Category: India
Analysing NDA martyrs using R #rstats
NDA – National Defence Academy
The National Defence Academy (NDA) is the Joint Services academy of the Indian Armed Forces, where cadets of the three services, the Army, the Navy and the Air Force train together before they go on to pre-commissioning training in their respective service academies. The NDA is located at Khadakwasla near Pune, Maharashtra. It is the first tri-service academy in the world.
NDA alumni have led and fought in every major conflict in which the Indian Armed Forces has been called to action since the academy was established. The alumni include 3 Param Vir Chakra recipients and 9 Ashoka Chakra recipients. National Defence Academy has produced 27 service Chiefs Of Staff till date. Current Chiefs Of Staff of the Army, the Navy and the Air Force are all NDA alumni.
http://rpubs.com/newajay/ndamartyrs
library(XML)
url="http://nda.nic.in/martyrs.html"
tables=readHTMLTable(url)
str(tables)## List of 13
## $ NULL :'data.frame': 303 obs. of 5 variables:
## ..$ V1: Factor w/ 23 levels "","2/LT","BRIG",..: 1 1 1 8 1 18 17 17 16 1 ...
## ..$ V2: Factor w/ 279 levels "","A BANDYOPADHYAY",..: 79 NA NA NA NA NA 89 NA NA NA ...
## ..$ V3: Factor w/ 183 levels "","-","1 DOGRA",..: NA NA NA NA NA NA NA NA NA NA ...
## ..$ V4: Factor w/ 91 levels "1","10","100",..: NA NA NA NA NA NA NA NA NA NA ...
## ..$ V5: Factor w/ 16 levels "A","B","C","D",..: NA NA NA NA NA NA NA NA NA NA ...
## $ NULL :'data.frame': 301 obs. of 5 variables:
## ..$ V1: Factor w/ 23 levels "","2/LT","BRIG",..: 1 8 1 18 17 17 16 1 14 20 ...
## ..$ V2: Factor w/ 278 levels "","A BANDYOPADHYAY",..: NA NA NA NA 88 NA NA NA NA 139 ...
## ..$ V3: Factor w/ 183 levels "","-","1 DOGRA",..: NA NA NA NA NA NA NA NA NA 183 ...
## ..$ V4: Factor w/ 91 levels "1","10","100",..: NA NA NA NA NA NA NA NA NA 91 ...
## ..$ V5: Factor w/ 16 levels "A","B","C","D",..: NA NA NA NA NA NA NA NA NA 16 ...
## $ NULL : NULL
## $ NULL :'data.frame': 295 obs. of 5 variables:
## ..$ V1: Factor w/ 21 levels "","2/LT","BRIG",..: 16 15 1 13 18 10 11 4 9 9 ...
## ..$ V2: Factor w/ 278 levels "","A BANDYOPADHYAY",..: NA NA NA NA 139 199 165 148 87 163 ...
## ..$ V3: Factor w/ 183 levels "","-","1 DOGRA",..: NA NA NA NA 183 138 104 112 179 179 ...
## ..$ V4: Factor w/ 91 levels "1","10","100",..: NA NA NA NA 91 18 49 2 9 10 ...
## ..$ V5: Factor w/ 16 levels "A","B","C","D",..: NA NA NA NA 16 1 1 1 1 1 ...
## $ NULL :'data.frame': 284 obs. of 5 variables:
## ..$ V1: Factor w/ 21 levels "","2/LT","BRIG",..: 16 15 1 13 18 10 11 4 9 9 ...
## ..$ V2: Factor w/ 276 levels "A BANDYOPADHYAY",..: NA NA NA NA 137 197 163 146 86 161 ...
## ..$ V3: Factor w/ 182 levels "-","1 DOGRA",..: NA NA NA NA 182 137 103 111 178 178 ...
## ..$ V4: Factor w/ 91 levels "1","10","100",..: NA NA NA NA 91 18 49 2 9 10 ...
## ..$ V5: Factor w/ 16 levels "A","B","C","D",..: NA NA NA NA 16 1 1 1 1 1 ...
## $ NULL :'data.frame': 1 obs. of 1 variable:
## ..$ Our Heroes: Factor w/ 1 level "": 1
## $ martyrs: NULL
## $ martyrs:'data.frame': 272 obs. of 5 variables:
## ..$ RANK : Factor w/ 15 levels "2/LT","BRIG",..: 9 10 3 8 8 6 13 1 1 1 ...
## ..$ NAME : Factor w/ 270 levels "A BANDYOPADHYAY",..: 192 158 141 85 156 64 181 80 260 135 ...
## ..$ UNIT : Factor w/ 179 levels "-","1 DOGRA",..: 135 103 111 176 176 175 175 130 121 162 ...
## ..$ COURSE: Factor w/ 90 levels "1","10","100",..: 18 49 2 9 10 12 13 16 16 17 ...
## ..$ SQN : Factor w/ 15 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ martyrs: NULL
## $ martyrs:'data.frame': 5 obs. of 5 variables:
## ..$ RANK : Factor w/ 4 levels "CAPT","FLG OFFR",..: 2 1 1 3 4
## ..$ NAME : Factor w/ 5 levels "GR SINGH","JS MALIK",..: 5 3 1 4 2
## ..$ UNIT : Factor w/ 4 levels "5 ASSAM","5 SIKH",..: 4 2 1 4 3
## ..$ COURSE: Factor w/ 5 levels "23","26","28",..: 3 4 5 2 1
## ..$ SQN : Factor w/ 5 levels "D","H","J","K",..: 1 2 3 4 5
## $ NULL :'data.frame': 10 obs. of 3 variables:
## ..$ V1: Factor w/ 1 level "": 1 1 1 1 1 1 1 1 1 1
## ..$ V2: Factor w/ 2 levels "","JOINING INSTRUCTIONS - 136TH COURSE\r\n \r\n SUO MOTU DISCLOSURE UNDER"| __truncated__: 1 1 1 1 1 1 2 1 2 1
## ..$ V3: Factor w/ 1 level "": NA NA NA NA NA NA NA NA NA 1
## $ NULL :'data.frame': 1 obs. of 2 variables:
## ..$ : Factor w/ 1 level "": 1
## ..$ : Factor w/ 1 level "JOINING INSTRUCTIONS - 136TH COURSE\r\n \r\n SUO MOTU DISCLOSURE UNDER"| __truncated__: 1
## $ NULL :'data.frame': 1 obs. of 1 variable:
## ..$ V1: Factor w/ 1 level "Designed & Developed by National Defence Academy. Site hosted by NIC. \r\n Copyright 2014 National Defence A"| __truncated__: 1names(tables)## [1] "NULL" "NULL" "NULL" "NULL" "NULL" "NULL" "martyrs"
## [8] "martyrs" "martyrs" "martyrs" "NULL" "NULL" "NULL"tables[8]## $martyrs
## RANK NAME
## 1 LT COL RP SINGH
## 2 MAJ PS RAMAN
## 3 CAPT NM CHADHA
## 4 LT CDR JOGINDER KRISHNA PURI
## 5 LT CDR PRABHAT KUMAR
## 6 FLT LT GS AHUJA
## 7 SQN LDR RC SACHDEV
## 8 2/LT IM KHAN
## 9 2/LT VN ATHALE
## 10 2/LT NC KOHLI
## 11 COL SSS RAMAN
## 12 CAPT MANJEET SINGH
## 13 CAPT PK JOHRI
## 14 MAJ SP SINGH, SM
## 15 2/LT VR DUBAL
## 16 BRIG BS SHERGIL
## 17 CAPT YV PRASAD
## 18 MAJ MANWINDRA SINGH
## 19 MAJ A MATHUR
## 20 CAPT R SUBRAMANIAM
## 21 SQN LDR PK CHHIKARA
## 22 CAPT AMIT SEMVAL
## 23 CAPT KHAWAR SAEED
## 24 CAPT SS MALIK
## 25 FLT LT AMIT SINGH
## 26 LT COL ONKAR SINGH
## 27 SQN LDR NK MALIK
## 28 MAJ YOGENDRA TANDON
## 29 MAJ SM DEV
## 30 CAPT GS SALARIA, PVC (Posthumous)
## 31 MAJ SM SHARMA, Vr C (Posthumous)
## 32 MAJ RANVEER SINGH
## 33 MAJ VR CHOUDHARY
## 34 LT MANVEER SINGH
## 35 MAJ GC VERMA
## 36 MAJ BS RANDHAWA
## 37 2/LT S DAGAR
## 38 MAJ BS MEHTA
## 39 MAJ BHAGAT SINGH
## 40 LT SH KUNDANMAL
## 41 2/LT MARINDER SINGH
## 42 2/LT GK MOHINDRA
## 43 2/LT DK DAS
## 44 2/LT KJ SINGH
## 45 SQN LDR R PUNDIR
## 46 MAJ PR TATHAWADE
## 47 MAJ AJAY PAL SINGH
## 48 2/LT RAKESH SINGH, AC (Posthumous)
## 49 MAJ SJ SINGH
## 50 CAPT VK SINGH
## 51 CAPT GURJINDER SINGH SURI
## 52 CAPT NITIN CHAVHAN
## 53 CAPT JAGDISH CHANDRA
## 54 CAPT AMARJIT SINGH
## 55 LT COL RK DIWAN
## 56 CAPT DPS DUTTA
## 57 LT MR PALTA
## 58 CAPT JS MALIA
## 59 CAPT SURJIT SINGH
## 60 MAJ SKS WALIA
## 61 FLT LT VV TAMBE
## 62 2/LT RVS RANA
## 63 LT PK GAUR
## 64 LT IGNATIOUS LOPEZ
## 65 LT UMA MAHESHWAR
## 66 LT SEN GUPTA
## 67 2/LT SS GILL
## 68 2/LT UC GUPTA
## 69 2/LT NAVNEET SWARAJ
## 70 MAJ DILIP SINGH
## 71 MAJ MS DAHIYA
## 72 MAJ SUSHIL AIMA
## 73 MAJ M TALWAR
## 74 MAJ SAMEER KOTWAL
## 75 FLT LT V KRISHNAMURTHAY
## 76 CAPT HARI RAJ KUMAR
## 77 MAJ JOJY JOSEPH
## 78 LT ASHOK KUMAR
## 79 FLT LT ANSHUL KHATI
## 80 MAJ J PRATAP
## 81 LT COL MD ANAND
## 82 LT COL VP GHAI
## 83 MAJ MS BAL
## 84 LT COL D NARANG
## 85 LT COL SS MALIK
## 86 CAPT KL SHUKLA
## 87 FLT LT VM JOSHI
## 88 2/LT VK GOSWAMI
## 89 2/LT KS GOPAL
## 90 LT CHARANJEET SINGH
## 91 LT COL R VISHWANATHAN
## 92 CAPT RAVINDER SINGH MADAR
## 93 SQN LDR AJAY AHUJA
## 94 2/LT RAJESH KUMAR
## 95 COL NEERAJ SOOD
## 96 MAJ NAVNEET VATS
## 97 FLT LT SR KAGDI
## 98 CAPT ADITYA MISHRA
## 99 CAPT RAMESH CHANDER
## 100 MAJ P SHYAM SUNDER
## 101 CAPT ATUL SOMRA
## 102 CAPT BADAL SINGH SIKARWAR
## 103 MAJ SR MANDKE
## 104 LT BI GOSWAMI
## 105 LT DS BARAR
## 106 2/LT CS DHILLON
## 107 2/LT CHATRAPATI SINGH
## 108 2/LT ROMESH PURI
## 109 MAJ DS PANNU
## 110 2/LT HARJEET SINGH
## 111 CAPT DALJINDER SINGH
## 112 MAJ RANJIT MUTHANNA
## 113 COL JAI PRAKASH
## 114 CAPT SUNIL CHANDRA
## 115 LT COL SCHNJ RAJA
## 116 MAJ SS SHARMA
## 117 MAJ ROHIT DUTT
## 118 COL GS SARNA, KC
## 119 CAPT ARUN SINGH JASROTIA, AC (Posthumous), SM
## 120 2/LT PUNEET NATH DUTT, AC (Posthumous)
## 121 CAPT ANUJ NAYYAR
## 122 CAPT KC DHARASHIVKAR
## 123 CAPT AMIT KUMAR CHANDAN
## 124 MAJ PUNEET KAROL
## 125 LT RANJEET SINGH
## 126 LT CDR RAJAT KUMAR SEN
## 127 FLT LT TK CHAUDHARY
## 128 CAPT GP BHATNAGAR
## 129 CAPT AK DUTT
## 130 LT SC CHAWLA
## 131 2/LT GURUCHARAN SINGH
## 132 MAJ MANMOHAN SINGH BAJAJ, SC
## 133 CAPT HC GUJRAL
## 134 FLT LT HARBIR SINGH SHIROHI
## 135 MAJ RD VATS
## 136 FLT LT RB MEHTA
## 137 CAPT KS CHILLAR
## 138 MAJ RK ARORA
## 139 2/LT KS PIRHAR
## 140 LT RAVI LARORIYA
## 141 MAJ YASHJIT SEHGAL
## 142 FLT LT PV APTE
## 143 CAPT RM GUPTA
## 144 CAPT MS DUGGAL
## 145 CAPT MS PATHANIA
## 146 2/LT VP SINGH, VrC
## 147 COL DJ SINGH
## 148 2/LT ARUN KHETARPAL, PVC (Posthumous)
## 149 MAJ NJD SINGH
## 150 CAPT ARUN SHANKAR KURUR
## 151 LT COL MAHAVIR SINGH
## 152 MAJOR GURPREET SINGH
## 153 CAPT RAJESH GARG
## 154 WG CDR RS DHALIVAL, VM
## 155 MAJ MH PITAMBARE
## 156 CAPT JITESH BHUTANI
## 157 MAJ N JAWAHAR REDDY
## 158 CAPT RVR REDDY
## 159 MAJ RUSHIKESH BB RAMANI
## 160 CAPT RAJINDER SINGH
## 161 CAPT BB GHOSH
## 162 MAJ CHAMAN LAL
## 163 FLT LT S BHARDWAJ
## 164 2/LT AMIR LAKHAN PAL
## 165 CAPT RC BAKSHI
## 166 MAJ AK KANNAL
## 167 MAJ JVS MAKIN
## 168 CAPT DS AHLAWAT
## 169 2/LT M GHANEKAR
## 170 2/LT B MASSAND
## 171 2/LT KR BHADBHADE
## 172 COL PB GOLE
## 173 LT COL AJIT BHANDARKAR
## 174 MAJ SANJAY SOOD
## 175 SQN LDR SANDEEP JAIN
## 176 CAPT RAVINDRA CHIKKARA
## 177 CAPT PANKAJ SHARMA
## 178 LT GAUTAM JAIN
## 179 LT SS DHINDSA
## 180 CAPT HARSHAN R, AC (Posthumous)
## 181 FLT LT AB KOLHE
## 182 CAPT DS JASWAL
## 183 MAJ KS RANA
## 184 MAJ KR PURI
## 185 LT COL IBS BAWA
## 186 CAPT SP DHINGRA
## 187 2/LT SK JASWAL
## 188 LT COL AS SEKHON
## 189 2/LT MPS CHOUDHARY
## 190 MAJ SM BHATT
## 191 CAPT PRAMOD JOLLY
## 192 SUB LT SARAN SURENDRA
## 193 FLT LT S SINGH
## 194 FLG OFFR UP BHAGWAT
## 195 FLT LT M TRIKHA
## 196 MAJ MUKESH CHAURASIA
## 197 CAPT A BANDYOPADHYAY
## 198 CAPT MMS GAMBHIR
## 199 LT CDR ASHOK RAI
## 200 CAPT G MUBAI
## 201 2/LT PK UKPPAL
## 202 CAPT KS RATHEE
## 203 CAPT U RAMDAS
## 204 FLG OFFR PK SAHU
## 205 COL NJ NAIR, AC (Posthumous), KC
## 206 MAJ SANTANU NAG
## 207 MAJ U JAITLY
## 208 CAPT DHANWANT SHARMA
## 209 CAPT S BURMAN
## 210 MAJ DEEPAK RAWAT
## 211 MAJ AS BHADURIA
## 212 CAPT AMARDEEP SINGH SARA
## 213 MAJ MOHIT SHARMA, AC (Posthumous), SM
## 214 PLT OFFR RB UMRALKAR
## 215 MAJ RK CHATURVEDI
## 216 MAJ MANOJ KUMAR
## 217 CAPT N MURLIDHARAN
## 218 2/LT RAMESH RAWAT
## 219 MAJ SUDHIR KUMAR WALIA, AC (Posthumous), SM*
## 220 MAJ AK TRIPATHI
## 221 MAJ MILTON BOBAN KURIAN
## 222 WG CDR D BHATIA
## 223 MAJ YOGESH GUPTA
## 224 CAPT KEVIN KUMAR
## 225 LT AMIT SINGH
## 226 CAPT GAUTAM SHARMA
## 227 CAPT KS MANN
## 228 LT VIKRAM SINGH
## 229 2/LT RPS KALRA
## 230 MAJ SATPRAKASH VERMA
## 231 CAPT SPS SEKHON
## 232 2/LT V KAYAST
## 233 MAJ LM BHATIA
## 234 2/LT VP SAPATNEKAR
## 235 CAPT HK MEHTA
## 236 2/LT N SHISODIA
## 237 2/LT SK VASHIST
## 238 CAPT SANJAY DOGRA
## 239 MAJ RK JOON, AC (Posthumous), SC
## 240 MAJ VIVEK GUPTA
## 241 CAPT SHAILESH RIALACH
## 242 CAPT SUMEET ROY
## 243 MAJ SAMRAT MAITI
## 244 LT PANKAJ JUYAL
## 245 FLT LT A SHARMA, VSM
## 246 FLT LT TS CHAVAN
## 247 MAJ SURINDER KUMAR
## 248 LT PAWAN KUMAR SINGH
## 249 2/LT LS MODI
## 250 2/LT IK GUPTA
## 251 CAPT PN PATHE
## 252 2/LT KAMAL GAMBHIR
## 253 LT AG PATIL
## 254 2/LT RM NARESH
## 255 MAJ SANJAY LOHCHAB
## 256 SQN LDR SANJAY BHARDWAJ
## 257 MAJ LALSON VARGHESH
## 258 LT HEMANT SINGH
## 259 MAJ SALAMAN AHMAD KHAN
## 260 CAPT VK RANA
## 261 SQN LDR KR MURTHY
## 262 FLT LT ABHIJIT GADGIL
## 263 CAPT MANOJ KUMAR PANDEY, PVC (Posthumous)
## 264 CAPT SOORAJ SHARMA
## 265 CAPT NS SIDHU
## 266 SQN LDR S BASU, SC
## 267 LT ATUL KATARIA, SM (Posthumous)
## 268 LT CDR SAURABH SAXENA
## 269 FLT LT RK SERRAO
## 270 CAPT AMOL KALIA
## 271 MAJ SANDEEP UNNIKRISHNAN, AC (Posthumous)
## 272 <NA> <NA>
## UNIT COURSE SQN
## 1 7 PARA 2 A
## 2 4 SIKH LI 6 A
## 3 5 FD REGT 10 A
## 4 IN 11 A
## 5 IN 12 A
## 6 IAF 14 A
## 7 IAF 15 A
## 8 65 FD COY 18 A
## 9 52 PARA FD BTY 18 A
## 10 9 PUNJAB 19 A
## 11 340INF BDR 25 A
## 12 35 LT REGT 28 A
## 13 4/5 GR 34 A
## 14 5/5 GR (FF) 34 A
## 15 4 PARA 37 A
## 16 HQ 7 SEC RR 39 A
## 17 1 PARA (SF) 56 A
## 18 28 RR 73 A
## 19 ENGINEERS 77 A
## 20 1 PARA (SF) 87 A
## 21 IAF 88 A
## 22 16 DOGRA 91 A
## 23 7 KUMAON 96 A
## 24 10 PARA (SF) 99 A
## 25 IAF 99 A
## 26 10 GARH RIF 1 B
## 27 IAF 2 B
## 28 4 RAJPUT 4 B
## 29 85 LT REGT 7 B
## 30 3/1 GR 10 B
## 31 8 JAK RIF 11 B
## 32 19 PUNJAB 13 B
## 33 9 ENGR REGT 13 B
## 34 4 INF DIV 16 B
## 35 3 DOGRA 17 B
## 36 4 RAJPUT 18 B
## 37 1 SIKH 19 B
## 38 70 ARMD REGT 19 B
## 39 6 GUARDS 20 B
## 40 IN 22 B
## 41 14 FD REGT 25 B
## 42 6/5 GR 26 B
## 43 12 KUMAON 37 B
## 44 14 HORSE 37 B
## 45 IAF 62 B
## 46 8 JAK LI 64 B
## 47 3R O FLT 74 B
## 48 22 GRANADIERS 79 B
## 49 12 DOGRA 79 B
## 50 ARTY 84 B
## 51 12 BIHAR 90 B
## 52 115 ENGR REGT 92 B
## 53 18 FD COY 4 C
## 54 5 GUARDS 7 C
## 55 70 ARMD REGT 8 C
## 56 3 JAK RIF 14 C
## 57 4 SIKH 15 C
## 58 3 GARH RIF 16 C
## 59 7 SIKH 21 C
## 60 82 LT REGT 22 C
## 61 IAF 22 C
## 62 19 MARATHA LI 24 C
## 63 ARTY 25 C
## 64 - 33 C
## 65 - 33 C
## 66 - 33 C
## 67 9 HORSE 35 C
## 68 1 JAK RIF 35 C
## 69 39 MED REGT 37 C
## 70 8 ENGR REGT 63 C
## 71 18 MADRAS 66 C
## 72 ADA 73 C
## 73 9 MAHAR 81 C
## 74 21 KUMAON 85 C
## 75 IAF 87 C
## 76 9 GRENADIERS 90 C
## 77 14 MARATHA LI 92 C
## 78 2 DOGRA 96 C
## 79 IAF 108 C
## 80 163 FD REGT 2 D
## 81 67 FD REGT 5 D
## 82 16 MADRAS 5 D
## 83 7 CAVALRY 6 D
## 84 45 CAVALRY 9 D
## 85 9 ENGR REGT 9 D
## 86 4 SIKH LIU 11 D
## 87 IAF 16 D
## 88 4 GARH RIF 17 D
## 89 4/8 GR 25 D
## 90 ARTY 28 D
## 91 18 GRENADIERS 58 D
## 92 5 RAJ RIF 60 D
## 93 IAF 66 D
## 94 14 JAT 73 D
## 95 8 RAJ RIF 81 D
## 96 92 RR 85 D
## 97 IAF 87 D
## 98 SINGALS 88 D
## 99 14 RAJPUT 90 D
## 100 38 RR (10 MADRAS) 90 D
## 101 77 MED REGT 93 D
## 102 80 FD REGT 95 D
## 103 1/8 GR 7 E
## 104 22 MTN REGT 15 E
## 105 4 RAJPUT 15 E
## 106 22 MTN REGT 16 E
## 107 4 RAJPUT 17 E
## 108 5 FD REGT 19 E
## 109 5 SIKH 20 E
## 110 10 MAHAR 24 E
## 111 9 HORSE 28 E
## 112 5 RAJPUT 41 E
## 113 120 INF BN (BIHAR) 49 E
## 114 8 MAHAR 63 E
## 115 298 FD REGT 64 E
## 116 10 SIKH LI 69 E
## 117 513 AD REGT 71 E
## 118 9 GRENADIERS 71 E
## 119 9 PARA (SF) 73 E
## 120 1/11 GR 87 E
## 121 17 JAT 90 E
## 122 8 MADRAS 90 E
## 123 21 GRENADIERS 91 E
## 124 8 R&O FLT ARTY 94 E
## 125 14 SIKH 98 E
## 126 IN 9 F
## 127 IAF 11 F
## 128 52 PARA FD BTY 12 F
## 129 6 FD REGT 13 F
## 130 52 PARA FD BTY 15 F
## 131 5 FD REGT 16 F
## 132 - 17 F
## 133 5 JAT 19 F
## 134 IAF 19 F
## 135 3 JAT 21 F
## 136 IAF 22 F
## 137 8 GRENADIERS 23 F
## 138 9 PUNJAB 23 F
## 139 2 RAJ RIF 24 F
## 140 ARTY 25 F
## 141 15 DOGRA 27 F
## 142 IAF 27 F
## 143 9 EMGR REGT 28 F
## 144 93 MTN REGT 29 F
## 145 5/11 GR 29 F
## 146 5/5 GR (FF) 35 F
## 147 1/5 GR 37 F
## 148 17 HORSE 38 F
## 149 4/5 GR 50 F
## 150 19 GARH RIF 65 F
## 151 35 AR 68 F
## 152 11 RR 76 F
## 153 ARMY AVN 81 F
## 154 IAF 86 F
## 155 3 PARA (SF) 88 F
## 156 5 ARMD REGT 89 F
## 157 2/8 GR 90 F
## 158 69 FD REGT 93 F
## 159 23 PUNJAB 106 F
## 160 6/8 GR 10 G
## 161 2/8 GR 11 G
## 162 99 MTN REGT 15 G
## 163 IAF 18 G
## 164 1 MADRAS 19 G
## 165 6/8 GR 19 G
## 166 4 PARA 21 G
## 167 5 ASSAM 22 G
## 168 10 DOGRA 31 G
## 169 5 MARATHA LI 35 G
## 170 7 PARA 35 G
## 171 71 ARMD REGT 35 G
## 172 9 JAT 52 G
## 173 25 RR 59 G
## 174 21 RR 72 G
## 175 IAF 72 G
## 176 6 GRENADIERS 93 G
## 177 14 PUNJAB 94 G
## 178 178 FD REGT 96 G
## 179 19 PUNJAB 97 G
## 180 2 PARA (SF) 101 G
## 181 IAF 103 G
## 182 7 CAVALRY 17 H
## 183 15 RAJPUT 23 H
## 184 4/5 GR 25 H
## 185 4/5 GR 30 H
## 186 106 ENGR REGT 31 H
## 187 1 DOGRA 35 H
## 188 7 MADRAS 35 H
## 189 10 BIHAR 39 H
## 190 102 BDE 42 H
## 191 12 JAT 51 H
## 192 IN 75 H
## 193 IAF 84 H
## 194 IAF 86 H
## 195 IAF 88 H
## 196 INT CORPS 91 H
## 197 1/4 GR 95 H
## 198 1/9 GR 10 I
## 199 IN 16 I
## 200 2 DOGRA 23 I
## 201 2 DOGRA 24 I
## 202 8 JAT 33 I
## 203 7 PARA 33 I
## 204 IAF 33 I
## 205 16 MARATHA LI 38 I
## 206 2/1 GR 69 I
## 207 3/11 GR 71 I
## 208 299 FD REGT 79 I
## 209 ARTY 82 I
## 210 ARMY AVN 83 I
## 211 18 MADRAS 84 I
## 212 2 ENGR REGT 90 I
## 213 1 PARA (SF) 95 I
## 214 IAF 36 J
## 215 5 RAJ RIF 50 J
## 216 11 MADRAS 56 J
## 217 5 MARATHA LI 58 J
## 218 1/11 GR 70 J
## 219 9 PARA (SF) 72 J
## 220 19 SIKH 77 J
## 221 5 SIKH 80 J
## 222 IAF 81 J
## 223 25 RR 90 J
## 224 9 PARA (SF) 93 J
## 225 1/9 GR 96 J
## 226 8 JAK LI 104 J
## 227 62 INF BDE SIG COY 10 K
## 228 6 KUMAON 15 K
## 229 6 FD REGT 19 K
## 230 3/8 GR 22 K
## 231 2 RAJ RIF 22 K
## 232 16 CAVALRY 25 K
## 233 15 RAJPUT 25 K
## 234 ENGRS 29 K
## 235 3/4 GR 33 K
## 236 3/4 GR 37 K
## 237 58 ENGR REGT 38 K
## 238 1 MARATHA LI 69 K
## 239 22 GRANADIERS 78 K
## 240 2 RAJ RIF 80 K
## 241 EME 91 K
## 242 18 GARH RIF 93 K
## 243 1 NAGA 95 K
## 244 201 ENGR REGT 98 K
## 245 IAF 99 K
## 246 IAF 100 K
## 247 69 FD REGT 10 L
## 248 3/9 GR 23 L
## 249 40 MED REGT 25 L
## 250 40 MED REGT 25 L
## 251 3 PARA 33 L
## 252 19 MARATHA LI 38 L
## 253 IN 38 L
## 254 9 JAT 39 L
## 255 2/3 GR 71 L
## 256 IAF 75 L
## 257 14 RR BN 88 L
## 258 17 GUARDS 94 L
## 259 INF 6 RR 4 SKH 94 L
## 260 9 HORSE 82 M
## 261 IAF 86 M
## 262 IAF 89 M
## 263 1/11 GR 90 M
## 264 2 PARA 92 M
## 265 27 RR 95 M
## 266 IAF 84 N
## 267 13 PUNJAB 88 N
## 268 IN 92 N
## 269 IAF 100 N
## 270 12 JAK LI 87 O
## 271 7 BIHAR 94 O
## 272 <NA> <NA> <NA>martyrs=tables[8]$martyrs
str(martyrs)## 'data.frame': 272 obs. of 5 variables:
## $ RANK : Factor w/ 15 levels "2/LT","BRIG",..: 9 10 3 8 8 6 13 1 1 1 ...
## $ NAME : Factor w/ 270 levels "A BANDYOPADHYAY",..: 192 158 141 85 156 64 181 80 260 135 ...
## $ UNIT : Factor w/ 179 levels "-","1 DOGRA",..: 135 103 111 176 176 175 175 130 121 162 ...
## $ COURSE: Factor w/ 90 levels "1","10","100",..: 18 49 2 9 10 12 13 16 16 17 ...
## $ SQN : Factor w/ 15 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...attach(martyrs)
table(RANK)## RANK
## 2/LT BRIG CAPT COL FLG OFFR FLT LT LT LT CDR
## 43 1 74 7 2 19 23 5
## LT COL MAJ MAJOR PLT OFFR SQN LDR SUB LT WG CDR
## 13 70 1 1 9 1 2library(RColorBrewer)
table(RANK)## RANK
## 2/LT BRIG CAPT COL FLG OFFR FLT LT LT LT CDR
## 43 1 74 7 2 19 23 5
## LT COL MAJ MAJOR PLT OFFR SQN LDR SUB LT WG CDR
## 13 70 1 1 9 1 2hist(table(RANK),col = brewer.pal(9,"Oranges"),breaks = 15,xlab = RANK)
table(COURSE)## COURSE
## 1 10 100 101 103 104 106 108 11 12 13 14 15 16 17 18 19 2
## 1 6 2 1 1 1 1 1 5 2 3 2 7 6 5 4 9 3
## 20 21 22 23 24 25 26 27 28 29 30 31 33 34 35 36 37 38
## 2 3 7 5 4 10 1 2 4 3 1 2 8 2 8 1 6 5
## 39 4 41 42 49 5 50 51 52 56 58 59 6 60 62 63 64 65
## 3 2 1 1 1 2 2 1 1 2 2 1 2 1 1 2 2 1
## 66 68 69 7 70 71 72 73 74 75 76 77 78 79 8 80 81 82
## 2 1 3 3 1 4 3 4 1 2 1 2 1 3 1 2 4 2
## 83 84 85 86 87 88 89 9 90 91 92 93 94 95 96 97 98 99
## 1 4 2 3 5 6 2 3 10 4 4 5 5 5 4 1 2 3hist(table(COURSE),col = brewer.pal(9,"Oranges"),breaks = 15,xlab = RANK)
Data Analytics post Demonetization in India
The demonetisation of ₹500 and ₹1000 banknotes was a policy enacted by the Government of India on 8 November 2016.
The announcement was made by the Prime Minister Narendra Modi .PM Modi declared that use of all ₹500 and ₹1000 banknotes would be invalid from midnight and announced the issuance of new ₹500 and ₹2000 banknotes in exchange for the old banknotes.
The government claimed that the demonetisation was an effort to stop counterfeiting of the current banknotes allegedly used for funding terrorism, as well as a crack down on black money in the country. The move was described as an effort to reduce corruption, the use of drugs, and smuggling.
(source – https://en.wikipedia.org/wiki/Indian_500_and_1000_rupee_note_demonetisation )
This led to huge lines of people outside banks and ATMs to withdraw new notes for daily needs and depositing cash to exchange notes f older denominations.
This also leads to a huge data analytics opportunity for data science to serve treasury and tax departments of India. The following data points would be of particular scrutiny for Indian data scientists helping or om contract to Indian Govt.
- Fraud– This would examine data points where inactive and dormant bank accounts suddenly had a huge inflow of cash. This data would be further matched and merged with income tax records using PAN CARD as a matching and AADHAR CARD too. Additional matching keys would be Name, Date of Birth, Address
- Terrorism – Terrorism in India is specific to a few geographic areas like Jammu and Kashmir and Naxalite areas. These could be further analyzed for fine tume of unusual currency patterns
- Cashless modes for laundering money ( Anti Money Laundering)- Plastic Money and Mobile apps saw a huge upsurge for transactions. This could be further used for additional sources of information since KYC norms of Telecom need Identification and so do Bank Accounts.
- Specific sectors- Land (real estate), Jewelery and other high value, high ticket items can be scrutinized
Overall data will be huge, so choosing the right database combination as well as the analytic (including especialy Big Data Spatial Analytics) could be key to help the current PM ‘s ambitious vision to transform India’s economy.
Comments are welcome.
Internships for Data Scientists at DecisionStats in New Delhi India
http://www.letsintern.com/internship/IT-internships/Decisionstats/Data-Science-Interns/68307
It’s your chance to work in the field of Data Science Interns with Decisionstats for IT Internship.
About the Internship: They are unpaid.
The data scientists will create , edit and make data science research and assist in writing.
The intern will be given on the job training for data science and analytics in Python SAS and R .
The data science intern will create , edit and make schedules and assist in coordination.
The data science intern will be given on the job training for managing in a start up environment, web analytics and search engine marketing as well as an understanding of digital business.
The data science intern will also proof read, edit and write content including blog posts and social media.
The data science intern will be given on the job training for social media, web analytics and search engine optimization as well as an understanding of digital business.
Number of Internships available: 5
Perks:
Certificate, Letter of recommendation, Flexible work hours, Informal dress code, 5 days a week.
Who can apply:
Only those candidates can apply who are available for full time (in-office) internship. They can start the internship between 30th Sep’16 and 30th Oct’16.
1. are available for minimum 2 months duration.
2. are living or staying in Delhi.
3. are pursuing any degree but have relevant skills and interest.
4. are currently in any year of study or are recent graduates.
International Students can also apply
Interview Kiran Rama India’s Number One Data Scientist
Here is an interview with Kiran Rama. He is currently Director, Data Sciences & Advanced Analytics at VMWare. I have chosen Kiran as India’s number one data scientist for the following reasons
- He has both an impeccable academic record as well as steady work experience across multiple companies
- He has demonstrated his expertise in competitions like Kaggle and KDD cup (which is tougher)
- He spends more time doing and expanding data science in India
Here is the interview with Kiran Rama, India’s Number One Data Scientist as per 2016 as per Decisionstats.com
Ajay- Describe your career as a data scientist from corporate job, entrepreneurial work experience, winning competitions, patents and finally back to industry
Kiran- I always had a flair for programming being a computer science engineer and winning inter-college on-the-spot programming and debugging fests. Post computer science engineering graduation, my interesting work was at Motorola using C/Linux for developing features for protocol of Multimedia Messaging Service on several mobile phones. I also owned protocol analytics that involved debugging log files looking for null pointer errors!
Post a couple of years of work, I pursued my post-graduation in management from Indian Institute of Management Kozhikode (IIMK) where I finished in the top 5 of the batch majoring in Information Technology & Systems. I was confused what to do after engineering getting 99 percentile+ in both CAT and GATE. I did not want to stop programming or lose my technical orientation and therefore a role in analytics/data sciences seemed like a natural fit as it involved both technical and business stuff.
I was one of the first hires of the e-business analytics team in Dell in 2006. I got certified in Base SAS and SAS Enterprise Miner and used SAS primarily for data sciences while I used Omniture tools, Excel, SQL for analytics. At Dell Global Analytics, I took on diverse responsibilities and grew from the equivalent of a senior analyst to a Senior Analytics Manager. I touched all parts of e-commerce and e-business.
Some of my achievements in Dell included:
- “2012 India Innovator of the Year” Award from Michael Dell
- 3 patents filed at US PTO on various aspects of e-commerce and marketing analytics
- World Quality Day Finalist in 2010
- Won the Best Project Award in Global Consumer & Small Business Analytics for 4 consecutive quarters
While at Dell, in 2010 or so, knowing SAS well I was frustrated that I could not freelance using SAS owing to the high cost. At that time I picked up R and it is the best decision I made in my career as I took to R like a fish to water owing to it’s many similarities with C.
At Dell, I started participating in data mining competitions on Kaggle.com and had several top finishes. I was a “Master Data Miner” on kaggle. I had great results in the Amazon Employee Access Challenge, Merck Competition to predict Molecular activity, GEFcom competition on load forecasting, on wind forecasting….etc. My Kaggle pursuits was one of the reason I was recruited by Amazon.
I worked as a Senior Business Analytics Lead at Amazon in their Bangalore office as a Level-6 individual contributor. Level-6 in Amazon in those days was one of the senior-most individual contributor position that they had in Bangalore in the engineering teams and very difficult to get laterally on the technical side. However the role was not to my liking and I decided to leave to head marketing & Customer analytics at Flipkart.
I freelanced for several US startups as part of part-time proprietorship “Chaotic Experiments”. Some projects included:
- Software Errors: Predict which line in software code is likely to be an error for a US based startup
- Accident evaluation analysis for a US semi sized startup
- Predict which music label to recommend to a startup
- Trying to predict futures prices in the stock market for a US Startup
- HLA Imputation of Genomic data
At Flipkart, I had the good opportunity of leading several data sciences & analytics projects for Flipkart of which the below ones I am proud of:
- Leveraging Data Sciences to come up with customer segments for Flipkart’s digital properties
- Coming up with an email rules engine to determine the best customers to target per category
- Setting up mobile app analytics at Flipkart
I worked closely at Flipkart with the CTO on data scientist hiring and helped in hiring data scientists being the decision maker for the “data sciences depth” round for data scientists at Flipkart
I continued my hand at Kaggle while at Flipkart and at one time for over a year, I was ranked amongst the top 10 data miners in the world on Kaggle. My top rank was 7 out of some 300K data miners competing in data sciences competitions for sport and the icing on the cake came when I finished in the top 3 in KDD Cup 2014 winning the competition to predict which essay was likely to get a donation on donorschoose.org
Post Flipkart, I was hired into VMW, where I play the role of “Director, Data Sciences & Advanced Analytics”. I play dual roles of functional (where I lead the data sciences innovations team for VMW globally working closely with digital analytics, Digital store/e-commerce, Professional Services, Sales, Marketing, Partner, Pricing, Support,… verticals) and dotted-line (where I represent the equivalent of the Enterprise Information Management in India comprising of Master Data Management, Business Intelligence & Advanced Analytics).
At VMW, have had a unique experience driving B2B data sciences with industry leading projects like:
- First ever digital buyer journey data sciences project at VMW
- “Propensity to Buy” models for several products of VMW, for the Technical Account Manager organization,..
- “Propensity to Sell” models for the partner organization of VMW
- “Propensity to Respond” models
- Deployment models
- …
I currently am at VMW and have been here since almost 2.5 years and loving every moment of it.
Ajay- What are the key things you want to say to someone with no work experience and who wants to work as a data scientist ? What would you say to someone who has a few years work experience and wants to switch to data science
Kiran- For both (freshers and experienced), I would say the following are key in the order of priority:
- Debugging Skills: You cannot give up as a data scientist and should be a person who can sit at one place and continuously debug for hours. Data Science techniques usage will involve installations, OS issues, nitty-gritty aspects of the code,… etc
- Programming Skills: You cannot be a data scientist if you cannot program. You need to be good at programming. Comments like code is available on the net and I will copy-paste do not work. I judge a data scientist by different parameters and one of the most important ones is the quality of the code!
- Knowledge of a Programming Language that has a machine learning library (R or Python are an example. R has access to many of the libraries on the CRAN repository while Python has the world beating scikit-learn package)
- Strong understanding of the mathematical and computer science and statistical background of the data sciences techniques behind the techniques
- Ability to translate a business problem into a data sciences problem. This involves key decisions like which is the target, is this a prediction or classification problem, what is the right cross-validation technique, what algorithms to use for data mining, what should be the right evaluation criteria, how the model will likely be deployed,…
- Strong business/domain understanding can lead to great feature engineering and great success while deployment.
- Ability to present the results to stakeholders and get buy-in for implementation is very important as well
There is a lot of misconception that everyone should do data sciences. Not everyone is suited for this. If you cannot sit in a place for a stretch and code for 5-6 hours in R or Python and SQL, this is not the right job for you. If you do, this is the best thing to do.
Ajay- you have used many tools like SAS Python and R. How would advise a new data scientist on which tool to learn and how to structure their tools training
Kiran-I would suggest a new data scientist to use Python > R > SAS mainly because:
- Python and R have better and wider machine learning libraries than SAS
- Most of the academic work and latest advancements are in Python & R
- Python is better than R because there are more things you can do in Python including software development. Trust me – there is no money in machine learning libraries. There is money only in applications and closer you are to software development + machine learning, the better
- Most of the high paying startups and young firms use Python/R and not SAS
- It is easier to learn Python/R and then if you happen to work for an old behemoth that is a SAS shop, pick up SAS as well
- Python/R are actual programming languages and better than SAS. SAS uses macros and not functions. SAS uses proprietary dataset format that is largely inefficient. SAS requires you to know different syntax for different methods and also different types of plots. On the other hand, the interface to call any function in R or Python is the same. Example: predict function in R. Since everything is returned as an object in R & Python it is easier to examine them (contrast looking at the object sub-objects to running multiple commands in SAS to find the output datasets – the infamous “ods trace on” in SAS,………etc)
A good data scientist should know both R & Python but better to start out in one and master for a year.
Great books to learn R and Python are:
- “The Art of R Programming” by Norman Matloff
- “Python for Data Analysis” by Wes Mc Kinney
For Machine learning fundamentals would recommend:
- Learning from Data by Mostafa
- Applied Data Mining by Paolo Giudici
- Machine Learning by Tom Mitchell
Ajay- What are some key best practises you want to tell to people preparing for data science competitions
Kiran- Here is the link to my kaggle interview on winning the KDD 2014 cup: http://blog.kaggle.com/2014/08/05/3rd-place-interview-from-the-kdd-cup-2014/
Here is the link to my code repository for the winning solution in KDD 2014 cup:https://github.com/rkirana/kdd2014
Best practices I suggest are:
- Build your own repository of functions and methods that you can re-use
- Understand what the winners of prior competitions did. For example: my code above
- Keep yourself current with the latest techniques. For example: xgboost
- Choose the right cross-validation technique. Else, you will overfit
- Be paranoid about leakage and look for ways to fix leakage in everything including data preparation, feature engineering and modeling
- Feature Engineering is the key. Even with lesser data, better features will do better than big data
- Try different methods that are varied. Example: one learner can be tree-based, one bagging, one boosting based, one neural network….etc
- Always ensemble. It can give 2-5% lift
Last mile optimization is difficult. While you can get a 0.85 AUC easily, taking it to 0.88 AUC can be an uphill task
Ajay- What are some of the key algorithms that a data scientist should know?
Kiran- Some of the algorithms that one should know are:
- Regularized Logistic Regression (glmnet in R)
- Bagging technique: Random Forest
- Boosting Technique: Gradient Boosting Machine, Extreme Gradient Boosting
- Collaborative Filtering Techniques: LIBFM
- Non linear learners like Neural Networks
- Bayesian Methods like BayesTree, bartMachine
- Support Vector Machines – LIBSVM library
- Fast learners like Vowpal Wabbit
One should always ensemble multiple techniques in order to get better results
Ajay- Describe your favourite online learning resources for learning data science languages, algorithms etc
Kiran- kaggle.com – nothing beats it
cran.r-project.org – all the vignettes there
Ajay- How do you keep yourself updated on data science knowledge
Kiran- I have not participated in data science competitions for last 2 years – participating was a way in which I kept myself and pushed myself to be updated
I am very keen on making some original contributions to data sciences research and teaching. I am pursuing a part time doctoral program (PhD) at IIM Lucknow while I do my full-time job. Spend a lot of time on scholar.google.com these days to understand the existing contributions to data science theory and how I can make original contributions to the same
I also drive industry-academia interaction with the Data Center and Analytics Lab at IIM Bangalore where I represent VMW on the DCAL board. I am at the forefront of organizing industry events on data sciences to share knowledge and learn about the latest in the industry.
I am thankful to my leaders and my direct team at VMW for giving me so many interesting business problems to solve using data sciences and that pushes me and drives me to keep myself updated
About-
Kiran is a Data Sciences Leader with more than 12 years of experience across marketing, digital (web/mobile), retail, pricing, partner, sales. Experience across B2C, e-commerce & B2B data sciences. One of the Top 10 Player on Kaggle – data mining competition platform – in 2013 and half of 2014 world-wide, Kiran is also KDD 2014 Prize Winner and Holder of 3 US patents. 2012 Innovator of the Year award from Michael Dell.
You can read about him here https://www.linkedin.com/in/rkirana
Latest DecisionStats Intern
Congratulations to our latest intern for completing the intensive internship at DecisionStats . See work done by here here-
https://datascience899.wordpress.com/blog/
Her latest blog post tries to use Python to understand police shootings in USA
https://datascience899.wordpress.com/2016/07/23/python-ii/
https://datascience899.wordpress.com/2016/07/23/operations-in-r/
https://datascience899.wordpress.com/2016/07/23/sql-ii/
Previous Interns wrote great Python code and R code
see
https://github.com/decision-stats (Sarah Masud and Farheen)
and
Anshul Gupta
Cricket Analysis – http://nbviewer.ipython.org/gist/anshulkgupta93/39689db8b337c3ccf247
pyCURL- http://nbviewer.ipython.org/gist/anshulkgupta93/a87a0884ada6e9380952
and
Chandan Routray
https://decisionstats.com/2014/08/04/the-first-decisionstats-com-intern/
Some points for future interns at DecisionStats-
- We normally dont pay interns anything
- 80 % interns drop out or are let go because they cannot keep up with the assignments
- Remaining 20% usually learn a lot in the intensive program
- Internships are like a free boot camp
- No more internships till June 2017 because I am trying to write a book
- Some research assistantships might be available in December 2016 to help with some code or Lyx formatting for the former
- See my LinkedIn profile for reviews given by the 20% interns who manage to stick around
- I usually emphasize writing, polyglot tools (both R, SAS and Python) , logical thinking and concise communication for my interns
- I usually treat them as students since I dont work for or in a university. That might change as I try and transition out from business to academic research options for a non Phd
Thanking Yoga
So I made an update on LinkedIn where I am lucky to find 12000 connections to talk about Yoga and get 1100 likes.. Connect with me on LinkedIn here 🙂
https://www.linkedin.com/in/ajayohri
The post
The likes
www.linkedin.com/hp/update/6146066977216610304
The profile views
LOL. Internet works in a strange way. I just wrote it as a thank you note to myself on my 39 birthday.
Related- Inspired by my favourite American hacker
DECISION STATS 