ​a​ ​data​ ​worker.     3)​ ​The​ ​biggest​ ​thing​ ​here​ ​I​ ​think​ ​is​ ​pivoting​ ​on​ ​what​ ​you’ve​ ​already​ ​done.​ ​You​ ​have​ ​experience  as​ ​a​ ​worker​ ​already​ ​which​ ​is​ ​worth​ ​a​ ​lot​ ​(industry​ ​knowledge,​ ​how​ ​to​ ​be​ ​a​ ​good​ ​employee,​ ​how  to​ ​work​ ​with​ ​people,​ ​etc.).​ ​Tie​ ​this​ ​nanodegree​ ​in​ ​with​ ​that​ ​experience​ ​if​ ​you​ ​can.​ ​Also,​ ​try​ ​to  do​ ​data​ ​work​ ​that​ ​is​ ​closely​ ​related​ ​to​ ​your​ ​field​ ​so​ ​your​ ​domain​ ​expertise​ ​can​ ​shine​ ​with​ ​your  analysis.​ ​It​ ​just​ ​makes​ ​you​ ​look​ ​that​ ​much​ ​better.​ ​Make​ ​sure​ ​your​ ​github​ ​looks​ ​good  (everything​ ​has​ ​clear​ ​README.md​ ​files)​ ​and​ ​your​ ​linkedin​ ​is​ ​good​ ​as​ ​well.    Robert​ ​Manriquez​:   Dylan,  Thank​ ​you​ ​for​ ​taking​ ​the​ ​time​ ​to​ ​reply!​ ​ ​That's​ ​quite​ ​a​ ​good​ ​piece​ ​of​ ​advice,​ ​weaving​ ​my  experience​ ​and​ ​data​ ​skills​ ​into​ ​a​ ​story​ ​for​ ​potential​ ​employers.​ ​ ​I'll​ ​definitely​ ​keep​ ​it​ ​in​ ​mind​ ​as​ ​I  job​ ​search,​ ​thank​ ​you!      S​ ​Azhar​:  Question​ ​:​ ​ ​How​ ​to​ ​successfully​ ​follow​ ​the​ ​schedule​ ​for​ ​nanodegree​ ​?​ ​(with​ ​job​ ​and​ ​travelling​ ​its  getting​ ​hard​ ​to​ ​take​ ​time​ ​out,​ ​anyone​ ​in​ ​same​ ​boat​ ​??)    Dylan​ ​Lennard​:  Hey​ ​Azhar,     The​ ​truth​ ​is​ ​it​ ​takes​ ​a​ ​lot​ ​of​ ​dedication.​ ​Carve​ ​out​ ​time​ ​each​ ​day​ ​(if​ ​possible)​ ​and​ ​a​ ​big​ ​chunk  each​ ​week​ ​to​ ​the​ ​program.​ ​Most​ ​importantly,​ ​make​ ​sure​ ​you’re​ ​ready​ ​for​ ​something​ ​like​ ​this.​ ​A  lot​ ​of​ ​people​ ​underestimate​ ​how​ ​much​ ​work​ ​is​ ​involved,​ ​but​ ​the​ ​payoff​ ​is​ ​great!      I​ ​personally​ ​watched​ ​videos​ ​in​ ​the​ ​morning​ ​and​ ​night,​ ​and​ ​worked​ ​on​ ​projects​ ​on​ ​the​ ​weekend  for​ ​at​ ​least​ ​5-10​ ​hours.​ ​That​ ​worked​ ​really​ ​well​ ​for​ ​me​ ​as​ ​a​ ​strategy.            zey:  For​ ​getting​ ​your​ ​first​ ​job,​ ​how​ ​many​ ​job​ ​applications​ ​did​ ​you​ ​send​ ​out?​ ​ ​What​ ​job​ ​websites​ ​did  you​ ​use?​ ​indeed?​ ​Dice?​ ​Monster?​ ​LinkedIn?      Dylan​ ​Lennard​:  Hey​ ​Zane,     Great​ ​question.​ ​I​ ​actually​ ​had​ ​an​ ​opportunity​ ​arise​ ​internally​ ​in​ ​the​ ​company​ ​I​ ​was​ ​with​ ​when​ ​I  started​ ​the​ ​nanodegree,​ ​and​ ​I​ ​was​ ​able​ ​to​ ​use​ ​the​ ​nanodegree​ ​and​ ​my​ ​experience​ ​at​ ​work​ ​to  land​ ​my​ ​next​ ​opportunity.​ ​When​ ​that​ ​time​ ​came,​ ​I​ ​had​ ​originally​ ​sent​ ​out​ ​many​ ​applications  and​ ​gotten​ ​a​ ​few​ ​hits.​ ​I​ ​changed​ ​my​ ​approach​ ​after​ ​getting​ ​more​ ​experience​ ​and​ ​focused​ ​on  LinkedIn.​ ​I​ ​had​ ​a​ ​lot​ ​of​ ​success​ ​with​ ​recruiters,​ ​and​ ​managed​ ​to​ ​get​ ​multiple​ ​high​ ​paying​ ​offers  around​ ​the​ ​same​ ​time​ ​without​ ​applying​ ​anywhere.​ ​Making​ ​your​ ​LinkedIn​ ​clean​ ​and​ ​making  yourself​ ​available​ ​for​ ​opportunities​ ​(there’s​ ​an​ ​option​ ​to​ ​let​ ​recruiters​ ​know​ ​you’re​ ​open)​ ​can  pay​ ​off​ ​very​ ​well​ ​(but​ ​be​ ​careful​ ​with​ ​contracting​ ​roles,​ ​there​ ​are​ ​pluses​ ​and​ ​minuses).      Guanrong​ ​Fu:  Question:​ ​How​ ​did​ ​you​ ​find​ ​your​ ​first​ ​data-related​ ​job?​ ​I​ ​am​ ​in​ ​a​ ​career​ ​transition​ ​right​ ​now,​ ​and  have​ ​finished​ ​DAND​ ​2​ ​months​ ​ago,​ ​but​ ​still​ ​cannot​ ​find​ ​a​ ​data-related​ ​position.    Dylan​ ​Lennard​:  Hello!     As​ ​said​ ​previously,​ ​I​ ​got​ ​lucky​ ​and​ ​had​ ​some​ ​internal​ ​opportunities​ ​open​ ​up​ ​at​ ​my​ ​first​ ​job,​ ​so  that’s​ ​where​ ​I​ ​started.​ ​If​ ​you’re​ ​having​ ​trouble​ ​in​ ​the​ ​job​ ​market,​ ​look​ ​for​ ​any​ ​data​ ​related  opportunity​ ​you​ ​can​ ​find​ ​at​ ​work​ ​and​ ​try​ ​to​ ​take​ ​advantage​ ​of​ ​it​ ​and​ ​put​ ​it​ ​on​ ​your​ ​resume.​ ​If  not​ ​possible,​ ​go​ ​to​ ​meet​ ​ups,​ ​do​ ​some​ ​analysis​ ​on​ ​your​ ​own.​ ​I​ ​once​ ​had​ ​a​ ​gaming​ ​product  manager​ ​suggest​ ​that​ ​I​ ​go​ ​to​ ​a​ ​startup​ ​and​ ​offer​ ​to​ ​work​ ​for​ ​free​ ​on​ ​weekends​ ​as​ ​an​ ​intern  (this​ ​was​ ​a​ ​last​ ​resort​ ​type​ ​of​ ​thing)​ ​to​ ​get​ ​the​ ​experience.​ ​That’s​ ​a​ ​bit​ ​extreme,​ ​but​ ​do​ ​what  you​ ​can​ ​to​ ​show​ ​people​ ​that​ ​you​ ​care​ ​about​ ​data!      zey:  How​ ​long​ ​did​ ​it​ ​take​ ​for​ ​you​ ​to​ ​finish​ ​the​ ​DAND?​ ​ ​What​ ​is​ ​the​ ​average​ ​time​ ​for​ ​students​ ​to  complete?    Dylan​ ​Lennard​:  It​ ​took​ ​me​ ​almost​ ​exactly​ ​1​ ​year,​ ​I’ll​ ​be​ ​honest.​ ​Back​ ​then,​ ​it​ ​wasn’t​ ​done​ ​in​ ​‘terms’,​ ​but​ ​was​ ​a  monthly​ ​subscription.​ ​I​ ​took​ ​long​ ​breaks​ ​where​ ​I​ ​didn’t​ ​do​ ​much​ ​(month​ ​or​ ​so),​ ​and​ ​then​ ​I​ ​had  months​ ​where​ ​I​ ​got​ ​multiple​ ​classes​ ​done.​ ​That’s​ ​how​ ​I​ ​operate​ ​as​ ​a​ ​person,​ ​but​ ​everyone​ ​is  different.​ ​I​ ​am​ ​currently​ ​running​ ​the​ ​Connect​ ​in​ ​person​ ​session​ ​for​ ​DAND​ ​and​ ​we​ ​finish​ ​in  about​ ​4​ ​months​ ​total​ ​time.​ ​It’s​ ​aggressive,​ ​but​ ​it​ ​can​ ​be​ ​done!      S​ ​Azhar​:  @Salvi​ ​while​ ​Dylan​ ​is​ ​answering​ ​some​ ​questions...​ ​i​ ​will​ ​provide​ ​my​ ​2​ ​cents.​ ​Start​ ​with​ ​one​ ​tool  or​ ​technology​ ​...​ ​get​ ​perfect​ ​then​ ​move​ ​onto​ ​the​ ​other​ ​tools..​ ​once​ ​u​ ​learn​ ​at​ ​least​ ​3​ ​then​ ​u​ ​can  multitask..since​ ​u​ ​will​ ​be​ ​confident​ ​by​ ​then....    Dylan​ ​Lennard​:  I​ ​very​ ​much​ ​agree​ ​with​ ​this​ ​sentiment.​ ​Become​ ​a​ ​beast​ ​with​ ​a​ ​few​ ​tools​ ​(SQL​ ​and​ ​Python​ ​I’d  recommend​ ​for​ ​y’all),​ ​and​ ​maybe​ ​lightly​ ​a​ ​third​ ​(for​ ​example,​ ​R).​ ​That​ ​way​ ​if​ ​you​ ​ever​ ​get​ ​called  on​ ​to​ ​do​ ​a​ ​language​ ​you’re​ ​not​ ​super​ ​savvy​ ​with​ ​(say​ ​you​ ​know​ ​python​ ​but​ ​not​ ​R​ ​well),​ ​then​ ​you  can​ ​speak​ ​to​ ​your​ ​python​ ​skills​ ​and​ ​make​ ​them​ ​feel​ ​comfortable​ ​that​ ​R​ ​will​ ​be​ ​an​ ​easy  transition.      Hong​ ​Kai​ ​Lee​:  Hey​ ​Dylan​ ​and​ ​all,  1.​ ​I​ ​would​ ​like​ ​to​ ​know​ ​if​ ​there​ ​is​ ​a​ ​job​ ​vacancy​ ​list​ ​from​ ​companies​ ​who​ ​are​ ​interested​ ​to​ ​hire  talents​ ​from​ ​Udacity​ ​Nanodegree​ ​Graduate,​ ​or​ ​rather​ ​a​ ​direct​ ​referral​ ​program​ ​from​ ​Udacity​ ​to  some​ ​data​ ​science​ ​companies/startups?    Dylan​ ​Lennard​:  Hey!     I​ ​am​ ​not​ ​directly​ ​sure​ ​about​ ​that,​ ​I’m​ ​not​ ​involved​ ​with​ ​the​ ​career​ ​services​ ​from​ ​Udacity.  However,​ ​Udacity​ ​does​ ​have​ ​the​ ​Career​ ​Portal​ ​which​ ​you​ ​all​ ​should​ ​have​ ​access​ ​to.​ ​Here​ ​you  can​ ​find​ ​resources​ ​for​ ​future​ ​employment,​ ​resume​ ​help,​ ​etc.   https://blog.udacity.com/2017/09/get-hired-udacity-career-portal.html      Priya​ ​Pradhan​:  Hi​ ​Dylan,​ ​Thanks​ ​for​ ​offering​ ​this​ ​session.​ ​I​ ​have​ ​three​ ​questions:​ ​1.​ ​how​ ​long​ ​did​ ​it​ ​take​ ​for​ ​you  to​ ​get​ ​a​ ​job​ ​after​ ​you​ ​started​ ​the​ ​job​ ​search​ ​in​ ​this​ ​field?​ ​2.​ ​Is​ ​this​ ​nanodegree​ ​enough​ ​or​ ​needs  to​ ​be​ ​coupled​ ​with​ ​some​ ​more​ ​skills​ ​to​ ​get​ ​a​ ​job?​ ​3.​ ​What​ ​are​ ​the​ ​major​ ​companies​ ​hiring​ ​in​ ​this  field?    Dylan​ ​Lennard​:  3)​ ​I’m​ ​not​ ​entirely​ ​sure,​ ​there’s​ ​always​ ​people​ ​looking​ ​for​ ​data​ ​workers​ ​though.   1)​ ​As​ ​stated,​ ​I​ ​got​ ​lucky​ ​and​ ​got​ ​an​ ​internal​ ​role​ ​about​ ​6​ ​or​ ​so​ ​months​ ​after​ ​working​ ​my​ ​first  job​ ​and​ ​starting​ ​the​ ​Nanodegree.   2)​ ​If​ ​you’re​ ​looking​ ​to​ ​work​ ​as​ ​a​ ​product​ ​analyst​ ​or​ ​data​ ​analyst,​ ​you’re​ ​good​ ​to​ ​go​ ​if​ ​you’re  confident​ ​in​ ​your​ ​projects​ ​and​ ​abilities.​ ​If​ ​not,​ ​you’ll​ ​need​ ​at​ ​least​ ​a​ ​few​ ​more​ ​things​ ​to​ ​get​ ​into  a​ ​role​ ​you’d​ ​like,​ ​for​ ​example​ ​a​ ​little​ ​work​ ​experience​ ​that​ ​you​ ​can​ ​pivot​ ​on.     If​ ​you’re​ ​looking​ ​to​ ​go​ ​into​ ​a​ ​data​ ​science​ ​role​ ​specifically,​ ​you’ve​ ​got​ ​to​ ​get​ ​a​ ​lot​ ​more​ ​under  your​ ​belt​ ​in​ ​terms​ ​of​ ​knowledge​ ​and​ ​projects.​ ​However,​ ​the​ ​DAND​ ​is​ ​a​ ​great​ ​place​ ​to​ ​start,​ ​and  a​ ​first​ ​job​ ​in​ ​data​ ​is​ ​another.​ ​Once​ ​you​ ​feel​ ​confident​ ​as​ ​a​ ​data​ ​worker,​ ​if​ ​you’re​ ​trying​ ​to​ ​go  Data​ ​Science​ ​I’d​ ​recommend​ ​the​ ​Machine​ ​Learning​ ​Nanodegree.​ ​I​ ​hear​ ​nothing​ ​but​ ​good  things,​ ​and​ ​am​ ​considering​ ​enrolling​ ​myself​ ​to​ ​be​ ​perfectly​ ​honest!        sanghun​ ​chae​:  Question:​ ​After​ ​you​ ​finished​ ​DAND,​ ​what​ ​have​ ​you​ ​been​ ​doing​ ​to​ ​keep​ ​improving​ ​your​ ​skill​ ​set?  Taking​ ​machine​ ​learning​ ​courses​ ​at​ ​Udacity?​ ​or​ ​studing​ ​stastic?    Dylan​ ​Lennard​:  This​ ​is​ ​a​ ​great​ ​question!​ ​I​ ​worked​ ​on​ ​many​ ​projects,​ ​some​ ​solo​ ​and​ ​some​ ​with​ ​Udacity.​ ​I  focused​ ​as​ ​much​ ​as​ ​I​ ​could​ ​in​ ​my​ ​job​ ​on​ ​technical​ ​projects​ ​using​ ​R,​ ​SQL,​ ​and​ ​I​ ​spearheaded​ ​an  initiative​ ​to​ ​move​ ​the​ ​company​ ​from​ ​using​ ​R​ ​to​ ​python​ ​since​ ​we​ ​didn’t​ ​have​ ​many​ ​analysts​ ​but  had​ ​many​ ​engineers.     *Work​ ​on​ ​fun​ ​projects!*​ ​That’s​ ​my​ ​best​ ​advice.​ ​I’m​ ​also​ ​currently​ ​learning​ ​c++​ ​on​ ​my​ ​own​ ​and  getting​ ​ready​ ​to​ ​take​ ​classes​ ​again​ ​next​ ​semester​ ​to​ ​expand​ ​my​ ​computer​ ​science​ ​knowledge  base​ ​and​ ​skill​ ​set.      Mrinal​ ​Roy​:  In​ ​addition​ ​to​ ​the​ ​course​ ​exercises​ ​and​ ​projects,​ ​which​ ​additional​ ​resources​ ​will​ ​you​ ​suggest?    Dylan​ ​Lennard​:  Anything​ ​that​ ​sparks​ ​your​ ​curiosity​ ​or​ ​drives​ ​you​ ​for​ ​the​ ​moment.​ ​I​ ​am​ ​teaching​ ​the​ ​Connect  session​ ​for​ ​DAND​ ​right​ ​now,​ ​and​ ​one​ ​thing​ ​I​ ​wanted​ ​to​ ​do​ ​was​ ​to​ ​automate​ ​the​ ​data​ ​loading  process​ ​in​ ​the​ ​data​ ​wrangling​ ​course.​ ​So​ ​I​ ​wrote​ ​scripts​ ​to​ ​fetch​ ​the​ ​data​ ​from​ ​a​ ​URL,​ ​read​ ​that  into​ ​an​ ​OSM​ ​file,​ ​read​ ​the​ ​OSM​ ​in,​ ​perform​ ​the​ ​necessary​ ​changes​ ​to​ ​the​ ​data,​ ​load​ ​the​ ​data​ ​to  csv,​ ​and​ ​then​ ​create​ ​the​ ​DB,​ ​create​ ​the​ ​tables,​ ​and​ ​load​ ​the​ ​data​ ​into​ ​the​ ​local​ ​database​ ​all  using​ ​python.​ ​It​ ​was​ ​a​ ​super​ ​fun​ ​project​ ​that​ ​took​ ​me​ ​forever,​ ​but​ ​very​ ​rewarding.     I’ve​ ​also​ ​done​ ​the​ ​machine​ ​learning​ ​for​ ​trading​ ​course​ ​which​ ​was​ ​fun,​ ​really​ ​just​ ​find  something​ ​you​ ​think​ ​is​ ​fun​ ​and​ ​work​ ​on​ ​it.​ ​Find​ ​a​ ​good​ ​blog,​ ​and​ ​try​ ​to​ ​copy​ ​their​ ​code,​ ​find​ ​a  buddy​ ​who​ ​is​ ​interested​ ​in​ ​this​ ​stuff​ ​and​ ​work​ ​with​ ​him/her.    

https://www.pdf-archive.com/2017/12/10/dand-ama-11-29-17/

10/12/2017 www.pdf-archive.com

DKN PGRI 2013-2014 Page 1-5 44%

r,NS NA IlN, NS NA NA :02 JUMLAH RATA UN NS NA NA KET 105 4-14-05-17 -108- I 05-8 JOKO TRIONO 7,20 8,23 7,6 5,80 8,15 6,7 4,00 7,94 5,6 8,91 8,20 8,6 25,91 32,52 28,50 7r1 L 106 4-14-05-17 -108- 106-7 KI]STIAWAN 9,00 8,58 8,8 6,40 8,08 7,7 6,50 8,20 7,2 8,84 8,45 8,7 30,74 33,31 31,80 8ro 107 4-14-05-17-108-107-6 LANAZ HUDHAADILANA 7,20 8,39 7,7 7,20 8,02 7,5 4,75 7,82 6,0 8,84 8,34 8,6 27,99 32,57 29,80 715 108 4-t4-05-17 -108- 108-5 LANGGENG DWI PRASETIO 7,60 8,68 8,0 5,00 8,07 6,2 2,50 8,10 4,7 8,77 8,36 8,6 23,87 33,21 27,50 619 109 4-14-0s-17-r08-109-4 NANDANG ANDAN PERMANA 6,80 8,23 7,4 8,00 8,20 8,1 6,00 8,38 7,0 9,12 8,45 8,9 29,92 33,26 31,40 719 L L L L 110 4-14-05-17-108-l l0-3 NOPI PONCO PRASTIYO 5,60 8,38 6,7 7,00 8,02 7,4 5,50 7,77 6,4 8,84 8,13 8,6 26,94 32,30 29,10 713 L lll 4-14-05-17-108-l OVAN TEGUH PRASTIO 7,40 8,55 7,9 7,20 8,10 7,6 6,75 7,91 7,2 9,20 8,26 8,8 30,55 32,82 31,50 719 L tt2 4-14-05-t7-t08-l l2-9 PAHARAUKIRIRI 8,40 8,42 8,4 7,20 7,89 7,5 5,50 7,84 6,4 8,77 8,01 8,5 29,87 32,16 30,80 7'7 L 113 4-14-05-17-108-l l3-8 PANJI ST]LISTIYONO 7,00 8,64 7,7 7,20 7,99 7,5 5,75 7,95 6,6 8,84 8,09 8,5 28,79 32,67 30,30 716 tt4 4-14-0s-r7-r08-l l4-7 PRIYA SETIYA SASANGKA 7,60 8,54 8,0 7,40 8,10 7,7 4,25 7,81 5,7 8,69 8,27 8,5 27,94 32,72 29,90 715 115 4-14-0s-17-r08-l l5-6 PUJI SANTOSO 4,60 8,39 6,7 7,00 7,97 7,4 3,25 7,87 5,1 8,91 8,22 8,6 23,76 32,45 27,20 618 L L L 116 4-14-0s-r7-108-1 l6-5 RIDHO ACHMAD SYAHRONI 8,20 8,51 8,3 7,20 7,98 7,5 4,50 7,91 5,9 8,91 8,20 8,6 28,81 32,60 30,30 7,,6 L l17 4-14-0s-17-r08-l t7-4 RIHKI ASMAUL PAIJIN 6,40 8,24 7,1 5,80 7,93 6,7 4,00 7,74 5,5 8,84 8,20 8,6 25,04 32,11 27,90 7r0 L 118 4-14-05-r7-r08-l l8-3 RIZAL EKO CAHYONO 3,20 8,25 <) 5,40 8,16 6,5 4,25 7,80 5,7 8,84 8,22 8,6 21,69 32,43 26,00 615 L 119 4-14-0s-17-r08-l l9-2 ROBI TURDIANSAH 7,20 8,01 7,5 6,20 8,03 6,9 6,00 7,78 6,7 8,39 8,17 8,3 27,79 31,99 29,40 714 t20 4-14-05-17 -108-120-9 SIDIK WIDHIA SENA 8,40 8,26 8,3 7,40 8,38 7,8 4,00 8,20 5,7 8,91 8,29 8,7 28,71 33,13 30,50 716 121 4-14-05-17 -r08-12 l-8 TAUFIQULNURROHMAN 7,00 8,34 7,5 5,20 8,03 6,3 5,25 7,95 6,3 8,84 8,45 8,7 26,29 32,77 28,80 712 t22 4-14-05-17-108-122-7 TRI WIDIANTORO 6,40 8,56 7,3 6,80 7,96 7,3 4,50 7,96 5,9 8,84 8,27 8,6 26,54 32,75 29,10 73 L L L L 123 4-14-0s-17-r08-123-6 VERDA AnI PRATAMA 8,20 8,44 8,3 8,00 8,07 8,0 6,50 7,99 7,1 8,84 8,44 8,7 31,54 32,94 32,10 8r0 L 124 4-14-0s-17 -108-124-5 WAHYU 8,20 8,38 8,3 6,20 8,08 7,0 3,50 7,88 5,3 8,69 8,46 8,6 26,59 32,80 29,20 713 L r25 4-14-05-t7-r08-12s4 WA}ryU BUDIONO 7,20 8,46 7,7 5,40 8,03 6,5 2,75 8,24 5,0 8,84 8,53 8,7 24,19 33,26 27,90 7r0 126 4-14-05-17 -108-126-3 WAHYUEKOPURNOMO 8,00 8,34 8,1 6,80 8,02 7,3 5,25 7,95 6,3 8,91 8,51 8,8 28,96 32,82 30,50 716 127 4-t4-05-17-108-127-2 WAHYUNUGROHO 6,40 8,28 7,2 3,00 7,98 5,0 )')\ 7,77 4,5 8,62 8,29 8,5 20,27 32"32 25,20 613 YOGA INDRA VERDILLA 7,00 8,39 7,6 6,80 8,04 7,3 6,00 7,82 6,7 8,99 8,42 8,8 28,79 32,67 30,40 716 L L L L L L L 1 l-2 128 4-14-0s-17 -108-128-9 129 4-t4-0s-17-r08-129-8 YOGY AGUS SETIAWAN 9,00 8,25 8,7 7,60 8,1 I 7,8 5,75 8,r7 6,7 8,77 8,51 8,7 31,12 33,04 31,90 8r0 130 4-14-05-17-108-1 30-7 AANANDRIANA 7,20 8,08 7,6 5,40 7,74 6,3 5,50 8,03 6,5 8,39 8,16 8,3 26,49 32,01 28,70 712 131 4-14-05-17 AAN PRAYODA 5,00 8,17 6,3 6,60 7,86 7,1 5,50 7,78 6,4 8,16 8,22 8,2 25,26 32,03 28,00 7r0 -1 08-t 3I -6 rmilililililililffi ilulillulillllll

https://www.pdf-archive.com/2014/05/20/dkn-pgri-2013-2014-page-1-5/

20/05/2014 www.pdf-archive.com

The Dartmouth Review 5.5.2008 Volume 28, Issue 11 37%

On April 27, 2008, the Review broke the news that Professor Priya Venkatesan—being rather put-out upon discovering that Dartmouth is in fact not a real research university and that, equally bad, Dartmouth is a veritable coven of bigoted buffoons incapable of making her feel special and loved­—is now threatening to sue her knuckle-dragging students for violating “anti-federal discriminatory laws” (see page 8-9).

https://www.pdf-archive.com/2014/04/19/the-dartmouth-review-5-5-2008-volume-28-issue-11/

19/04/2014 www.pdf-archive.com

Unish Dicitonary 11%

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https://www.pdf-archive.com/2014/01/21/unish-dicitonary/

21/01/2014 www.pdf-archive.com

OFFSIDE MASTER TEXT PROOF.PDF 3%

Linda Atherton; Tony Fogg; Cody Jackson Hawksworth; Charlie Henson; Dylan Anderson; Phil Earp; Gaynor Sharp; Amy Maggie Bullock; Callum J Cunningham; Liam Cunningham; Abigail Deighton; Juliette Deighton; Joe Kirkwood; Toby Huff; Jacob Huff; Aden Huff; Madeline Huff; Emilia Kirkwood; Richard Patocka; - DERYCK WILLIAM EARDLEY -; Tristram Reed; Kamal Manku; Michelle Manku; Tej Manku; Maya Manku; Priya Manku; Roeland Pieter Perold; David John De Hallé; Robert Patrick Clarke; Julie Clarke; ALAN J NICHOLSON; Alexa GIBBIN; Olivia GIBBIN; Hunter Binks; Gary Broomfield ; Malcolm Palmer; Dennet Wempe; Tim Spilman; Maximillian Andrew Gibbins; Jason Weatherall; **Scott Henderson**; Michelle Crossman ; Edward Nedham; Theo Nedham; John Theobald; Karen Theobald; CHARLIE (COOL DUDE) RICHARDSON; Heather Beaumont; Ethan James Bullock; Eliza Allford; Nicki Glazzard &

https://www.pdf-archive.com/2017/12/15/offside-master-text-proof/

15/12/2017 www.pdf-archive.com