|
RAR-files |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.rar |
350,000,000 |
271A83B3 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r00 |
350,000,000 |
E88F5088 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r01 |
350,000,000 |
5F0DB9C9 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r02 |
350,000,000 |
F6085DF1 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r03 |
350,000,000 |
C91E391B |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r04 |
350,000,000 |
FCAB926D |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r05 |
350,000,000 |
F25397E6 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r06 |
350,000,000 |
EFA070F8 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r07 |
350,000,000 |
88E99DB9 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r08 |
350,000,000 |
B1646866 |
| skillshare.the.data.science.microdegree.introduction.to.python.data.analysis.and.visualization-skilledhares.r09 |
42,716,411 |
A85B5B59 |
|
| Total size: |
3,542,716,411 |
|
|
| Archived
files |
|
01-introduction.mkv
[fec07e636c938b50]
|
17,335,251 |
96E443E2 |
|
02-setting_up_your_pc.mkv
[48f11d5881257cc3]
|
18,669,455 |
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|
03-anaconda_installation.mkv
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|
31,912,778 |
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|
04-launching_jupyter_notebook.mkv
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|
34,281,337 |
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|
05-navigating_jupyter_notebook.mkv
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36,148,328 |
E2201298 |
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06-markdown_cells.mkv
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|
14,976,940 |
3A3165C5 |
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07-python-data_types_and_arithmetic_operations.mkv
[1dd3f35a00778947]
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95,487,280 |
404BBAF3 |
|
08-python-variables.mkv
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|
68,034,614 |
3E31EEA8 |
|
09-python-strings_and_print_function.mkv
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|
59,305,135 |
85F0BC5C |
|
10-python-string_splicing.mkv
[42566faf7146d98f]
|
46,248,886 |
B4E01DB6 |
|
11-python-lists.mkv
[ab45b470815022ab]
|
100,693,072 |
EA675374 |
|
12-python-dictionaries.mkv
[a4dbc5a4b4293463]
|
78,693,292 |
70EF645C |
|
13-python-tuples_and_sets.mkv
[a7af7e1ec5056866]
|
51,672,428 |
AEFC20E3 |
|
14-python-relational_and_logical_operators.mkv
[a3ca55dabcdd6957]
|
80,253,539 |
87928A4A |
|
15-python-if_else.mkv
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57,875,573 |
56998262 |
|
16-python-for_loops.mkv
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30,746,542 |
F656D0AD |
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17-python-while_loops.mkv
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27,777,587 |
C7F10594 |
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18-pythpn-in_built_functions.mkv
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43,962,069 |
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19-python-creating_a_function.mkv
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88,604,644 |
65B07424 |
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20-numpy-introduction_to_numpy.mkv
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14,099,897 |
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21-numpy-arrays.mkv
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11,394,090 |
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22-numpy-generating_numpy_arrays.mkv
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16,369,058 |
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23-numpy-linspace.mkv
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12,427,385 |
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24-numpy-identity_matrix.mkv
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4,522,448 |
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25-numpy-generating_arrays_with_random_values.mkv
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24,941,586 |
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26-numpy-reshape_min_and_max.mkv
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24,931,185 |
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27-numpy-shape_and_dtype.mkv
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8,485,035 |
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28-numpy-indexing.mkv
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20,277,616 |
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29-numpy-index_broadcasting_i.mkv
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6,905,724 |
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30-numpy-index_broadcasting_ii.mkv
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15,320,763 |
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31-numpy-2d_indexing.mkv
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16,197,061 |
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32-numpy-extracting_submatrices.mkv
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13,218,603 |
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33-numpy-conditional_indexing.mkv
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11,943,366 |
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34-numpy-operations.mkv
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22,659,803 |
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35-numpy-universal_functions.mkv
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17,506,118 |
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36-pandas-series_i.mkv
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21,152,588 |
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37-pandas-series_ii.mkv
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30,371,348 |
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38-pandas-dataframes.mkv
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87,603,856 |
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39-pandas-dataframes_adding_and_dropping_columns.mkv
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54,554,510 |
32B810B4 |
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40-pandas-loc_and_iloc.mkv
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64,359,384 |
6CD36D28 |
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41-pandas-conditional_selection.mkv
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78,322,303 |
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42-pandas-multiple_conditions.mkv
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41,368,438 |
341B09F0 |
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43-pandas-reset_index_and_set_index.mkv
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66,481,977 |
AF639261 |
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44-pandas-dropna_and_fillna.mkv
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101,283,283 |
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45-pandas-group_by.mkv
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78,638,694 |
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46-pandas-join_merge_and_concatenate.mkv
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|
124,180,498 |
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47-pandas-operations.mkv
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130,481,617 |
A7F173DB |
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48-pandas-file_processing.mkv
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119,523,439 |
2BCC96FC |
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49-matplotlib-introduction.mkv
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32,065,037 |
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50-matplotlib-plotting_a_simple_graph.mkv
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78,119,008 |
25971389 |
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51-matplotlib-multiple_plots_inside_same_canvas.mkv
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23,276,085 |
35337BEF |
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52-matplotlib-object_oriented_plots.mkv
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92,496,682 |
365DD5EB |
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53-matplotlib-subplots_using_oop.mkv
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66,464,219 |
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54-matplotlib-modifying_figure_size_and_dpi.mkv
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|
51,139,830 |
61DE3D50 |
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55-matplotlib-saving_the_plot.mkv
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|
23,443,297 |
1864E42C |
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56-matplotlib-creating_a_legend.mkv
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53,638,132 |
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57-matplotlib-customization.mkv
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99,908,941 |
6D6F071C |
|
58-matplotlib-plot_range.mkv
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30,123,834 |
79CD0EDC |
|
59-seaborn-introduction.mkv
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|
30,701,218 |
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60-seaborn-distribution_plots_i.mkv
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108,029,223 |
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61-seaborn-distribution_plots_ii.mkv
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86,574,719 |
85124FA3 |
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62-seaborn-categorical_plots_i.mkv
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|
86,520,327 |
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63-seaborn-categorical_plots_ii.mkv
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103,476,325 |
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64-seaborn-matrix_plots.mkv
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110,986,562 |
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65-seaborn-grids.mkv
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114,368,196 |
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|
66-seaborn-size_and_color.mkv
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129,178,680 |
9A9AF148 |
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| Total size: |
3,542,710,738 |
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|
