RAR-files |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.rar |
200,000,000 |
9BA09148 |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r00 |
200,000,000 |
65B43770 |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r01 |
200,000,000 |
663FF24C |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r02 |
200,000,000 |
63AF943B |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r03 |
200,000,000 |
DF124EE9 |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r04 |
200,000,000 |
DD310884 |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r05 |
200,000,000 |
342875E1 |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r06 |
200,000,000 |
38CA103D |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r07 |
200,000,000 |
EB32A1BF |
skillshare.master.python.and.data.libraries.comprehensive.bootcamp-skilledhares.r08 |
147,922,569 |
67FB5CAD |
|
Total size: |
1,947,922,569 |
|
|
Archived
files |
01-download_and_install_the_working_tools.mkv
[437b63effb0d5bd8]
|
12,624,782 |
902DAA30 |
02-jupyter_overview_and_markdown_in_jupyter_tutorial.mkv
[c0ba557607f986e5]
|
34,158,409 |
9961FB2B |
03-using_jupyter_notebook_for_coding_with_python.mkv
[71c9414940201e41]
|
39,474,634 |
4C921019 |
04-use_anaconda_prompt.mkv
[26e1e837648573af]
|
23,851,979 |
708A1C8E |
05-variables_and_types_tutorial.mkv
[be12a904775b8d9]
|
65,489,303 |
9851C23F |
06-describe_whats_inside_the_code.mkv
[4481b409c5e03245]
|
23,590,939 |
12F9B10D |
07-define_blocks_and_avoid_indentationerror.mkv
[7c22149b3b246924]
|
22,841,491 |
9F94E30C |
08-string_full_tutorial.mkv
[2c9865112e3ba4d3]
|
65,275,608 |
CE3E90F0 |
09-numbers_math_and_f-string_tutorial.mkv
[9ec8363fd49a5b23]
|
61,207,405 |
CA8A22FF |
10-handling_inputs_and_outputs.mkv
[533f854d12da57fb]
|
21,911,338 |
3FDB4369 |
11-structure_data_using_lists.mkv
[7d5a78782c601010]
|
97,009,332 |
50ECF89E |
12-structure_data_using_tuples.mkv
[d1436430f563c873]
|
49,623,359 |
C0968329 |
13-structure_data_using_dictionaries.mkv
[d60d1ea02b14a1d9]
|
42,020,565 |
C7AA977E |
14-structure_data_using_sets.mkv
[9e5fbf86ebd54d89]
|
34,094,450 |
0D8B4A85 |
15-comparing_values.mkv
[556bdda23d40a8bf]
|
32,782,344 |
952A14F8 |
16-output_from_logics.mkv
[3897b43a4ff33aa1]
|
40,925,581 |
4B6A72C3 |
17-conditional_statements.mkv
[1aa01b516acbf091]
|
44,032,378 |
718BF1B8 |
18-the_while_loop.mkv
[1e9e504c7c154e19]
|
14,895,479 |
0996762F |
19-the_for_loop_in_python.mkv
[5d5406b70906c8de]
|
41,240,654 |
950AB37A |
20-python_library_functions.mkv
[ce42703c45ec7b23]
|
74,915,463 |
493AAC17 |
21-user-defined_functions.mkv
[c820ccff36cac11e]
|
33,768,299 |
C90D7670 |
22-the_lambda_power.mkv
[9805174d606e2299]
|
43,936,456 |
D47E7895 |
23-the_break_statement.mkv
[6c18dd998ca229b8]
|
20,538,485 |
95203A1B |
24-the_continue_statement.mkv
[646f46f0815b7927]
|
21,819,851 |
30606D44 |
25-for_else_statement.mkv
[71c52a878a2c17ba]
|
12,280,857 |
6DD5D70B |
26-project_app_to_put_all_together.mkv
[77afa2835acb9c7b]
|
9,502,751 |
7EC60FB9 |
27-core_python_oop_classes_and_instances.mkv
[361e212e9ef1da22]
|
54,001,464 |
427328CE |
28-core_python_oop_exploring_inheritance.mkv
[cdf321ff9166f94e]
|
44,180,436 |
2260898C |
29-comprehensions.mkv
[91ce48878bdb6858]
|
43,610,560 |
BB56379F |
30-constructed_modules_and_random.mkv
[2f20cb4d8d00fc4d]
|
27,827,432 |
78855F40 |
31-doing_mathematics.mkv
[98085ea3434f1ed7]
|
21,638,505 |
F7BF760F |
32-doing_statistics.mkv
[b2c94a381823566e]
|
20,458,063 |
AAE076BA |
33-errors_exploration.mkv
[86621367e4aa4afe]
|
25,059,565 |
A1D7FAAE |
34-exceptions_playground.mkv
[89a619d1f8d0b2a0]
|
51,101,900 |
3A01980C |
35-io_data_in_memory_.mkv
[b429614df6a39086]
|
30,759,454 |
AD38ADE2 |
36-interacting_with_operating_system_data.mkv
[63bdcce59ef55c30]
|
23,115,004 |
546BB224 |
37-moving_data_files_between_directories.mkv
[aea86328d1e8b990]
|
27,445,357 |
F6CCA222 |
38-data_will_be_in_the_trash_bin.mkv
[f8a178c3eebfec69]
|
30,529,124 |
B698A845 |
39-zipping_and_unzipping_data.mkv
[e2b7006f219dff07]
|
36,858,358 |
01984803 |
40-numpy_level_1.mkv
[d059d72e08d4c75a]
|
30,731,966 |
E07A1ABB |
41-numpy_level_2.mkv
[8d6b88d33fb77ba2]
|
25,456,012 |
1399A0CB |
42-numpy_level_3.mkv
[114e99f8730d088b]
|
11,171,718 |
CB34A879 |
43-numpy_level_4.mkv
[a02a6df9079a95ab]
|
15,755,456 |
5737707C |
44-numpy_level_5.mkv
[6b4ccb7b5935e474]
|
41,505,924 |
AD3B6B0C |
45-numpy_level_6.mkv
[c202f6591cc0335d]
|
28,795,071 |
3FF8DF25 |
46-numpy_level__7.mkv
[acd446dc2bad0436]
|
23,418,264 |
C32024D0 |
47-numpy_level_8.mkv
[14633af786d3bea6]
|
20,697,549 |
07979036 |
48-numpy_level_9.mkv
[6628e5b97369ceaf]
|
15,202,470 |
E721FC1F |
49-pandas_data_analysis_level_1.mkv
[b9b7fd50711853ae]
|
16,854,141 |
0242C654 |
50-pandas_data_analysis_level_2.mkv
[2f71740715883016]
|
22,095,518 |
025F2BF0 |
51-pandas_data_analysis_level_3.mkv
[f1a3281f35519cba]
|
12,292,634 |
A9094E74 |
52-pandas_data_analysis_level_4.mkv
[f4c50fac928d8661]
|
19,323,478 |
5FFF9735 |
53-pandas_data_analysis_level_5.mkv
[a59f9a0bfac1f7a7]
|
19,895,811 |
A731EE2A |
54-pandas_data_analysis_level_6.mkv
[146e1ed27df27b86]
|
27,615,844 |
A7D2984B |
55-matplotlib_data_visualization_part1.mkv
[f2edcf2eafe16301]
|
14,609,041 |
FCFDDF16 |
56-matplotlib_data_visualization_part2.mkv
[1084dff7c272c081]
|
6,277,832 |
7950E148 |
57-matplotlib_data_visualization_part3.mkv
[6f74d6e8af9abc60]
|
16,842,927 |
EC8C53F4 |
58-matplotlib_data_visualization_part4.mkv
[74f81eeea5ccfadc]
|
17,379,953 |
8B1E88D2 |
59-matplotlib_data_visualization_part5.mkv
[2b354cdcf21ab467]
|
9,066,384 |
ADACFCEB |
60-matplotlib_data_visualization_part6.mkv
[d0ed215da56cf980]
|
14,471,468 |
9050E245 |
61-matplotlib_data_visualization_part7.mkv
[dbf453ce2c8b9fc9]
|
13,535,335 |
A9E7F9D9 |
62-seaborn_statistical_graphs_level_1.mkv
[5fccb5bc7d7b69e5]
|
14,717,894 |
AC2CA369 |
63-seaborn_statistical_graphs_level_2.mkv
[b7f03bb4c0ad7e37]
|
5,876,285 |
106E464A |
64-seaborn_statistical_graphs_level_3.mkv
[38af8c43f0ded388]
|
5,541,980 |
F69D7D6B |
65-seaborn_statistical_graphs_level_4.mkv
[7e30729048237946]
|
8,114,272 |
E69453A0 |
66-seaborn_statistical_graphs_level_5.mkv
[dbf3aed82a4242b6]
|
13,281,684 |
87950BA5 |
67-seaborn_statistical_graphs_level_6.mkv
[2763c90ecd2311bc]
|
10,705,572 |
C2EB9ACB |
68-seaborn_statistical_graphs_level__7.mkv
[f074f0d74158280e]
|
7,513,845 |
1E95892F |
69-python_programming_resources.mkv
[fa6439aa927ffa5d]
|
10,019,287 |
61E45E55 |
70-numpy_resources.mkv
[b5bfc01b9909d7ba]
|
2,628,117 |
C206DBDD |
71-pandas_resources.mkv
[80bdd457319a045d]
|
4,323,379 |
53792D6C |
72-matplotlib_resources.mkv
[d93694da1daf9217]
|
6,913,002 |
AE33704C |
73-seaborn_resources.mkv
[669410d879596a11]
|
3,359,354 |
FE9A890B |
74-intro.mkv
[d0d1c6f9fa00ca64]
|
11,529,507 |
B71FDB1B |
|
Total size: |
1,947,916,388 |
|
|