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T unguch
m ic ro dis tr ic t
7 6
•
B is h kek
c ity ,
7 20048
•
b ufa tim a.n urm ah am ad kyzy @ ala to o.e d u.k g
•
+ 996220168921
O bje ctiv e
D ed ic ate d
D ata
S cie n ce
E nth usia st
a n d
M ath
L ectu re r
w ith
a
s tr o ng
f o undatio n
i n
d ata
a n aly sis
a n d
m ach in e
l e arn in g.
S eek in g
a
D ata
S cie n ce
I n te rn sh ip
t o
a p ply
m y
s k ills
a n d
c o ntr ib ute
t o
r e al- w orld
p ro je cts .
E duca tio n
I N TER NAT IO NAL
A LAT O O
U NIV ER SIT Y
B is h kek ,K yrg yzsta n
M aste r’s
d eg re e,
D ata
S cie n ce.
S ep ,
2 023
–
J u n,
2 025
I N TER NAT IO NAL
A LAT O O
U NIV ER SIT Y
B is h kek ,K yrg yzsta n
B ach elo r’s
d eg re e,
A pplie d
M ath em atic s
a n d
I n fo rm atic s.
G PA
- 3 .3 8/4
S ep ,
2 016
–
J u n,
2 020
T hesis :
G en era tin g
m usic
u sin g
N eu ra l
N etw ork s
W ORLD Q UANT
U NIV ER SIT Y
O nlin e
C ertif ic ate ,
A pplie d
D ata
S cie n ce
L ab
F eb ,
2 023–
J u n,
2 023
P ro fic ie n cy
i n
d ata
s c ie n ce
t h ro ugh
e ig ht
p ro je cts :
-
R eal
e sta te
t r e n d
a n aly sis
w ith
2 1,0 00
p ro pertie s,
e x plo rin g
s iz e
v s.
l o catio n
i m pact.
-
A ccu ra te
l in ear
r e g re ssio n
f o r
a p artm en t
p ric in g,
o ptim iz in g
w ith
d ata
h an dlin g.
-
A ir
q uality
p re d ic tio n
v ia
A RM A
t im e-s e rie s
m odels ,
r e fin ed
w ith
M ongoD B.
-
E th ic al
i n sig hts
i n to
e arth quak e
d am ag e
u sin g
r e g re ssio n
a n d
d ecis io n
t r e es.
-
E ff e ctiv e
b an kru ptc y
p re d ic tio n,
a d dre ssin g
d ata
i m bala n ces
a n d
L in ux
n av ig atio n.
-
C usto m er
s e g m en ta tio n
w ith
k -m ean s
a n d
P C A ,
v is u aliz ed
u sin g
P lo tly
D ash .
-
A /B
t e stin g
e x pertis e ,
e m plo yin g
P yth on
c la sse s
a n d
i n te ra ctiv e
a p plic atio ns.
-
G ARCH
m odel
c re atio n
f o r
a sse t
v ola tility ,
s h ow casin g
d ata
a cq uis itio n
s k ills .
A SK
C on su lt in g
E xp erie n ce
B is h kek ,
K yrg yzsta n
S tu dy
A bro ad
A dm is sio n s
C oord in ato r
J a n ,
2 022
–
S ep ,2 022
-
G uid ed
U S
u niv ers ity
a p plic an ts
o n
a d m is sio ns,
f in an cia l
a id ,
a n d
p ro gra m
o ff e rin gs.
-
O ff e re d
p ers o naliz ed
s u pport
t h ro ughout
t h e
a p plic atio n
p ro cess.
-
C olla b ora te d
w ith
U S
u niv ers itie s
f o r
a ccu ra te
a n d
t im ely
p ro cessin g
o f
a p plic atio ns
a n d
d ocu m en ts .
A la -T oo
I n te rn atio n al
U niv ersit y
B is h kek ,
K yrg yzsta n
L ectu re r
M ar,
2 023
–
P re se n t
-
D ev elo p
e n gag in g
l e sso ns
i n
m ath em atic s,
s ta tis tic s,
a n d
d ata
s c ie n ce.
-
G uid e
c la ssro om
d is c u ssio ns
a n d
o ff e r
i n div id ualiz ed
s u pport.
-
T each
c o ncep ts
a n d
p ra ctic al
a p plic atio ns
i n
m ath em atic s
a n d
m ach in e
l e arn in g.

P ro je cts
&
C ertif ic a tio n s
•
C ertif ic a tio n s
(
F or
o th er
r e le v an t
c ertif ic atio ns,
p le ase
r e fe r
t o
m y
L in ked In
) :
-
D ata
S cie n tis t
A sso cia te
C ertif ic ate ,
D ata C am p
-
D ata
S cie n tis t
w ith
P yth on,
D ata C am p
-
P yth on
P ro gra m min g
E sse n tia ls ,
R ic e
U niv ers ity
-
D ata
S cie n ce
V ir tu al
E xperie n ce
P ro gra m ,
F ora g e
-
M ach in e
L earn in g,
S ta n fo rd
U niv ers ity
-
A I
F or
E very one,
D eep L earn in g.A I
-
F undam en ta ls
o f
D ata
S ecu rity ,
e S kills
A cad em y
-
I n tr o ductio n
t o
P ro gra m min g
w ith
M ATLA B,
V an derb ilt
U niv ers ity
-
I n te rm ed ia te
M ach in e
L earn in g,
K ag gle
-
I n tr o ductio n
t o
S ta tis tic s,
S ta n fo rd
U niv ers ity
-
I n tr o ductio n
t o
C alc u lu s,
U niv ers ity
o f
S ydney
-
D ata
S cie n ce
M ath
S kills ,
D uke
U niv ers ity
-
L earn
T ab le au
D esk to p
9
f ro m
S cra tc h
f o r
D ata
S cie n ce,
e S kills
A cad em y
•
P ro je cts
( F or
d eta ile d
p ro je ct
d esc rip tio ns,
p le ase
r e fe r
t o
m y
G itH ub):
1 .
G old
P ric e
V ola tility
F ore castin g
w ith
G ARCH
M odel
2 .
C usto m er
S eg m en ta tio n
A naly sis
3 .
E nhan cin g
S to ck
L ev el
P re d ic tio ns
f o r
G ala
G ro cerie s
4 .
T im e
S erie s
A naly sis
o f
A ir
Q uality
i n
B is h kek
5 .
C usto m er
B ookin g
P re d ic tio n:
B ritis h
A ir w ay s
6 .
C usto m er
C hurn
P re d ic tio n
P ro je ct
7 .
N atu ra l
L an guag e
P ro cessin g
( N LP):
B ritis h
A ir w ay s
R ev ie w s
A naly sis
8 .
W in e
Q uality
C la ssif ic atio n
9 .
R eg re ssio n
A naly sis :
P re d ic tin g
C alif o rn ia
H ousin g
P ric es
1 0.
E xplo ra to ry
D ata
A naly sis
( E D A):
U nco verin g
I n sig hts
i n
T ita n ic
D ata
S kills
-
P ro gra m min g
L an gu ages:
P yth on
( N um Py,
P an das,
s c ik it- le arn ,
T en so rF lo w ,
K era s),
R ,
M ATLA B
-
D ata
A naly sis
a n d
V is u aliz a tio n :
S Q L,
T ab le au
-
M ach in e
L ea rn in g:
S uperv is e d
a n d
U nsu perv is e d
L earn in g
( R eg re ssio n,
C la ssif ic atio n,
C lu ste rin g),
D eep
L earn in g
( N eu ra l
N etw ork s,
C N Ns,
R N Ns)
-
S ta tis tic a l
A naly sis :
H ypoth esis
T estin g
( A /B
T estin g,
S ta tis tic al
S ig nif ic an ce),
T im e
S erie s
A naly sis
a n d
F ore castin g
-
D ata
P re p ro cessin g:
F eatu re
E ngin eerin g,
D ata
W ra n glin g,
D ata
S to ry te llin g,
D ata
S ecu rity ,
D ata
E th ic s
-
P ro b le m
S olv in g
a n d
C rit ic a l
T hin kin g
P ortfo lio
-
L in ked In
P ro file :
h ttp s://w ww.l in ked in .c o m /in /b ufa tim a-n -k /
-
G it H ub
P ro je cts
:
h ttp s://g ith ub.c o m /B ufa tim a-N k/
-
P ortfo lio
W eb sit e
:
h ttp s://b ufa tim a-n k.g ith ub.i o /