Udemy - Python for Data Science

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 3.0 GB
  • Uploaded By tutsnode
  • Downloads 211
  • Last checked 3 weeks ago
  • Date uploaded 5 years ago
  • Seeders 16
  • Leechers 9

Infohash : 0D3F02BD2DB5F3531AD36B6D07B62CB39CD6BED3




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

Files:

Python for Data Science [TutsNode.com] - Python for Data Science 2. Python Fundamentals & NumPy Package
  • 4. Segment - 23-lab-tutorials-learning-juypter-notebook.mp4 (162.7 MB)
  • 1. Segment - 20-introduction-to-python-notebook.mp4 (62.9 MB)
  • 1. Segment - 20-introduction-to-python-notebook.srt (12.4 KB)
  • 2. Segment - 21-git-and-repl.mp4 (36.1 MB)
  • 2. Segment - 21-git-and-repl.srt (3.1 KB)
  • 3. Segment - 22-introduction-ids-and-juypter-notebook.mp4 (97.2 MB)
  • 3. Segment - 22-introduction-ids-and-juypter-notebook.srt (16.3 KB)
  • 4. Segment - 23-lab-tutorials-learning-juypter-notebook.srt (26.4 KB)
  • 5. Segment - 24-python-loops-and-functions.mp4 (77.8 MB)
  • 5. Segment - 24-python-loops-and-functions.srt (16.8 KB)
  • 6. Segment - 25-python-objects-introduction.mp4 (49.4 MB)
  • 6. Segment - 25-python-objects-introduction.srt (8.8 KB)
  • 7. Segment - 26-python-numpy.mp4 (32.4 MB)
  • 7. Segment - 26-python-numpy.srt (6.3 KB)
  • 8. Segment - 27-arrays.mp4 (125.0 MB)
  • 8. Segment - 27-arrays.srt (24.0 KB)
  • 9. Segment - 28-advanced-arrays.mp4 (88.6 MB)
  • 9. Segment - 28-advanced-arrays.srt (17.5 KB)
  • 10. Segment - 29-matrices.mp4 (53.4 MB)
  • 10. Segment - 29-matrices.srt (9.5 KB)
1. Introduction to Machine Learning and It’s Technologies
  • 1. Segment - 02-introduction-to-data-science-fin.mp4 (78.4 MB)
  • 1. Segment - 02-introduction-to-data-science-fin.srt (15.0 KB)
  • 2. Segment - 03.mp4 (13.8 MB)
  • 2. Segment - 03.srt (3.0 KB)
  • 3. Segment - 04-doing-data-science.mp4 (26.2 MB)
  • 3. Segment - 04-doing-data-science.srt (5.3 KB)
  • 4. Segment - 05-problem-definitions-and-collecting-data.mp4 (11.2 MB)
  • 4. Segment - 05-problem-definitions-and-collecting-data.srt (2.6 KB)
  • 5. Segment - 06-data-pipelines-preparation-cleaning-understanding.mp4 (22.3 MB)
  • 5. Segment - 06-data-pipelines-preparation-cleaning-understanding.srt (4.4 KB)
  • 6. Segment - 07-model-building-validation-visualization-data-science-applications.mp4 (42.1 MB)
  • 6. Segment - 07-model-building-validation-visualization-data-science-applications.srt (9.7 KB)
  • 7. Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools.mp4 (37.9 MB)
  • 7. Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools.srt (7.5 KB)
  • 8. Segment - 09-data-science-future-readings.mp4 (35.9 MB)
  • 8. Segment - 09-data-science-future-readings.srt (5.8 KB)
  • 9. Segment - 10-ai-primer-and-machine-learning-concepts.mp4 (50.6 MB)
  • 9. Segment - 10-ai-primer-and-machine-learning-concepts.srt (9.9 KB)
  • 10. Segment - 11-machine-learning-applications.mp4 (67.2 MB)
  • 10. Segment - 11-machine-learning-applications.srt (12.3 KB)
  • 11. Segment - 12-machine-learning-supervised-unsupervised.mp4 (21.2 MB)
  • 11. Segment - 12-machine-learning-supervised-unsupervised.srt (4.6 KB)
  • 12. Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX.mp4 (45.6 MB)
  • 12. Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX.srt (7.8 KB)
  • 13. Segment - 13-supervised-unsupervised-learning-methodology-clustering.mp4 (31.6 MB)
  • 13. Segment - 13-supervised-unsupervised-learning-methodology-clustering.srt (6.1 KB)
  • 14. Segment - 14-python-vs-r.mp4 (19.2 MB)
  • 14. Segment - 14-python-vs-r.srt (3.8 KB)
  • 15. Segment - 15-tools-for-scalable-machine-learning.mp4 (32.4 MB)
  • 15. Segment - 15-tools-for-scalable-machine-learning.srt (6.4 KB)
  • 16. Segment - 16-introduction-to-python.mp4 (58.1 MB)
  • 16. Segment - 16-introduction-to-python.srt (12.5 KB)
  • 17. Segment - 17-more-python-details.mp4 (50.5 MB)
  • 17. Segment - 17-more-python-details.srt (10.8 KB)
  • 18. Segment - 18-python-examples.mp4 (45.8 MB)
  • 18. Segment - 18-python-examples.srt (10.2 KB)
  • 19. Segment - 19-anaconda-navigator.mp4 (43.0 MB)
  • 19. Segment - 19-anaconda-navigator.srt (10.9 KB)
3. Data Analysis using Pandas and Data Visualization
  • 1. Segment - 30-numpy-lab-tutorial.mp4 (140.5 MB)
  • 1. Segment - 30-numpy-lab-tutorial.srt (23.1 KB)
  • 2. Segment 31 -review-session-python-for-data-science.mp4 (39.1 MB)
  • 2. Segment 31 -review-session-python-for-data-science.srt (8.9 KB)
  • 3. Segment 32 - Why Pandas.mp4 (10.6 MB)
  • 3. Segment 32 - Why Pandas.srt (2.1 KB)
  • 4. Segment 33 - Data Series.mp4 (44.2 MB)
  • 4. Segment 33 - Data Series.srt (8.8 KB)
  • 5. Segment 34 - Series, Keys and Indices.mp4 (41.6 MB)
  • 5. Segment 34 - Series, Keys and Indices.srt (8.5 KB)
  • 6. Segment 35 - NumPy Array vs. Panda Series.mp4 (15.9 MB)
  • 6. Segment 35 - NumPy Array vs. Panda Series.srt (2.8 KB)
  • 7. Segment 36 - Dataframe.mp4 (34.0 MB)
  • 7. Segment 36 - Dataframe.srt (7.3 KB)
  • 8. Segment 37 - Dataframe Operations.mp4 (15.0 MB)
  • 8. Segment 37 - Dataframe Operations.srt (3.5 KB)
  • 9. Segment 38 - Using Lambda.mp4 (19.4 MB)
  • 9. Segment 38 - Using Lambda.srt (4.5 KB)
  • 10. Segment 39 - Dataframe Operations (Continued).mp4 (70.9 MB)
  • 10. Segment 39 - Dataframe Operations (Continued).srt (14.7 KB)
  • 11. Segment 40 - Statistical Analysis, Calculations and Operations.mp4 (106.9 MB)
  • 11. Segment 40 - Statistical Analysis, Calculations and Operations.srt (20.7 KB)
  • 12. Segment 41 - Lab - Advanced Operations in Action.mp4 (55.2 MB)
  • 12. Segment 41 - Lab - Advanced Operations in Action.srt (7.3 KB)
  • 13. Segment 42 - Lab - Advanced Operations in Action (Continued).mp4 (83.9 MB)
  • 13. Segment 42 - Lab - Advanced Operations in Action (Continued).srt (11.1 KB)
  • 14. Segment 43 - Pandas Visualization and Matplotlib.mp4 (68.3 MB)
  • 14. Segment 43 - Pandas Visualization and Matplotlib.srt (14.1 KB)
  • 15. Segment 44 - Seaborn.mp4 (16.7 MB)
  • 15. Segment 44 - Seaborn.srt (3.1 KB)
  • 16. Segment 45 - ggplot.mp4 (12.9 MB)
  • 16. Segment 45 - ggplot.srt (2.6 KB)
  • 17. Segment 46 - Statistical Graphs.mp4 (14.0 MB)
  • 17. Segment 46 - Statistical Graphs.srt (2.6 KB)
  • 18. Segment 47 - Lab - Visualizations.mp4 (79.4 MB)
  • 18. Segment 47 - Lab - Visualizations.srt (12.0 KB)
4. Supervised (Regression and Classification) & Unsupervised (Clustering) Machine L
  • 1. Segment 48 - Introduction to Scikit-Learn.mp4 (51.0 MB)
  • 1. Segment 48 - Introduction to Scikit-Learn.srt (12.5 KB)
  • 2. Segment 49 - Scikit-Learn Uses and Applications.mp4 (69.3 MB)
  • 2. Segment 49 - Scikit-Learn Uses and Applications.srt (11.6 KB)
  • 3. Segment 50 - Scikit-Learn vs. Other Tools.mp4 (18.1 MB)
  • 3. Segment 50 - Scikit-Learn vs. Other Tools.srt (3.4 KB)
  • 4. Segment 51 - Scikit-Learn Classes, Utils and Data Sets.mp4 (54.1 MB)
  • 4. Segment 51 - Scikit-Learn Classes, Utils and Data Sets.srt (12.0 KB)
  • 5. Segment 51 - Setting Up Scikit-Learn.mp4 (8.6 MB)
  • 5. Segment 51 - Setting Up Scikit-Learn.srt (2.1 KB)
  • 6. Segment 52 - Estimators and Algorithms.mp4 (88.2 MB)
  • 6. Segment 52 - Estimators and Algorithms.srt (17.2 KB)
  • 7. Segment 53 - Preprocessing and Feature Engineering.mp4 (75.5 MB)
  • 7. Segment 53 - Preprocessing and Feature Engineering.srt (18.0 KB)
  • 8. Segment 54 - Metrics.mp4 (73.1 MB)
  • 8. Segment 54 - Metrics.srt (18.3 KB)
  • 9. Segment 55 - Clustering.mp4 (39.5 MB)
  • 9. Segment 55 - Clustering.srt (7.7 KB)
  • 10. Segment 56 - Prediction.mp4 (40.7 MB)
  • 10. Segment 56 - Prediction.srt (9.1 KB)
  • 11. Segment 57 - Principal Component Analysis.mp4 (43.2 MB)
  • 11. Segment 57 - Principal Component Analysis.srt (9.8 KB)
  • 12. Segment 58 - Lab - Classification Algorithm.mp4 (151.5 MB)
  • 12. Segment 58 - Lab - Classification Algorithm.srt (25.2 KB)
5. Quizzes
  • 1. Quiz 1.html (0.1 KB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)

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