|
RAR-files |
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| Total size: |
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|
| Archived
files |
|
5. Using Anaconda Prompt.mp4
[34b68f4c591a2d75]
|
19,908,097 |
92087F05 |
|
6. Variables and Types Tutorial.mp4
[b68c7c6827c3d270]
|
54,445,745 |
956B955F |
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7. Describe what's inside the code.mp4
[921d33062432a15a]
|
20,829,957 |
CB48D52D |
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8. Define Blocks and Avoid IndentationError.mp4
[fa20366543f718d5]
|
19,545,035 |
71FC6B86 |
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9. Strings full tutorial.mp4
[8852cc10c450fc5c]
|
55,812,838 |
F3B9C491 |
|
10. Numbers, Math and f-string tutorial.mp4
[8929042ecf98075f]
|
53,897,318 |
5135F53C |
|
11. Handling inputs and outputs.mp4
[2519114c65cb55cc]
|
18,765,687 |
28CA902E |
|
12. Structure Data using lists.mp4
[dd9a47a6de05c67f]
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84,067,404 |
FDF12D94 |
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13. Structure data using tuples.mp4
[8bd2a04b7c02c61a]
|
42,968,261 |
9FBA5DD4 |
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14. Structure Data using Dictionaries.mp4
[d7f1e48d6b60e46b]
|
35,916,151 |
96D01682 |
|
15. Structure Data using sets.mp4
[645edd5f1b332e38]
|
29,618,933 |
092FC6E4 |
|
16. Comparing Values.mp4
[ad6b90b7a0499e99]
|
28,875,207 |
66FB91C8 |
|
17. Output from Logics.mp4
[41187ce2172cb255]
|
32,997,981 |
1D314E4B |
|
18. Conditional Statements.mp4
[fa58adb98ef0ab2d]
|
37,872,688 |
D7FC2252 |
|
19. The while loop in Python.mp4
[10cf01762f4eee52]
|
12,640,885 |
4A3CF866 |
|
20. The for loop in Python.mp4
[1948594626255f57]
|
32,374,714 |
D0D9FFB4 |
|
21. Python Library Functions.mp4
[fa7ab2a2435bc7ba]
|
58,328,555 |
7CB7309A |
|
22. User-Defined Functions.mp4
[e4037e5e8e25945c]
|
29,548,401 |
689B2091 |
|
23. The lambda power.mp4
[d728f50e5bda2f88]
|
36,469,274 |
32DD81AA |
|
24. The break statement.mp4
[4a94b986d652a45d]
|
17,399,692 |
5B1F8700 |
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25. The continue statement.mp4
[7cb573bded4ded10]
|
18,560,160 |
A700DBDD |
|
26. The for else statement.mp4
[566ab280596182ea]
|
9,713,684 |
8B614A1D |
|
27. Program to Put all together.mp4
[321db81935c2ff4e]
|
7,764,519 |
A0EF8FB9 |
|
28. Core Python OOP Classes and Instances.mp4
[95182faa936b9434]
|
48,712,215 |
CA1619D5 |
|
29. Core Python OOP Exploring Inheritance.mp4
[ca454c62b41c8587]
|
38,332,683 |
C5F82FFE |
|
30. Concise Comprehensions.mp4
[d6687e406910e8d7]
|
32,770,295 |
C475D781 |
|
31. Constructed modules and random.mp4
[540e148f4d507df7]
|
24,259,035 |
3804BF24 |
|
32. Doing mathematics.mp4
[c79b75ed2f936ef1]
|
18,493,374 |
43968054 |
|
33. Doing statistics.mp4
[9c38422077317f39]
|
17,516,867 |
BA3A2311 |
|
34. Errors Exploration.mp4
[d438a6e1d3ab4757]
|
21,083,437 |
7640CDC5 |
|
35. Exceptions Playground.mp4
[2a6a0461790b25a4]
|
39,243,475 |
51A25021 |
|
36. IO data in memory.mp4
[1811f2394476b93a]
|
26,306,162 |
9D979137 |
|
37. Interacting with operating system data.mp4
[de2557bc6f10f754]
|
18,330,303 |
D6E70B79 |
|
38. Moving data files between directories.mp4
[6c46551ae22e0440]
|
22,721,841 |
EF50D0C8 |
|
39. Data will be in the trash bin.mp4
[16e53e18a8d3b944]
|
25,111,799 |
CA34574E |
|
40. Zipping and Unzipping Data.mp4
[27d6de9564268887]
|
30,756,935 |
EAD6A6D6 |
|
41. NumPy Level 1.mp4
[588d6d1b4a888f92]
|
27,444,489 |
EDF0BEDB |
|
42. NumPy Level 2.mp4
[e47d73ccea03f69a]
|
21,352,268 |
F16D779A |
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43. NumPy Level 3.mp4
[36a0230bb7b4c918]
|
9,671,205 |
3295B241 |
|
44. NumPy Level 4.mp4
[2dd2971ab3bd4522]
|
13,599,121 |
95645652 |
|
45. NumPy Level 5.mp4
[45e40e45eb28447c]
|
32,701,746 |
9CD38692 |
|
46. NumPy Level 6.mp4
[6910f1045fd6b8f4]
|
22,513,270 |
7FE8A8D2 |
|
47. NumPy Level 7.mp4
[5621aeb2eda4bcf0]
|
18,169,665 |
55802BD6 |
|
48. NumPy Level 8.mp4
[74ad071a6420849b]
|
16,347,104 |
41495D05 |
|
49. NumPy Level 9.mp4
[4d079bb031fc6a43]
|
12,978,049 |
DF82CE3C |
|
50. Pandas data analysis level 1.mp4
[fc18585fee01301f]
|
14,891,919 |
9C713704 |
|
51. Pandas data analysis level 2.mp4
[d803b1af4bcbc2c8]
|
19,069,679 |
A2E46945 |
|
52. Pandas data analysis level 3.mp4
[12403769f77cb258]
|
10,393,032 |
E254550B |
|
53. Pandas data analysis level 4.mp4
[af3e7bbc3aaa38fb]
|
16,636,482 |
70881C41 |
|
54. Pandas data analysis level 5.mp4
[b7d0959dfd7283aa]
|
16,570,088 |
6E49DF6B |
|
55. Pandas data analysis level 6.mp4
[786375bd5419ec5d]
|
21,779,437 |
18EB5B39 |
|
56. Matplotlib data visualization level 1.mp4
[b0d57cdec65e7ed3]
|
12,969,217 |
30E275E6 |
|
57. Matplotlib data visualization level 2.mp4
[d972c735afaee882]
|
5,446,315 |
10CC746B |
|
58. Matplotlib data visualization level 3.mp4
[7aa09f770b87f7a4]
|
13,912,012 |
BC64B15B |
|
59. Matplotlib data visualization level 4.mp4
[868f969617767dc5]
|
14,268,081 |
6996AB21 |
|
60. Matplotlib data visualization level 5.mp4
[ce2fb33e36bdbbd]
|
7,337,325 |
0AAE0DF5 |
|
61. Matplotlib data visualization level 6.mp4
[f7efdeb9dfc7dc77]
|
12,146,565 |
96F9C072 |
|
62. Matplotlib data visualization level 7.mp4
[78f5a61c59f4d3fd]
|
11,243,745 |
26956401 |
|
63. Seaborn statistical graphs level 1.mp4
[25e439b4d4450d5a]
|
13,019,574 |
EB1F88B5 |
|
64. Seaborn statistical graphs level 2.mp4
[ace6ade6fe66fc26]
|
5,346,644 |
5699233D |
|
65. Seaborn statistical graphs level 3.mp4
[250d82056f36bdf1]
|
4,816,197 |
F348A36B |
|
66. Seaborn statistical graphs level 4.mp4
[7e976250464c7015]
|
7,075,946 |
57EAB25C |
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67. Seaborn statistical graphs level 5.mp4
[4b586fa855c47d02]
|
11,337,093 |
EE3A36E1 |
|
68. Seaborn statistical graphs level 6.mp4
[a9cf5ffa7fd2968b]
|
9,207,363 |
27EA261A |
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69. Seaborn statistical graphs level 7.mp4
[8ce29fb701d2fbe7]
|
6,564,939 |
39656137 |
|
71. Python Programming.mp4
[8f5a4c0ac8e4e7c9]
|
8,329,431 |
48F8E9E9 |
|
72. NumPy.mp4
[b6fa6d3ba2c6f7b7]
|
2,098,647 |
E44B3DC2 |
|
73. Pandas.mp4
[dea0c4e4fe9612c0]
|
3,137,714 |
C4B1AA8C |
|
74. Matplotlib.mp4
[cc37c2630486c632]
|
5,586,140 |
470B914D |
|
75. Seaborn.mp4
[d681cc8cd933e609]
|
2,836,668 |
A889E6BD |
|
1. Welcome to Data Science Python for Data Analysis 2022 Full Bootcamp.mp4
[61c26c78288d6174]
|
36,460,815 |
AA452951 |
|
2. Download and Install the working tools.mp4
[ffe6d5ed85d10805]
|
9,811,638 |
D4232C67 |
|
3. Jupyter Overview + Markdown in Jupyter tutorial.mp4
[ef756bd65d4ab1bf]
|
28,287,910 |
CFB29417 |
|
4. Using Jupyter Notebook for coding with Python.mp4
[da87df3b2335e6ac]
|
30,663,173 |
A4D961B3 |
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| Total size: |
1,657,980,313 |
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