├── .gitignore
├── Week 2 Basic Structured Query Language
└── Assignment
│ ├── counting_email_in_a_database.py
│ ├── mbox.txt
│ └── orgs.sqlite
├── Week 3 Data Models and Relational SQL
└── Assignment
│ ├── Library.xml
│ ├── trackdb.sqlite
│ └── tracks.py
├── Week 4 Many-to-Many Relationships
└── Assignment
│ ├── roster.py
│ ├── roster_data.json
│ └── rosterdb.sqlite
└── Week 5 Databases and Visualization
└── Assignment
├── README.txt
├── geodump.py
├── geoload.py
├── where.data
├── where.html
└── where.js
/.gitignore:
--------------------------------------------------------------------------------
1 | .idea
2 |
3 | # Ignore all logfiles and tempfiles.
4 | /log/*.log
5 | /tmp
6 |
7 | tmp/**/*
8 | tmp/*
9 | *.swp
10 | *~
11 | .DS_Store
12 |
--------------------------------------------------------------------------------
/Week 2 Basic Structured Query Language/Assignment/counting_email_in_a_database.py:
--------------------------------------------------------------------------------
1 | import sqlite3
2 |
3 | conn = sqlite3.connect('orgs.sqlite')
4 | cur = conn.cursor()
5 |
6 | cur.execute('DROP TABLE IF EXISTS Counts')
7 |
8 | cur.execute('CREATE TABLE Counts (org TEXT, count INTEGER)')
9 |
10 | fname = input('Enter file name: ')
11 | if len(fname) < 1:
12 | fname = 'mbox.txt'
13 | fh = open(fname)
14 | for line in fh:
15 | if not line.startswith('From: '):
16 | continue
17 | pieces = line.split()
18 | email = pieces[1]
19 | org = email.split('@')[1]
20 | cur.execute('SELECT count FROM Counts WHERE org = ?', (org, ))
21 | try:
22 | count = cur.fetchone()[0]
23 | cur.execute('UPDATE Counts SET count = count + 1 WHERE org = ?', (org, ))
24 | except:
25 | cur.execute('INSERT INTO Counts (org, count) VALUES (?, 1)', (org, ))
26 |
27 | conn.commit()
28 |
29 | sqlstr = 'SELECT org, count FROM Counts ORDER BY count DESC LIMIT 10'
30 | for row in conn.execute(sqlstr):
31 | print(row[0], row[1])
32 |
33 | conn.close()
34 |
--------------------------------------------------------------------------------
/Week 2 Basic Structured Query Language/Assignment/orgs.sqlite:
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https://raw.githubusercontent.com/jeremy-wl/coursera-using-databases-with-python/cd3154f72cfaa8cb6dc12126d48cfa5fad63f9f6/Week 2 Basic Structured Query Language/Assignment/orgs.sqlite
--------------------------------------------------------------------------------
/Week 3 Data Models and Relational SQL/Assignment/trackdb.sqlite:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/jeremy-wl/coursera-using-databases-with-python/cd3154f72cfaa8cb6dc12126d48cfa5fad63f9f6/Week 3 Data Models and Relational SQL/Assignment/trackdb.sqlite
--------------------------------------------------------------------------------
/Week 3 Data Models and Relational SQL/Assignment/tracks.py:
--------------------------------------------------------------------------------
1 | import xml.etree.ElementTree as et
2 | import sqlite3
3 |
4 | conn = sqlite3.connect('trackdb.sqlite')
5 | cur = conn.cursor()
6 |
7 | # Make some fresh tables using executescript()
8 | cur.executescript('''
9 | DROP TABLE IF EXISTS Artist;
10 | DROP TABLE IF EXISTS Genre;
11 | DROP TABLE IF EXISTS Album;
12 | DROP TABLE IF EXISTS Track;
13 |
14 | CREATE TABLE Artist (
15 | id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
16 | name TEXT UNIQUE
17 | );
18 |
19 | CREATE TABLE Genre (
20 | id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
21 | name TEXT UNIQUE
22 | );
23 |
24 | CREATE TABLE Album (
25 | id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
26 | artist_id INTEGER,
27 | title TEXT UNIQUE
28 | );
29 |
30 | CREATE TABLE Track (
31 | id INTEGER NOT NULL PRIMARY KEY
32 | AUTOINCREMENT UNIQUE,
33 | title TEXT UNIQUE,
34 | album_id INTEGER,
35 | genre_id INTEGER,
36 | len INTEGER, rating INTEGER, count INTEGER
37 | );
38 | ''')
39 |
40 |
41 | fname = input('Enter file name: ')
42 | if len(fname) < 1:
43 | fname = 'Library.xml'
44 |
45 | # Track ID369
46 | # NameAnother One Bites The Dust
47 | # ArtistQueen
48 |
49 |
50 | def lookup(dict, key):
51 | found = False
52 | for tag in dict:
53 | if found:
54 | return tag.text
55 | if tag.tag == 'key' and tag.text == key:
56 | found = True
57 | return None
58 |
59 | stuff = et.parse(fname)
60 | all = stuff.findall('dict/dict/dict')
61 | print('Dict count:', len(all))
62 | for entry in all:
63 | if lookup(entry, 'Track ID') is None: continue
64 |
65 | name = lookup(entry, 'Name')
66 | artist = lookup(entry, 'Artist')
67 | album = lookup(entry, 'Album')
68 | count = lookup(entry, 'Play Count')
69 | rating = lookup(entry, 'Rating')
70 | length = lookup(entry, 'Total Time')
71 | genre = lookup(entry, 'Genre')
72 |
73 | if name is None or artist is None or album is None or genre is None:
74 | continue
75 |
76 | print(name, artist, album, count, rating, length)
77 |
78 | cur.execute('''INSERT OR IGNORE INTO Artist (name)
79 | VALUES ( ? )''', (artist, ))
80 | cur.execute('SELECT id FROM Artist WHERE name = ? ', (artist, ))
81 | artist_id = cur.fetchone()[0]
82 |
83 | cur.execute('''INSERT OR IGNORE INTO Album (title, artist_id)
84 | VALUES ( ?, ? )''', (album, artist_id))
85 | cur.execute('SELECT id FROM Album WHERE title = ? ', (album, ))
86 | album_id = cur.fetchone()[0]
87 |
88 | cur.execute('''INSERT OR IGNORE INTO Genre (name)
89 | VALUES ( ? )''', (genre, ))
90 | cur.execute('SELECT id FROM Genre WHERE name = ?', (genre, ))
91 | genre_id = cur.fetchone()[0]
92 |
93 | cur.execute('''INSERT OR REPLACE INTO Track
94 | (title, album_id, genre_id, len, rating, count)
95 | VALUES ( ?, ?, ?, ?, ?, ? )''',
96 | (name, album_id, genre_id, length, rating, count))
97 |
98 | conn.commit()
99 |
--------------------------------------------------------------------------------
/Week 4 Many-to-Many Relationships/Assignment/roster.py:
--------------------------------------------------------------------------------
1 | import json
2 | import sqlite3
3 |
4 | conn = sqlite3.connect('rosterdb.sqlite')
5 | cur = conn.cursor()
6 |
7 | # Do some setup
8 | cur.executescript('''
9 | DROP TABLE IF EXISTS User;
10 | DROP TABLE IF EXISTS Member;
11 | DROP TABLE IF EXISTS Course;
12 |
13 | CREATE TABLE User (
14 | id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
15 | name TEXT UNIQUE
16 | );
17 |
18 | CREATE TABLE Course (
19 | id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT UNIQUE,
20 | title TEXT UNIQUE
21 | );
22 |
23 | CREATE TABLE Member (
24 | user_id INTEGER,
25 | course_id INTEGER,
26 | role INTEGER,
27 | PRIMARY KEY (user_id, course_id)
28 | )
29 | ''')
30 |
31 | fname = input('Enter file name: ')
32 | if len(fname) < 1:
33 | fname = 'roster_data.json'
34 |
35 | # [
36 | # [ "Charley", "si110", 1 ],
37 | # [ "Mea", "si110", 0 ],
38 |
39 | str_data = open(fname).read()
40 | json_data = json.loads(str_data)
41 |
42 | for entry in json_data:
43 |
44 | name = entry[0]
45 | title = entry[1]
46 | role = entry[2]
47 |
48 | print(name, title)
49 |
50 | cur.execute('''INSERT OR IGNORE INTO User (name)
51 | VALUES ( ? )''', (name, ))
52 | cur.execute('SELECT id FROM User WHERE name = ? ', (name, ))
53 | user_id = cur.fetchone()[0]
54 |
55 | cur.execute('''INSERT OR IGNORE INTO Course (title)
56 | VALUES ( ? )''', (title, ))
57 | cur.execute('SELECT id FROM Course WHERE title = ? ', (title, ))
58 | course_id = cur.fetchone()[0]
59 |
60 | cur.execute('''INSERT OR REPLACE INTO Member
61 | (user_id, course_id, role) VALUES ( ?, ?, ? )''',
62 | (user_id, course_id, role))
63 |
64 | conn.commit()
65 |
--------------------------------------------------------------------------------
/Week 4 Many-to-Many Relationships/Assignment/roster_data.json:
--------------------------------------------------------------------------------
1 | [
2 | [
3 | "Aryankhan",
4 | "si110",
5 | 1
6 | ],
7 | [
8 | "Vincenzo",
9 | "si110",
10 | 0
11 | ],
12 | [
13 | "Waqaas",
14 | "si110",
15 | 0
16 | ],
17 | [
18 | "Maximillian",
19 | "si110",
20 | 0
21 | ],
22 | [
23 | "Oluwademilade",
24 | "si110",
25 | 0
26 | ],
27 | [
28 | "Nash",
29 | "si110",
30 | 0
31 | ],
32 | [
33 | "Heidi",
34 | "si110",
35 | 0
36 | ],
37 | [
38 | "Miah",
39 | "si110",
40 | 0
41 | ],
42 | [
43 | "Suzy",
44 | "si110",
45 | 0
46 | ],
47 | [
48 | "Danika",
49 | "si110",
50 | 0
51 | ],
52 | [
53 | "Daniela",
54 | "si110",
55 | 0
56 | ],
57 | [
58 | "Anabelle",
59 | "si110",
60 | 0
61 | ],
62 | [
63 | "Kyren",
64 | "si110",
65 | 0
66 | ],
67 | [
68 | "Sera",
69 | "si110",
70 | 0
71 | ],
72 | [
73 | "Zishan",
74 | "si110",
75 | 0
76 | ],
77 | [
78 | "Kiern",
79 | "si110",
80 | 0
81 | ],
82 | [
83 | "Jeemie",
84 | "si110",
85 | 0
86 | ],
87 | [
88 | "Ege",
89 | "si110",
90 | 0
91 | ],
92 | [
93 | "Benjamyn",
94 | "si110",
95 | 0
96 | ],
97 | [
98 | "Sascha",
99 | "si110",
100 | 0
101 | ],
102 | [
103 | "Rees",
104 | "si110",
105 | 0
106 | ],
107 | [
108 | "Perrie",
109 | "si110",
110 | 0
111 | ],
112 | [
113 | "Aneliese",
114 | "si110",
115 | 0
116 | ],
117 | [
118 | "Tiona",
119 | "si106",
120 | 1
121 | ],
122 | [
123 | "Sheriff",
124 | "si106",
125 | 0
126 | ],
127 | [
128 | "Faizaan",
129 | "si106",
130 | 0
131 | ],
132 | [
133 | "Giada",
134 | "si106",
135 | 0
136 | ],
137 | [
138 | "Adrianna",
139 | "si106",
140 | 0
141 | ],
142 | [
143 | "Vincent",
144 | "si106",
145 | 0
146 | ],
147 | [
148 | "Roxana",
149 | "si106",
150 | 0
151 | ],
152 | [
153 | "Avraham",
154 | "si106",
155 | 0
156 | ],
157 | [
158 | "Taddy",
159 | "si106",
160 | 0
161 | ],
162 | [
163 | "Mhirren",
164 | "si106",
165 | 0
166 | ],
167 | [
168 | "Chintu",
169 | "si106",
170 | 0
171 | ],
172 | [
173 | "Shuni",
174 | "si106",
175 | 0
176 | ],
177 | [
178 | "Doha",
179 | "si106",
180 | 0
181 | ],
182 | [
183 | "Bhaaldeen",
184 | "si106",
185 | 0
186 | ],
187 | [
188 | "Maia",
189 | "si106",
190 | 0
191 | ],
192 | [
193 | "Aliya",
194 | "si106",
195 | 0
196 | ],
197 | [
198 | "Bailey",
199 | "si106",
200 | 0
201 | ],
202 | [
203 | "Chukwuemeka",
204 | "si106",
205 | 0
206 | ],
207 | [
208 | "Alasdair",
209 | "si106",
210 | 0
211 | ],
212 | [
213 | "Roderick",
214 | "si106",
215 | 0
216 | ],
217 | [
218 | "Carlynn",
219 | "si106",
220 | 0
221 | ],
222 | [
223 | "Clementine",
224 | "si106",
225 | 0
226 | ],
227 | [
228 | "Nadine",
229 | "si106",
230 | 0
231 | ],
232 | [
233 | "Calleigh",
234 | "si106",
235 | 0
236 | ],
237 | [
238 | "Wynn",
239 | "si106",
240 | 0
241 | ],
242 | [
243 | "Audrey",
244 | "si106",
245 | 0
246 | ],
247 | [
248 | "Melandra",
249 | "si106",
250 | 0
251 | ],
252 | [
253 | "Aria",
254 | "si106",
255 | 0
256 | ],
257 | [
258 | "Sabriyah",
259 | "si106",
260 | 0
261 | ],
262 | [
263 | "Caitaidh",
264 | "si106",
265 | 0
266 | ],
267 | [
268 | "Burak",
269 | "si106",
270 | 0
271 | ],
272 | [
273 | "Dilan",
274 | "si106",
275 | 0
276 | ],
277 | [
278 | "Zerah",
279 | "si106",
280 | 0
281 | ],
282 | [
283 | "Christy",
284 | "si106",
285 | 0
286 | ],
287 | [
288 | "Gemmalea",
289 | "si106",
290 | 0
291 | ],
292 | [
293 | "Mackenzie",
294 | "si106",
295 | 0
296 | ],
297 | [
298 | "Karley",
299 | "si106",
300 | 0
301 | ],
302 | [
303 | "Dineo",
304 | "si106",
305 | 0
306 | ],
307 | [
308 | "Elidh",
309 | "si106",
310 | 0
311 | ],
312 | [
313 | "Thumbiko",
314 | "si106",
315 | 0
316 | ],
317 | [
318 | "Keiryn",
319 | "si206",
320 | 1
321 | ],
322 | [
323 | "Sam",
324 | "si206",
325 | 0
326 | ],
327 | [
328 | "Deena",
329 | "si206",
330 | 0
331 | ],
332 | [
333 | "Bader",
334 | "si206",
335 | 0
336 | ],
337 | [
338 | "Isaiah",
339 | "si206",
340 | 0
341 | ],
342 | [
343 | "Kurt",
344 | "si206",
345 | 0
346 | ],
347 | [
348 | "Lacy",
349 | "si206",
350 | 0
351 | ],
352 | [
353 | "Laci",
354 | "si206",
355 | 0
356 | ],
357 | [
358 | "Kandice",
359 | "si206",
360 | 0
361 | ],
362 | [
363 | "Kelice",
364 | "si206",
365 | 0
366 | ],
367 | [
368 | "Keryn",
369 | "si206",
370 | 0
371 | ],
372 | [
373 | "Ruta",
374 | "si206",
375 | 0
376 | ],
377 | [
378 | "Abhia",
379 | "si206",
380 | 0
381 | ],
382 | [
383 | "Janey",
384 | "si206",
385 | 0
386 | ],
387 | [
388 | "Adonica",
389 | "si206",
390 | 0
391 | ],
392 | [
393 | "Nathaniel",
394 | "si206",
395 | 0
396 | ],
397 | [
398 | "Gareth",
399 | "si206",
400 | 0
401 | ],
402 | [
403 | "Sevin",
404 | "si206",
405 | 0
406 | ],
407 | [
408 | "Nassir",
409 | "si206",
410 | 0
411 | ],
412 | [
413 | "Chara",
414 | "si206",
415 | 0
416 | ],
417 | [
418 | "Nicole",
419 | "si206",
420 | 0
421 | ],
422 | [
423 | "Aimie",
424 | "si206",
425 | 0
426 | ],
427 | [
428 | "Sinem",
429 | "si206",
430 | 0
431 | ],
432 | [
433 | "Nicky",
434 | "si206",
435 | 0
436 | ],
437 | [
438 | "Rameen",
439 | "si206",
440 | 0
441 | ],
442 | [
443 | "Saffron",
444 | "si206",
445 | 0
446 | ],
447 | [
448 | "Benji",
449 | "si301",
450 | 1
451 | ],
452 | [
453 | "Mirryn",
454 | "si301",
455 | 0
456 | ],
457 | [
458 | "Tira",
459 | "si301",
460 | 0
461 | ],
462 | [
463 | "Cailyn",
464 | "si301",
465 | 0
466 | ],
467 | [
468 | "Eve",
469 | "si301",
470 | 0
471 | ],
472 | [
473 | "Harjyot",
474 | "si301",
475 | 0
476 | ],
477 | [
478 | "Muhammed",
479 | "si301",
480 | 0
481 | ],
482 | [
483 | "Clarisse",
484 | "si301",
485 | 0
486 | ],
487 | [
488 | "Siddhant",
489 | "si301",
490 | 0
491 | ],
492 | [
493 | "Nur",
494 | "si301",
495 | 0
496 | ],
497 | [
498 | "Jorge",
499 | "si301",
500 | 0
501 | ],
502 | [
503 | "Rasmus",
504 | "si301",
505 | 0
506 | ],
507 | [
508 | "Aleena",
509 | "si301",
510 | 0
511 | ],
512 | [
513 | "Almirah",
514 | "si301",
515 | 0
516 | ],
517 | [
518 | "Jeffrey",
519 | "si301",
520 | 0
521 | ],
522 | [
523 | "Maias",
524 | "si301",
525 | 0
526 | ],
527 | [
528 | "Nicki",
529 | "si301",
530 | 0
531 | ],
532 | [
533 | "Muir",
534 | "si301",
535 | 0
536 | ],
537 | [
538 | "Andrew",
539 | "si301",
540 | 0
541 | ],
542 | [
543 | "Annsarai",
544 | "si301",
545 | 0
546 | ],
547 | [
548 | "Elle",
549 | "si301",
550 | 0
551 | ],
552 | [
553 | "Cairn",
554 | "si301",
555 | 0
556 | ],
557 | [
558 | "Shreeram",
559 | "si301",
560 | 0
561 | ],
562 | [
563 | "Kizhi",
564 | "si301",
565 | 0
566 | ],
567 | [
568 | "Russel",
569 | "si301",
570 | 0
571 | ],
572 | [
573 | "Shergo",
574 | "si301",
575 | 0
576 | ],
577 | [
578 | "Deborah",
579 | "si301",
580 | 0
581 | ],
582 | [
583 | "Liam",
584 | "si301",
585 | 0
586 | ],
587 | [
588 | "Kori",
589 | "si301",
590 | 0
591 | ],
592 | [
593 | "Katriona",
594 | "si301",
595 | 0
596 | ],
597 | [
598 | "Keatin",
599 | "si301",
600 | 0
601 | ],
602 | [
603 | "Riana",
604 | "si301",
605 | 0
606 | ],
607 | [
608 | "Zennon",
609 | "si301",
610 | 0
611 | ],
612 | [
613 | "Daksh",
614 | "si301",
615 | 0
616 | ],
617 | [
618 | "Bonni",
619 | "si301",
620 | 0
621 | ],
622 | [
623 | "Miyah",
624 | "si301",
625 | 0
626 | ],
627 | [
628 | "Avah",
629 | "si301",
630 | 0
631 | ],
632 | [
633 | "Terra",
634 | "si301",
635 | 0
636 | ],
637 | [
638 | "Regan",
639 | "si301",
640 | 0
641 | ],
642 | [
643 | "Elli",
644 | "si301",
645 | 0
646 | ],
647 | [
648 | "Jacky",
649 | "si301",
650 | 0
651 | ],
652 | [
653 | "Tiree",
654 | "si301",
655 | 0
656 | ],
657 | [
658 | "Nikhil",
659 | "si310",
660 | 1
661 | ],
662 | [
663 | "Greg",
664 | "si310",
665 | 0
666 | ],
667 | [
668 | "Amaia",
669 | "si310",
670 | 0
671 | ],
672 | [
673 | "Sofian",
674 | "si310",
675 | 0
676 | ],
677 | [
678 | "Jane",
679 | "si310",
680 | 0
681 | ],
682 | [
683 | "Roxanne",
684 | "si310",
685 | 0
686 | ],
687 | [
688 | "Galilee",
689 | "si310",
690 | 0
691 | ],
692 | [
693 | "Callum",
694 | "si310",
695 | 0
696 | ],
697 | [
698 | "Erina",
699 | "si310",
700 | 0
701 | ],
702 | [
703 | "Samar",
704 | "si310",
705 | 0
706 | ],
707 | [
708 | "Carson",
709 | "si310",
710 | 0
711 | ],
712 | [
713 | "Kaleb",
714 | "si310",
715 | 0
716 | ],
717 | [
718 | "Fathema",
719 | "si310",
720 | 0
721 | ],
722 | [
723 | "Naina",
724 | "si310",
725 | 0
726 | ],
727 | [
728 | "Haidyn",
729 | "si310",
730 | 0
731 | ],
732 | [
733 | "Teigan",
734 | "si310",
735 | 0
736 | ],
737 | [
738 | "Teighan",
739 | "si310",
740 | 0
741 | ],
742 | [
743 | "Tasha",
744 | "si310",
745 | 0
746 | ],
747 | [
748 | "Anise",
749 | "si310",
750 | 0
751 | ],
752 | [
753 | "Zohair",
754 | "si310",
755 | 0
756 | ],
757 | [
758 | "Babur",
759 | "si310",
760 | 0
761 | ],
762 | [
763 | "Haghdann",
764 | "si310",
765 | 0
766 | ],
767 | [
768 | "Khizer",
769 | "si310",
770 | 0
771 | ],
772 | [
773 | "Conli",
774 | "si310",
775 | 0
776 | ],
777 | [
778 | "Taniesha",
779 | "si310",
780 | 0
781 | ],
782 | [
783 | "Mackenzie",
784 | "si310",
785 | 0
786 | ],
787 | [
788 | "Divinewill",
789 | "si310",
790 | 0
791 | ],
792 | [
793 | "Jules",
794 | "si310",
795 | 0
796 | ],
797 | [
798 | "Nerea",
799 | "si310",
800 | 0
801 | ],
802 | [
803 | "Fikret",
804 | "si310",
805 | 0
806 | ],
807 | [
808 | "Aimiee",
809 | "si310",
810 | 0
811 | ],
812 | [
813 | "Caoilfinn",
814 | "si310",
815 | 0
816 | ],
817 | [
818 | "Sharona",
819 | "si310",
820 | 0
821 | ],
822 | [
823 | "Renars",
824 | "si310",
825 | 0
826 | ],
827 | [
828 | "Lyra",
829 | "si310",
830 | 0
831 | ],
832 | [
833 | "Megg",
834 | "si310",
835 | 0
836 | ],
837 | [
838 | "Ben",
839 | "si310",
840 | 0
841 | ],
842 | [
843 | "Patience",
844 | "si310",
845 | 0
846 | ],
847 | [
848 | "Siouxsie",
849 | "si310",
850 | 0
851 | ],
852 | [
853 | "Bret",
854 | "si334",
855 | 1
856 | ],
857 | [
858 | "Amiee",
859 | "si334",
860 | 0
861 | ],
862 | [
863 | "Suzannah",
864 | "si334",
865 | 0
866 | ],
867 | [
868 | "Aleeyah",
869 | "si334",
870 | 0
871 | ],
872 | [
873 | "Summer",
874 | "si334",
875 | 0
876 | ],
877 | [
878 | "Choire",
879 | "si334",
880 | 0
881 | ],
882 | [
883 | "Layah",
884 | "si334",
885 | 0
886 | ],
887 | [
888 | "Chardonnay",
889 | "si334",
890 | 0
891 | ],
892 | [
893 | "Sahara",
894 | "si334",
895 | 0
896 | ],
897 | [
898 | "Abu",
899 | "si334",
900 | 0
901 | ],
902 | [
903 | "Saffi",
904 | "si334",
905 | 0
906 | ],
907 | [
908 | "Edison",
909 | "si334",
910 | 0
911 | ],
912 | [
913 | "Scot",
914 | "si334",
915 | 0
916 | ],
917 | [
918 | "Badr",
919 | "si334",
920 | 0
921 | ],
922 | [
923 | "Rowen",
924 | "si334",
925 | 0
926 | ],
927 | [
928 | "Dalong",
929 | "si334",
930 | 0
931 | ],
932 | [
933 | "Samarjit",
934 | "si334",
935 | 0
936 | ],
937 | [
938 | "Chi",
939 | "si334",
940 | 0
941 | ],
942 | [
943 | "Deshawn",
944 | "si334",
945 | 0
946 | ],
947 | [
948 | "Mollie",
949 | "si334",
950 | 0
951 | ],
952 | [
953 | "Siouxsie",
954 | "si334",
955 | 0
956 | ],
957 | [
958 | "Boys",
959 | "si334",
960 | 0
961 | ],
962 | [
963 | "Sahar",
964 | "si334",
965 | 0
966 | ],
967 | [
968 | "Narissa",
969 | "si334",
970 | 0
971 | ],
972 | [
973 | "Keeton",
974 | "si334",
975 | 0
976 | ],
977 | [
978 | "Mikella",
979 | "si334",
980 | 0
981 | ],
982 | [
983 | "Laiyah",
984 | "si334",
985 | 0
986 | ],
987 | [
988 | "Abigael",
989 | "si334",
990 | 0
991 | ],
992 | [
993 | "Khelis",
994 | "si334",
995 | 0
996 | ],
997 | [
998 | "Orrin",
999 | "si334",
1000 | 0
1001 | ],
1002 | [
1003 | "Ayomide",
1004 | "si334",
1005 | 0
1006 | ],
1007 | [
1008 | "Kodi",
1009 | "si334",
1010 | 0
1011 | ],
1012 | [
1013 | "Selina",
1014 | "si334",
1015 | 0
1016 | ],
1017 | [
1018 | "Lanna",
1019 | "si334",
1020 | 0
1021 | ],
1022 | [
1023 | "Abdulkhader",
1024 | "si334",
1025 | 0
1026 | ],
1027 | [
1028 | "Bradyn",
1029 | "si334",
1030 | 0
1031 | ],
1032 | [
1033 | "Harlie",
1034 | "si334",
1035 | 0
1036 | ],
1037 | [
1038 | "Verity",
1039 | "si334",
1040 | 0
1041 | ],
1042 | [
1043 | "Nawal",
1044 | "si334",
1045 | 0
1046 | ],
1047 | [
1048 | "Simra",
1049 | "si334",
1050 | 0
1051 | ],
1052 | [
1053 | "Kyrran",
1054 | "si334",
1055 | 0
1056 | ],
1057 | [
1058 | "Tyelor",
1059 | "si363",
1060 | 1
1061 | ],
1062 | [
1063 | "Sorche",
1064 | "si363",
1065 | 0
1066 | ],
1067 | [
1068 | "Girls",
1069 | "si363",
1070 | 0
1071 | ],
1072 | [
1073 | "Aulay",
1074 | "si363",
1075 | 0
1076 | ],
1077 | [
1078 | "Darwyn",
1079 | "si363",
1080 | 0
1081 | ],
1082 | [
1083 | "Caitlinn",
1084 | "si363",
1085 | 0
1086 | ],
1087 | [
1088 | "Roman",
1089 | "si363",
1090 | 0
1091 | ],
1092 | [
1093 | "Milly",
1094 | "si363",
1095 | 0
1096 | ],
1097 | [
1098 | "Lee",
1099 | "si363",
1100 | 0
1101 | ],
1102 | [
1103 | "Roddy",
1104 | "si363",
1105 | 0
1106 | ],
1107 | [
1108 | "Reace",
1109 | "si363",
1110 | 0
1111 | ],
1112 | [
1113 | "Chrissie",
1114 | "si363",
1115 | 0
1116 | ],
1117 | [
1118 | "Mutinta",
1119 | "si363",
1120 | 0
1121 | ],
1122 | [
1123 | "Tristan",
1124 | "si363",
1125 | 0
1126 | ],
1127 | [
1128 | "Niven",
1129 | "si363",
1130 | 0
1131 | ],
1132 | [
1133 | "Tal",
1134 | "si363",
1135 | 0
1136 | ],
1137 | [
1138 | "Unaiza",
1139 | "si363",
1140 | 0
1141 | ],
1142 | [
1143 | "Olutobilola",
1144 | "si363",
1145 | 0
1146 | ],
1147 | [
1148 | "Aonghus",
1149 | "si363",
1150 | 0
1151 | ],
1152 | [
1153 | "Halle",
1154 | "si363",
1155 | 0
1156 | ],
1157 | [
1158 | "Roxie",
1159 | "si363",
1160 | 0
1161 | ],
1162 | [
1163 | "Sarahlouise",
1164 | "si363",
1165 | 0
1166 | ],
1167 | [
1168 | "Lysander",
1169 | "si363",
1170 | 0
1171 | ],
1172 | [
1173 | "Adenn",
1174 | "si363",
1175 | 0
1176 | ],
1177 | [
1178 | "Hayla",
1179 | "si363",
1180 | 0
1181 | ],
1182 | [
1183 | "Saskia",
1184 | "si363",
1185 | 0
1186 | ],
1187 | [
1188 | "Jordan",
1189 | "si363",
1190 | 0
1191 | ],
1192 | [
1193 | "Camerin",
1194 | "si363",
1195 | 0
1196 | ],
1197 | [
1198 | "Ayrton",
1199 | "si363",
1200 | 0
1201 | ],
1202 | [
1203 | "Emelye",
1204 | "si363",
1205 | 0
1206 | ],
1207 | [
1208 | "Melodie",
1209 | "si363",
1210 | 0
1211 | ],
1212 | [
1213 | "Mathuyan",
1214 | "si363",
1215 | 0
1216 | ],
1217 | [
1218 | "Stevie",
1219 | "si363",
1220 | 0
1221 | ],
1222 | [
1223 | "Fruin",
1224 | "si363",
1225 | 0
1226 | ],
1227 | [
1228 | "Kalli",
1229 | "si363",
1230 | 0
1231 | ],
1232 | [
1233 | "Imama",
1234 | "si363",
1235 | 0
1236 | ],
1237 | [
1238 | "Nivyn",
1239 | "si363",
1240 | 0
1241 | ],
1242 | [
1243 | "Yong",
1244 | "si363",
1245 | 0
1246 | ],
1247 | [
1248 | "Ravin",
1249 | "si363",
1250 | 0
1251 | ],
1252 | [
1253 | "Donnie",
1254 | "si364",
1255 | 1
1256 | ],
1257 | [
1258 | "Anastasia",
1259 | "si364",
1260 | 0
1261 | ],
1262 | [
1263 | "Jaeden",
1264 | "si364",
1265 | 0
1266 | ],
1267 | [
1268 | "Ivan",
1269 | "si364",
1270 | 0
1271 | ],
1272 | [
1273 | "Natividad",
1274 | "si364",
1275 | 0
1276 | ],
1277 | [
1278 | "Beyza",
1279 | "si364",
1280 | 0
1281 | ],
1282 | [
1283 | "Cecily",
1284 | "si364",
1285 | 0
1286 | ],
1287 | [
1288 | "Rheanan",
1289 | "si364",
1290 | 0
1291 | ],
1292 | [
1293 | "Ayaan",
1294 | "si364",
1295 | 0
1296 | ],
1297 | [
1298 | "Sahana",
1299 | "si364",
1300 | 0
1301 | ],
1302 | [
1303 | "Siannon",
1304 | "si364",
1305 | 0
1306 | ],
1307 | [
1308 | "Tiarn",
1309 | "si364",
1310 | 0
1311 | ],
1312 | [
1313 | "Cal",
1314 | "si364",
1315 | 0
1316 | ],
1317 | [
1318 | "Lacci",
1319 | "si364",
1320 | 0
1321 | ],
1322 | [
1323 | "Lucille",
1324 | "si364",
1325 | 0
1326 | ],
1327 | [
1328 | "Deena",
1329 | "si364",
1330 | 0
1331 | ],
1332 | [
1333 | "Surina",
1334 | "si364",
1335 | 0
1336 | ],
1337 | [
1338 | "Ala",
1339 | "si364",
1340 | 0
1341 | ],
1342 | [
1343 | "Peni",
1344 | "si364",
1345 | 0
1346 | ],
1347 | [
1348 | "Kellan",
1349 | "si364",
1350 | 0
1351 | ],
1352 | [
1353 | "Carina",
1354 | "si364",
1355 | 0
1356 | ],
1357 | [
1358 | "Tiree",
1359 | "si422",
1360 | 1
1361 | ],
1362 | [
1363 | "Ramsey",
1364 | "si422",
1365 | 0
1366 | ],
1367 | [
1368 | "Cruz",
1369 | "si422",
1370 | 0
1371 | ],
1372 | [
1373 | "Thorben",
1374 | "si422",
1375 | 0
1376 | ],
1377 | [
1378 | "Franko",
1379 | "si422",
1380 | 0
1381 | ],
1382 | [
1383 | "Alanas",
1384 | "si422",
1385 | 0
1386 | ],
1387 | [
1388 | "Julieanne",
1389 | "si422",
1390 | 0
1391 | ],
1392 | [
1393 | "Blaire",
1394 | "si422",
1395 | 0
1396 | ],
1397 | [
1398 | "Chizaram",
1399 | "si422",
1400 | 0
1401 | ],
1402 | [
1403 | "Yva",
1404 | "si422",
1405 | 0
1406 | ],
1407 | [
1408 | "Jonathon",
1409 | "si422",
1410 | 0
1411 | ],
1412 | [
1413 | "Isaa",
1414 | "si422",
1415 | 0
1416 | ],
1417 | [
1418 | "Rhy",
1419 | "si422",
1420 | 0
1421 | ],
1422 | [
1423 | "Pamela",
1424 | "si422",
1425 | 0
1426 | ],
1427 | [
1428 | "Abdulbasir",
1429 | "si422",
1430 | 0
1431 | ],
1432 | [
1433 | "Stella",
1434 | "si422",
1435 | 0
1436 | ],
1437 | [
1438 | "Taylar",
1439 | "si422",
1440 | 0
1441 | ],
1442 | [
1443 | "Owen",
1444 | "si422",
1445 | 0
1446 | ],
1447 | [
1448 | "Unaiza",
1449 | "si422",
1450 | 0
1451 | ],
1452 | [
1453 | "Barbara",
1454 | "si422",
1455 | 0
1456 | ],
1457 | [
1458 | "Sukhmani",
1459 | "si422",
1460 | 0
1461 | ],
1462 | [
1463 | "Jincheng",
1464 | "si422",
1465 | 0
1466 | ],
1467 | [
1468 | "Page",
1469 | "si422",
1470 | 0
1471 | ],
1472 | [
1473 | "Luic",
1474 | "si422",
1475 | 0
1476 | ],
1477 | [
1478 | "Kaelyn",
1479 | "si430",
1480 | 1
1481 | ],
1482 | [
1483 | "Ina",
1484 | "si430",
1485 | 0
1486 | ],
1487 | [
1488 | "Hashem",
1489 | "si430",
1490 | 0
1491 | ],
1492 | [
1493 | "Amera",
1494 | "si430",
1495 | 0
1496 | ],
1497 | [
1498 | "Olurotimi",
1499 | "si430",
1500 | 0
1501 | ],
1502 | [
1503 | "Julita",
1504 | "si430",
1505 | 0
1506 | ],
1507 | [
1508 | "Evan",
1509 | "si430",
1510 | 0
1511 | ],
1512 | [
1513 | "Azeem",
1514 | "si430",
1515 | 0
1516 | ],
1517 | [
1518 | "Caleigh",
1519 | "si430",
1520 | 0
1521 | ],
1522 | [
1523 | "Cacie",
1524 | "si430",
1525 | 0
1526 | ],
1527 | [
1528 | "Kavita",
1529 | "si430",
1530 | 0
1531 | ],
1532 | [
1533 | "Cari",
1534 | "si430",
1535 | 0
1536 | ],
1537 | [
1538 | "Taonga",
1539 | "si430",
1540 | 0
1541 | ],
1542 | [
1543 | "Shafaq",
1544 | "si430",
1545 | 0
1546 | ],
1547 | [
1548 | "Thia",
1549 | "si430",
1550 | 0
1551 | ],
1552 | [
1553 | "Samuela",
1554 | "si430",
1555 | 0
1556 | ],
1557 | [
1558 | "Zack",
1559 | "si430",
1560 | 0
1561 | ],
1562 | [
1563 | "Grace",
1564 | "si430",
1565 | 0
1566 | ],
1567 | [
1568 | "Fenn",
1569 | "si430",
1570 | 0
1571 | ],
1572 | [
1573 | "Sammie",
1574 | "si430",
1575 | 0
1576 | ],
1577 | [
1578 | "Becca",
1579 | "si430",
1580 | 0
1581 | ],
1582 | [
1583 | "Queenie",
1584 | "si430",
1585 | 0
1586 | ],
1587 | [
1588 | "Aoibheann",
1589 | "si430",
1590 | 0
1591 | ],
1592 | [
1593 | "Ayleigh",
1594 | "si430",
1595 | 0
1596 | ],
1597 | [
1598 | "Wojciech",
1599 | "si430",
1600 | 0
1601 | ],
1602 | [
1603 | "Keisha",
1604 | "si430",
1605 | 0
1606 | ],
1607 | [
1608 | "Keeley",
1609 | "si430",
1610 | 0
1611 | ],
1612 | [
1613 | "James",
1614 | "si430",
1615 | 0
1616 | ],
1617 | [
1618 | "Bowen",
1619 | "si430",
1620 | 0
1621 | ],
1622 | [
1623 | "Niven",
1624 | "si430",
1625 | 0
1626 | ]
1627 | ]
--------------------------------------------------------------------------------
/Week 4 Many-to-Many Relationships/Assignment/rosterdb.sqlite:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/jeremy-wl/coursera-using-databases-with-python/cd3154f72cfaa8cb6dc12126d48cfa5fad63f9f6/Week 4 Many-to-Many Relationships/Assignment/rosterdb.sqlite
--------------------------------------------------------------------------------
/Week 5 Databases and Visualization/Assignment/README.txt:
--------------------------------------------------------------------------------
1 | Using the Google Geocoding API with a Database and
2 | Visualizing data on Google Map
3 |
4 | In this project, we are using the Google geocoding API
5 | to clean up some user-entered geographic locations of
6 | university names and then placing the data on a Google
7 | Map.
8 |
9 | You should install the SQLite browser to view and modify
10 | the databases from:
11 |
12 | http://sqlitebrowser.org/
13 |
14 | The first problem to solve is that the Google geocoding
15 | API is rate limited to 2500 requests per day. So if you have
16 | a lot of data you might need to stop and restart the lookup
17 | process several times. So we break the problem into two
18 | phases.
19 |
20 | In the first phase we take our input data in the file
21 | (where.data) and read it one line at a time, and retreive the
22 | geocoded response and store it in a database (geodata.sqlite).
23 | Before we use the geocoding API, we simply check to see if
24 | we already have the data for that particular line of input.
25 |
26 | You can re-start the process at any time by removing the file
27 | geodata.sqlite
28 |
29 | Run the geoload.py program. This program will read the input
30 | lines in where.data and for each line check to see if it is already
31 | in the database and if we don't have the data for the location,
32 | call the geocoding API to retrieve the data and store it in
33 | the database.
34 |
35 | Here is a sample run after there is already some data in the
36 | database:
37 |
38 | Mac: python geoload.py
39 | Win: geoload.py
40 |
41 | Found in database Northeastern University
42 |
43 | Found in database University of Hong Kong, Illinois Institute of Technology, Bradley University
44 |
45 | Found in database Technion
46 |
47 | Found in database Viswakarma Institute, Pune, India
48 |
49 | Found in database UMD
50 |
51 | Found in database Tufts University
52 |
53 | Resolving Monash University
54 | Retrieving http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=Monash+University
55 | Retrieved 2063 characters { "results" : [
56 | {u'status': u'OK', u'results': ... }
57 |
58 | Resolving Kokshetau Institute of Economics and Management
59 | Retrieving http://maps.googleapis.com/maps/api/geocode/json?sensor=false&address=Kokshetau+Institute+of+Economics+and+Management
60 | Retrieved 1749 characters { "results" : [
61 | {u'status': u'OK', u'results': ... }
62 |
63 | The first five locations are already in the database and so they
64 | are skipped. The program scans to the point where it finds un-retrieved
65 | locations and starts retrieving them.
66 |
67 | The geoload.py can be stopped at any time, and there is a counter
68 | that you can use to limit the number of calls to the geocoding
69 | API for each run.
70 |
71 | Once you have some data loaded into geodata.sqlite, you can
72 | visualize the data using the (geodump.py) program. This
73 | program reads the database and writes tile file (where.js)
74 | with the location, latitude, and longitude in the form of
75 | executable JavaScript code.
76 |
77 | A run of the geodump.py program is as follows:
78 |
79 | Mac: python geodump.py
80 | Win: geodump.py
81 |
82 | Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA 42.3396998 -71.08975
83 | Bradley University, 1501 West Bradley Avenue, Peoria, IL 61625, USA 40.6963857 -89.6160811
84 | ...
85 | Technion, Viazman 87, Kesalsaba, 32000, Israel 32.7775 35.0216667
86 | Monash University Clayton Campus, Wellington Road, Clayton VIC 3800, Australia -37.9152113 145.134682
87 | Kokshetau, Kazakhstan 53.2833333 69.3833333
88 | ...
89 | 12 records written to where.js
90 | Open where.html to view the data in a browser
91 |
92 | The file (where.html) consists of HTML and JavaScript to visualize
93 | a Google Map. It reads the most recent data in where.js to get
94 | the data to be visualized. Here is the format of the where.js file:
95 |
96 | myData = [
97 | [42.3396998,-71.08975, 'Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA'],
98 | [40.6963857,-89.6160811, 'Bradley University, 1501 West Bradley Avenue, Peoria, IL 61625, USA'],
99 | [32.7775,35.0216667, 'Technion, Viazman 87, Kesalsaba, 32000, Israel'],
100 | ...
101 | ];
102 |
103 | This is a JavaScript list of lists. The syntax for JavaScript
104 | list constants is very similar to Python so the syntax should
105 | be familiar to you.
106 |
107 | Simply open where.html in a browser to see the locations. You
108 | can hover over each map pin to find the location that the
109 | gecoding API returned for the user-entered input. If you
110 | cannot see any data when you open the where.html file, you might
111 | want to check the JavaScript or developer console for your browser.
112 |
113 |
--------------------------------------------------------------------------------
/Week 5 Databases and Visualization/Assignment/geodump.py:
--------------------------------------------------------------------------------
1 | import sqlite3
2 | import json
3 | import codecs
4 |
5 | conn = sqlite3.connect('geodata.sqlite')
6 | cur = conn.cursor()
7 |
8 | cur.execute('SELECT * FROM Locations')
9 | fhand = codecs.open('where.js','w', "utf-8")
10 | fhand.write("myData = [\n")
11 | count = 0
12 | for row in cur :
13 | data = str(row[1])
14 | try: js = json.loads(str(data))
15 | except: continue
16 |
17 | if not('status' in js and js['status'] == 'OK') : continue
18 |
19 | lat = js["results"][0]["geometry"]["location"]["lat"]
20 | lng = js["results"][0]["geometry"]["location"]["lng"]
21 | if lat == 0 or lng == 0 : continue
22 | where = js['results'][0]['formatted_address']
23 | where = where.replace("'","")
24 | try :
25 | print where, lat, lng
26 |
27 | count = count + 1
28 | if count > 1 : fhand.write(",\n")
29 | output = "["+str(lat)+","+str(lng)+", '"+where+"']"
30 | fhand.write(output)
31 | except:
32 | continue
33 |
34 | fhand.write("\n];\n")
35 | cur.close()
36 | fhand.close()
37 | print count, "records written to where.js"
38 | print "Open where.html to view the data in a browser"
39 |
40 |
--------------------------------------------------------------------------------
/Week 5 Databases and Visualization/Assignment/geoload.py:
--------------------------------------------------------------------------------
1 | import urllib
2 | import sqlite3
3 | import json
4 | import time
5 | import ssl
6 |
7 | # If you are in China use this URL:
8 | # serviceurl = "http://maps.google.cn/maps/api/geocode/json?"
9 | serviceurl = "http://maps.googleapis.com/maps/api/geocode/json?"
10 |
11 | # Deal with SSL certificate anomalies Python > 2.7
12 | # scontext = ssl.SSLContext(ssl.PROTOCOL_TLSv1)
13 | scontext = None
14 |
15 | conn = sqlite3.connect('geodata.sqlite')
16 | cur = conn.cursor()
17 |
18 | cur.execute('''
19 | CREATE TABLE IF NOT EXISTS Locations (address TEXT, geodata TEXT)''')
20 |
21 | fh = open("where.data")
22 | count = 0
23 | for line in fh:
24 | if count > 200: break
25 | address = line.strip()
26 | print("")
27 | cur.execute("SELECT geodata FROM Locations WHERE address= ?", (address, ))
28 |
29 | try:
30 | data = cur.fetchone()[0]
31 | print("Found in database ", address)
32 | continue
33 | except:
34 | pass
35 |
36 | print("Resolving',", address)
37 | url = serviceurl + urllib.urlencode({"sensor": "false", "address": address})
38 | print("Retrieving',", url)
39 | uh = urllib.urlopen(url, scontext)
40 | data = uh.read()
41 | print("Retrieved',", len(data), 'characters', data[:20].replace('\n', ' '))
42 | count += 1
43 | try:
44 | js = json.loads(str(data))
45 | # print js # We print in case unicode causes an error
46 | except:
47 | continue
48 |
49 | if 'status' not in js or (js['status'] != 'OK' and js['status'] != 'ZERO_RESULTS'):
50 | print("==== Failure To Retrieve ===='")
51 | print(data)
52 | break
53 |
54 | cur.execute('''INSERT INTO Locations (address, geodata)
55 | VALUES ( ?, ? )''', (address, data))
56 | conn.commit()
57 | time.sleep(1)
58 |
59 | print("Run geodump.py to read the data from the database so you can visualize it on a map.")
60 |
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/Week 5 Databases and Visualization/Assignment/where.data:
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1 | Shanghai Normal University
2 | East China Normal University
3 | Jinmao Tower
4 | Northeastern University
5 | University of Hong Kong, Illinois Institute of Technology, Bradley University
6 | Technion
7 | Viswakarma Institute, Pune, India
8 | UMD
9 | Tufts University
10 | Monash University
11 | Kokshetau Institute of Economics and Management
12 | RSU named S.A. Esenin
13 | Tavrida National V.I. Vernadsky University
14 | UOC
15 | Irkutsk State University
16 | Institute of Technology Telkom
17 | Shanghai Jiao Tong University
18 | University of Ilorin, Kwara State. Nigeria
19 | Monash University Churchill Australia
20 | UNISA
21 | Fachhochschule FH Salzburg
22 | Tampere University of Technology (Tampere, Finland)
23 | Saint Petersburg State University
24 | University of São Paulo
25 | Smolensk State University (Russia)
26 | Institute of Business Administration, Karachi
27 | universidad complutense de madrid
28 | Masdar Institute
29 | University of London
30 | University of Oxford
31 | Tallinn University of Technology
32 | University of Tartu
33 | University of Padua
34 | University of Pune, India
35 | National Kyiv Shevchenko University
36 | UC Berkeley
37 | University of Wisconsin - Madison
38 | Lodz University of Technology
39 | NRU IFMO
40 | Dniepropetrovsk National University (Ukraine), Applied Math Faculty
41 | Dokuz Eylul University, Izmir, Turkey
42 | Beijing normal university
43 | University of Piraeus, Athens
44 | Universidad de Buenos Aires (UBA). Argentina.
45 | SASTRA University
46 | Nagpur University
47 | Duke University
48 | San Francisco State University
49 | FATEC-SP - Faculdade de Tecnologia do Estado de São Paulo
50 | University of Texas at Austin
51 | University of applied sciense of Mikkeli (Finland)
52 | Troy University
53 | Universidade do Minho
54 | National University of Sciences and Technology (NUST)-Pakistan
55 | Pontificia universidad catolica de chile
56 | Illinois State University Joliet Junior College
57 | American University in Cairo (AUC)
58 | Obninsk Technical University of Nuclear Power Engineering, Russia
59 | Vyatka State Humanitarian University
60 | Weizmann Institute of Science (Israel)
61 | University of Washington
62 | Kharkiv State Academy of Municipal Economy, Ukraine
63 | Faculty of Electrical Engineering in Sarajevo, University of Sarajevo
64 | Universidad de Los Andes Colombia
65 | University of Colorado at Boulder
66 | Magnitogorsk State Technical University
67 | USC
68 | Simon Fraser University
69 | Columbia University (New York)
70 | University of Southern California
71 | University of Warsaw
72 | Warsaw University of Technology
73 | (Some place in New Zealand you haven't heard of.)
74 | Massey university part-time Distance learning
75 | University of Oklahoma
76 | University of Pavia, Italy
77 | University of Missouri - Columbia
78 | Czech Technical University in Prague
79 | Illinois Institute of Technology
80 | Penn State University
81 | University of Utah
82 | Faculty of Science, University of Zagreb - Department of Mathematics
83 | Universitat Politecnica de Valencia
84 | University of Vienna
85 | University of Puerto Rico - Mayaguez Campus
86 | University "Hyperion" of Bucharest
87 | University of New Haven
88 | University of Washington -Bothell
89 | Drexel University
90 | University of Texas at Austin
91 | University of Helsinki
92 | University of Michigan
93 | Carnegie Mellon University
94 | Kazan Federal University
95 | Pondicherry University
96 | Far-Eastern State University
97 | Nanyang Technological University
98 | Slovak University of Technology
99 | NYU
100 | UFABC - Universidade Federal do ABC, Sanso André - SP - Brazil
101 | University of Debrecen
102 | California State University, San Bernardino
103 | National University "Kyiv-Mohyla Academy" (Kyiv, Ukraine)
104 | Laurentian University
105 | Humanities Institute of TV and Radio, Moscow, Russia
106 | University of Cambridge, UK
107 | Payame Noor University, Tehran, Iran
108 | Middle East Technical University
109 | EPFL
110 | Faculty of Technical Sciences, Novi Sad, Serbia
111 | University of Gothenburg, Sweden
112 | Polytechnic University of Timisoara
113 | University of Hawaii (Go, Rainbows!)
114 | Belarusian State University
115 | Haaga-Helia university of applied sciences
116 | JADAVPUR UNIVERSITY
117 | Gauhati University, India
118 | Universidad de Buenos Aires
119 | Università degli Studi di Genova, Genova, Italia
120 | King Mongkut's University of Technology Thonburi
121 | Universidad de la Sabana, Chia, Colombia
122 | State University of New York (SUNY) College at Oswego
123 | Kyrgyz Slavic Russian University
124 | De La Salle University http://www.dlsu.edu.ph
125 | Jawaharlal Nehru Technological University, INDIA
126 | UCL (Université Catholique de Louvain) in Belgium
127 | Boston University
128 | The University of Manchester
129 | Fachhochschule Düsseldorf
130 | Pine Manor College (AA), Harvard University (BA), Lesley University (MEd)
131 | Simón Bolívar University
132 | Indiana University at Bloomington
133 | RPI
134 | University of Ottawa, Canada
135 | Ural Federal University
136 | BITS Pilani
137 | Transilvania University
138 | IIT(BHU), Varanasi, India
139 | EM Lyon
140 | Universidad Central de Venezuela
141 | NTUU "KPI"
142 | Universidade Federal da Paraiba, Brazil
143 | Budapest University of Technology and Economics
144 | Moscow Institute of Physics & Technology (State University)
145 | Saint Petersburg State University of Aerospace Instrumentation, Russia
146 | North Central College, Naperville, IL
147 | Tech. Uni. Denmark (DTU)
148 | Stanford
149 | "Politehnica" Timisoara
150 | National University of Engineering
151 | Monash
152 | Federal University of Campina Grande (UFCG)
153 | Universidade Federal do Rio Grande do Sul (UFRGS)
154 | Universidad Nacional Autónoma de México
155 | University of New South Wales Harvard Business School
156 | University of Tehran
157 | Old Dominion University
158 | Kyiv Unisersity of Oriental Language
159 | Babcock University
160 | University of Essex
161 | Kharkiv National University of Radio Electronics (Ukraine)
162 | Kaunas Technology University
163 | University of Buenos Aires
164 | University of Jaffna.
165 | R V College of Engineering, Bangalore, India for BE in Instrumentation Technology
166 | Beloit College
167 | UCLA
168 | University of Chicago
169 | University of Sciences and Technology of Oran. Mohamed Boudiaf (USTO-MB).
170 | Zagazig University, Egypt
171 | University of Alberta
172 | Belorussian State University
173 | Jones International University (online) Illinois State Univeristy
174 | University of Florida
175 | Too many to mention.
176 | University of Kerala, India
177 | Politecnico di Milano
178 | Vilnius Gediminas Technical University
179 | Madras university/ Bharthidasan University in India .
180 | Universidade Tecnica de Lisboa - Instituto Superior Técnico
181 | Does not apply.
182 | Stellenbosch University
183 | imt ghazIABAD INDIA
184 | University of Pennsylvania
185 | National Institute of Technology, Jalandhar (India)
186 | Universidad ICESI
187 | Virginia Tech
188 | arizona state university
189 | Universidad del Valle de Guatemala
190 | Mykolas Romeris University, Vilnius, Lithuania
191 | BSU
192 | Distance Learning Center at the Technical University of Kaiserslautern in Germany
193 | Ain shams university, Cairo, Egypt
194 | Universidad Nacional de Colombia
195 | Saint-Petersburg Polytechnic Univesity
196 | NAIT (Northern Alberta Institute of Technology)
197 | Wayne State took courses at U of M
198 | Universidad Nacional, Costa Rica
199 | Marietta College (Ohio) Northwestern University
200 | Grandville
201 | Portland State University, Oregon Institute of Technology
202 | Malayer Azad University, Iran
203 |
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/Week 5 Databases and Visualization/Assignment/where.html:
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1 |
2 |
3 |
4 |
5 | A Map of Information
6 |
7 |
8 |
10 |
11 |
12 |
13 |
14 |
43 |
44 |
45 |
46 | About this Map
47 |
48 | This is a cool map from
49 | www.pythonlearn.com.
50 |
51 |
52 |
53 |
--------------------------------------------------------------------------------
/Week 5 Databases and Visualization/Assignment/where.js:
--------------------------------------------------------------------------------
1 | myData = [
2 | [31.1615031,121.4193454, 'Shanghai Normal University, Xuhui, Shanghai, China, 200231'],
3 | [31.2272891,121.4094554, 'East China Normal University Gymnasium, Huashida, Putuo, Shanghai, China, 200062'],
4 | [31.2352394,121.5058635, 'Jinmao Tower, Lujiazui, Pudong, Shanghai, China'],
5 | [42.340075,-71.0895367, 'Northeastern, Boston, MA 02115, USA'],
6 | [38.2113643,-85.7470011, 'Bradley Ave, Louisville, KY, USA'],
7 | [32.778949,35.019648, 'Technion/ Sports Building, Haifa'],
8 | [18.4574518,73.8837999, 'Vishwakarma Institutes Play Ground, Yashodhan Society, Kapil Nagar, Kondhwa Budrukh, Vishwakarma, Maharashtra 411048, India'],
9 | [31.1615031,121.4193454, 'Shanghai Normal University, Xuhui, Shanghai, China, 200231'],
10 | [31.2272891,121.4094554, 'East China Normal University Gymnasium, Huashida, Putuo, Shanghai, China, 200062'],
11 | [31.2352394,121.5058635, 'Jinmao Tower, Lujiazui, Pudong, Shanghai, China'],
12 | [42.340075,-71.0895367, 'Northeastern, Boston, MA 02115, USA'],
13 | [38.2113643,-85.7470011, 'Bradley Ave, Louisville, KY, USA'],
14 | [32.778949,35.019648, 'Technion/ Sports Building, Haifa'],
15 | [18.4574518,73.8837999, 'Vishwakarma Institutes Play Ground, Yashodhan Society, Kapil Nagar, Kondhwa Budrukh, Vishwakarma, Maharashtra 411048, India'],
16 | [33.1561058,131.826132, 'Japan, 〒875-0002 Ōita-ken, Usuki-shi, Shitanoe, 1232−2 ＵＭＤ'],
17 | [42.4036847,-71.120482, 'South Hall Tufts University, 30 Lower Campus Rd, Somerville, MA 02144, USA'],
18 | [-37.914517,145.1303881, 'Monash College, Wellington Rd, Clayton VIC 3168, Australia'],
19 | [53.2948229,69.4047872, 'Kokshetau 020000, Kazakhstan'],
20 | [40.7127837,-74.0059413, 'New York, NY, USA'],
21 | [52.2869741,104.3050183, 'Irkutsk, Irkutsk Oblast, Russia'],
22 | [31.1790053,121.4237432, 'Shanghai Sixth Peoples Hospital Affiliated to Shanghai Jiao Tong University, Xuhui, Shanghai, China, 200231'],
23 | [8.5080482,4.5849938, 'Kwara State University, Ilorin, Nigeria'],
24 | [-38.3105571,146.4292232, 'Monash University Gippsland Student Lounge, 7N Mary Grant Bruce Dr, Churchill VIC 3842, Australia'],
25 | [-34.9221963,138.5922272, 'Yungondi Building, North Terrace, Adelaide SA 5000, Australia'],
26 | [47.80949,13.05501, 'Salzburg, Austria'],
27 | [27.7518284,-82.6267345, 'St. Petersburg, FL, USA'],
28 | [28.6853472,-106.1015266, 'São Paulo, Chihuahua, Chih., Mexico'],
29 | [54.7903112,32.0503663, 'Smolensk, Smolensk Oblast, Russia'],
30 | [40.4469796,-3.7278167, 'Av. Complutense, Madrid, Madrid, Spain'],
31 | [24.4325423,54.6174842, 'Masdar Institute Bus Station - Abu Dhabi - United Arab Emirates'],
32 | [51.5266171,-0.1260773, 'University Of London, 1-11 Cartwright Gardens, Kings Cross, London WC1H 9EB, UK'],
33 | [39.5069974,-84.745231, 'Oxford, OH 45056, USA'],
34 | [59.393847,24.6650872, 'Tallinn University of Technology Stadium, 12616 Tallinn, Estonia'],
35 | [58.3733281,26.7265098, 'University of Tartu Physics Building, 50103 Tartu, Estonia'],
36 | [33.6778327,-117.8151285, 'Padua, Irvine, CA 92614, USA'],
37 | [18.5544976,73.8257325, 'Pune University, Ganeshkhind, Pune, Maharashtra, India'],
38 | [37.8764984,-122.2804342, 'California St, Berkeley, CA, USA'],
39 | [43.0412831,-89.4301473, 'University of Wisconsin-Madison Arboretum, 1207 Seminole Hwy, Madison, WI 53711, USA'],
40 | [51.745806,19.4489068, 'Institute of Applied Computer Science, Lodz University of Technology, 90-924 Łódź, Poland'],
41 | [38.3946981,27.0322689, 'İnciraltı, Dokuz Eylül Ünv. Hst., 35330 Balçova/İzmir, Turkey'],
42 | [39.9314428,116.3049709, 'Beijing Normal University, Haidian, Beijing, China, 100000'],
43 | [37.983917,23.7293599, 'Athens, Greece'],
44 | [10.7295115,79.0196067, 'Sastra University Road, Tirumalaisamudram, Tamil Nadu 613401, India'],
45 | [21.1470404,79.0397862, 'Nagpur University Campus, Nagpur, Maharashtra 440033, India'],
46 | [41.9197689,-91.649501, 'Duke St SW, Cedar Rapids, IA 52404, USA'],
47 | [37.7634766,-122.4390923, 'States St, San Francisco, CA 94114, USA'],
48 | [-23.5505199,-46.6333094, 'São Paulo, São Paulo - State of São Paulo, Brazil'],
49 | [30.2850284,-97.7335226, 'University of Texas at Austin, Austin, TX, USA'],
50 | [61.6858074,27.2734876, '50100 Mikkeli, Finland'],
51 | [32.4204729,-85.0323718, 'H. Curtis Pitts Hall, 3413 S Seale Rd, Phenix City, AL 36869, USA'],
52 | [41.557411,-8.397774, 'Universidade do Minho, 4710 Braga, Portugal'],
53 | [-33.0444219,-71.6066334, 'Pontificia Universidad Catolica De Valparaiso - Gimpert, Valparaíso, Valparaíso, Región de Valparaíso, Chile'],
54 | [40.6331249,-89.3985283, 'Illinois, USA'],
55 | [30.0199119,31.5001527, 'AUC Library, Cairo Governorate 11835, Egypt'],
56 | [55.1170375,36.5970818, 'Obninsk, Kaluga Oblast, Russia'],
57 | [31.9026385,34.8085102, 'Weizmann Institute of Science, Herzl St 234, Rehovot, 76100, Israel'],
58 | [31.767879,-106.440736, 'Washington, El Paso, TX 79905, USA'],
59 | [49.9935,36.230383, 'Kharkiv, Kharkiv Oblast, Ukraine'],
60 | [43.8562586,18.4130763, 'Sarajevo, Bosnia and Herzegovina'],
61 | [4.602551,-74.066627, 'Consultorio Jurídico Universidad de Los Andes, Cl. 19, Bogotá, Bogotá, Colombia'],
62 | [40.0082221,-105.2591119, 'Colorado Ave & University Heights, Boulder, CO 80302, USA'],
63 | [53.4129429,59.0016233, 'Magnitogorsk, Chelyabinsk Oblast, Russia'],
64 | [46.4062583,8.9040484, 'Usc, 6749, Switzerland']
65 | ];
66 |
--------------------------------------------------------------------------------