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Description
This Python for Data Science course is an introduction to Python and how to apply it in data science. The course contains ~60 lectures and 7.5 hours of content taught by Praba Santanakrishnan, a highly experienced data scientist from Microsoft.
Staring with some fundamentals about “what is data science,” and “who is a data scientist,” the program rapidly move into the specific challenges of data science. This includes the challenges of problem definitions and collecting data, to data pipelines, data preparation, data cleaning and related subjects. Data science methodologies, data analytics tools and open source tools are all covered. Model building validation, visualization and various data science applications are also covered. Discussion of the types of machine learning are covered, including supervised and unsupervised machine learning, as well as methodologies and clustering. NumPy, Pandas, Python Notebook, Git, REPL, IDS and Jupyter Notebook are also covered. Arrays, advanced arrays, and matrices are discussed in some detail to ensure you understand what it is all about and how these tools are implemented.
Who this course is for:
New Python developers looking to quickly develop and keen understanding of the power of Python
Early stage users of Python who need to use Python in serious, enterprise level applications
Individuals who are familiar with data science and need to understand the optimal uses for Python
Requirements
Basic Python knowledge is assumed
Some software development experience (including languages, databases…)
Last Updated 4/2020
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Python for Data Science
[TutsNode.com] - Python for Data Science
2. Python Fundamentals & NumPy Package
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4. Segment - 23-lab-tutorials-learning-juypter-notebook.mp4 (162.7 MB)
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1. Segment - 20-introduction-to-python-notebook.mp4 (62.9 MB)
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1. Segment - 20-introduction-to-python-notebook.srt (12.4 KB)
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2. Segment - 21-git-and-repl.mp4 (36.1 MB)
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2. Segment - 21-git-and-repl.srt (3.1 KB)
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3. Segment - 22-introduction-ids-and-juypter-notebook.mp4 (97.2 MB)
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3. Segment - 22-introduction-ids-and-juypter-notebook.srt (16.3 KB)
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4. Segment - 23-lab-tutorials-learning-juypter-notebook.srt (26.4 KB)
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5. Segment - 24-python-loops-and-functions.mp4 (77.8 MB)
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5. Segment - 24-python-loops-and-functions.srt (16.8 KB)
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6. Segment - 25-python-objects-introduction.mp4 (49.4 MB)
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6. Segment - 25-python-objects-introduction.srt (8.8 KB)
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7. Segment - 26-python-numpy.mp4 (32.4 MB)
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7. Segment - 26-python-numpy.srt (6.3 KB)
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8. Segment - 27-arrays.mp4 (125.0 MB)
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8. Segment - 27-arrays.srt (24.0 KB)
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9. Segment - 28-advanced-arrays.mp4 (88.6 MB)
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9. Segment - 28-advanced-arrays.srt (17.5 KB)
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10. Segment - 29-matrices.mp4 (53.4 MB)
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10. Segment - 29-matrices.srt (9.5 KB)
1. Introduction to Machine Learning and It’s Technologies
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1. Segment - 02-introduction-to-data-science-fin.mp4 (78.4 MB)
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1. Segment - 02-introduction-to-data-science-fin.srt (15.0 KB)
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2. Segment - 03.mp4 (13.8 MB)
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2. Segment - 03.srt (3.0 KB)
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3. Segment - 04-doing-data-science.mp4 (26.2 MB)
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3. Segment - 04-doing-data-science.srt (5.3 KB)
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4. Segment - 05-problem-definitions-and-collecting-data.mp4 (11.2 MB)
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4. Segment - 05-problem-definitions-and-collecting-data.srt (2.6 KB)
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5. Segment - 06-data-pipelines-preparation-cleaning-understanding.mp4 (22.3 MB)
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5. Segment - 06-data-pipelines-preparation-cleaning-understanding.srt (4.4 KB)
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6. Segment - 07-model-building-validation-visualization-data-science-applications.mp4 (42.1 MB)
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6. Segment - 07-model-building-validation-visualization-data-science-applications.srt (9.7 KB)
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7. Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools.mp4 (37.9 MB)
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7. Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools.srt (7.5 KB)
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8. Segment - 09-data-science-future-readings.mp4 (35.9 MB)
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8. Segment - 09-data-science-future-readings.srt (5.8 KB)
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9. Segment - 10-ai-primer-and-machine-learning-concepts.mp4 (50.6 MB)
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9. Segment - 10-ai-primer-and-machine-learning-concepts.srt (9.9 KB)
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10. Segment - 11-machine-learning-applications.mp4 (67.2 MB)
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10. Segment - 11-machine-learning-applications.srt (12.3 KB)
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11. Segment - 12-machine-learning-supervised-unsupervised.mp4 (21.2 MB)
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11. Segment - 12-machine-learning-supervised-unsupervised.srt (4.6 KB)
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12. Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX.mp4 (45.6 MB)
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12. Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX.srt (7.8 KB)
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13. Segment - 13-supervised-unsupervised-learning-methodology-clustering.mp4 (31.6 MB)
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13. Segment - 13-supervised-unsupervised-learning-methodology-clustering.srt (6.1 KB)
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14. Segment - 14-python-vs-r.mp4 (19.2 MB)
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14. Segment - 14-python-vs-r.srt (3.8 KB)
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15. Segment - 15-tools-for-scalable-machine-learning.mp4 (32.4 MB)
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15. Segment - 15-tools-for-scalable-machine-learning.srt (6.4 KB)
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16. Segment - 16-introduction-to-python.mp4 (58.1 MB)
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16. Segment - 16-introduction-to-python.srt (12.5 KB)
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17. Segment - 17-more-python-details.mp4 (50.5 MB)
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17. Segment - 17-more-python-details.srt (10.8 KB)
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18. Segment - 18-python-examples.mp4 (45.8 MB)
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18. Segment - 18-python-examples.srt (10.2 KB)
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19. Segment - 19-anaconda-navigator.mp4 (43.0 MB)
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19. Segment - 19-anaconda-navigator.srt (10.9 KB)
3. Data Analysis using Pandas and Data Visualization
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1. Segment - 30-numpy-lab-tutorial.mp4 (140.5 MB)
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1. Segment - 30-numpy-lab-tutorial.srt (23.1 KB)
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2. Segment 31 -review-session-python-for-data-science.mp4 (39.1 MB)
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2. Segment 31 -review-session-python-for-data-science.srt (8.9 KB)
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3. Segment 32 - Why Pandas.mp4 (10.6 MB)
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3. Segment 32 - Why Pandas.srt (2.1 KB)
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4. Segment 33 - Data Series.mp4 (44.2 MB)
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4. Segment 33 - Data Series.srt (8.8 KB)
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5. Segment 34 - Series, Keys and Indices.mp4 (41.6 MB)
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5. Segment 34 - Series, Keys and Indices.srt (8.5 KB)
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6. Segment 35 - NumPy Array vs. Panda Series.mp4 (15.9 MB)
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6. Segment 35 - NumPy Array vs. Panda Series.srt (2.8 KB)
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7. Segment 36 - Dataframe.mp4 (34.0 MB)
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7. Segment 36 - Dataframe.srt (7.3 KB)
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8. Segment 37 - Dataframe Operations.mp4 (15.0 MB)
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8. Segment 37 - Dataframe Operations.srt (3.5 KB)
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9. Segment 38 - Using Lambda.mp4 (19.4 MB)
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9. Segment 38 - Using Lambda.srt (4.5 KB)
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10. Segment 39 - Dataframe Operations (Continued).mp4 (70.9 MB)
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10. Segment 39 - Dataframe Operations (Continued).srt (14.7 KB)
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11. Segment 40 - Statistical Analysis, Calculations and Operations.mp4 (106.9 MB)
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11. Segment 40 - Statistical Analysis, Calculations and Operations.srt (20.7 KB)
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12. Segment 41 - Lab - Advanced Operations in Action.mp4 (55.2 MB)
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12. Segment 41 - Lab - Advanced Operations in Action.srt (7.3 KB)
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13. Segment 42 - Lab - Advanced Operations in Action (Continued).mp4 (83.9 MB)
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13. Segment 42 - Lab - Advanced Operations in Action (Continued).srt (11.1 KB)
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14. Segment 43 - Pandas Visualization and Matplotlib.mp4 (68.3 MB)
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14. Segment 43 - Pandas Visualization and Matplotlib.srt (14.1 KB)
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15. Segment 44 - Seaborn.mp4 (16.7 MB)
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15. Segment 44 - Seaborn.srt (3.1 KB)
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16. Segment 45 - ggplot.mp4 (12.9 MB)
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16. Segment 45 - ggplot.srt (2.6 KB)
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17. Segment 46 - Statistical Graphs.mp4 (14.0 MB)
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17. Segment 46 - Statistical Graphs.srt (2.6 KB)
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18. Segment 47 - Lab - Visualizations.mp4 (79.4 MB)
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18. Segment 47 - Lab - Visualizations.srt (12.0 KB)
4. Supervised (Regression and Classification) & Unsupervised (Clustering) Machine L
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