O’REILLY | Data Science Bookcamp, Video Edition [FCO]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size6.4 GB
  • Uploaded BySunRiseZone
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  • Last checkedMar. 12th '22
  • Date uploadedMar. 10th '22
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Author : Leonard Apeltsin
Language : English
Released : November 2021
Publisher(s) : Manning Publications
Duration : 18h 11m
Course Source : https://www.oreilly.com/videos/data-science-bookcamp/9781617296253VE/

Video Description

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Valuable and accessible… a solid foundation for anyone aspiring to be a data scientist.
Amaresh Rajasekharan, IBM Corporation

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science.

In Data Science Bookcamp you will find:

• Techniques for computing and plotting probabilities
• Statistical analysis using Scipy
• How to organize datasets with clustering algorithms
• How to visualize complex multi-variable datasets
• How to train a decision tree machine learning algorithm

In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career.

About the technology
A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data.

About the book
Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results.

About the audience
For readers who know the basics of Python. No prior data science or machine learning skills required.

About the author
Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse.

Really good introduction of statistical data science concepts. A must-have for every beginner!
Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland

A full-fledged tutorial in data science including common Python libraries and language tricks!
Jean-François Morin, Laval University

This book is a complete package for understanding how the data science process works end to end.
Ayon Roy, Internshala

Files:

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  • 1 - Case study 1 - Finding the winning strategy in a card game.mp4 (6.9 MB)
  • 10 - Chapter 3. Using permutations to shuffle cards.mp4 (35.4 MB)
  • 100 - Chapter 20. Network-driven supervised machine learning.mp4 (49.0 MB)
  • 101 - Chapter 20. The basics of supervised machine learning.mp4 (49.2 MB)
  • 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 (37.3 MB)
  • 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 (55.2 MB)
  • 104 - Chapter 20. Optimizing KNN performance.mp4 (35.7 MB)
  • 105 - Chapter 20. Running a grid search using scikit-learn.mp4 (39.3 MB)
  • 106 - Chapter 20. Limitations of the KNN algorithm.mp4 (63.2 MB)
  • 107 - Chapter 21. Training linear classifiers with logistic regression.mp4 (58.3 MB)
  • 108 - Chapter 21. Training a linear classifier, Part 1.mp4 (43.5 MB)
  • 109 - Chapter 21. Training a linear classifier, Part 2.mp4 (73.3 MB)
  • 11 - Chapter 4. Case study 1 solution.mp4 (34.3 MB)
  • 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 (43.4 MB)
  • 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 (43.1 MB)
  • 112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 (49.6 MB)
  • 113 - Chapter 21. Measuring feature importance with coefficients.mp4 (93.1 MB)
  • 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 (65.2 MB)
  • 115 - Chapter 22. Training a nested if_else model using two features.mp4 (53.3 MB)
  • 116 - Chapter 22. Deciding which feature to split on.mp4 (57.2 MB)
  • 117 - Chapter 22. Training if_else models with more than two features.mp4 (57.8 MB)
  • 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 (51.9 MB)
  • 119 - Chapter 22. Studying cancerous cells using feature importance.mp4 (59.3 MB)
  • 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 (47.1 MB)
  • 120 - Chapter 22. Improving performance using random forest classification.mp4 (57.4 MB)
  • 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 (53.0 MB)
  • 122 - Chapter 23. Case study 5 solution.mp4 (32.9 MB)
  • 123 - Chapter 23. Exploring the experimental observations.mp4 (39.0 MB)
  • 124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 (52.6 MB)
  • 125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 (53.9 MB)
  • 126 - Chapter 23. Adding profile features to the model.mp4 (62.0 MB)
  • 127 - Chapter 23. Optimizing performance across a steady set of features.mp4 (42.6 MB)
  • 128 - Chapter 23. Interpreting the trained model.mp4 (64.2 MB)
  • 13 - Case study 2 - Assessing online ad clicks for significance.mp4 (31.4 MB)
  • 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 (76.2 MB)
  • 15 - Chapter 5. Mean as a measure of centrality.mp4 (36.6 MB)
  • 16 - Chapter 5. Variance as a measure of dispersion.mp4 (73.9 MB)
  • 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 (58.6 MB)
  • 18 - Chapter 6. Comparing two sampled normal curves.mp4 (31.5 MB)
  • 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 (55.2 MB)
  • 2 - Chapter 1. Computing probabilities using Python This section covers.mp4 (56.8 MB)
  • 20 - Chapter 6. Computing the area beneath a normal curve.mp4 (64.6 MB)
  • 21 - Chapter 7. Statistical hypothesis testing.mp4 (39.2 MB)
  • 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 (68.3 MB)
  • 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 (79.9 MB)
  • 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 (53.3 MB)
  • 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 (52.8 MB)
  • 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4 (43.7 MB)
  • 27 - Chapter 8. Analyzing tables using Pandas.mp4 (40.9 MB)
  • 28 - Chapter 8. Retrieving table rows.mp4 (38.2 MB)
  • 29 - Chapter 8. Saving and loading table data.mp4 (40.3 MB)
  • 3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 (60.9 MB)
  • 30 - Chapter 9. Case study 2 solution.mp4 (33.6 MB)
  • 31 - Chapter 9. Determining statistical significance.mp4 (43.6 MB)
  • 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4 (6.6 MB)
  • 33 - Chapter 10. Clustering data into groups.mp4 (61.4 MB)
  • 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 (61.2 MB)
  • 35 - Chapter 10. Using density to discover clusters.mp4 (52.2 MB)
  • 36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 (68.8 MB)
  • 37 - Chapter 10. Analyzing clusters using Pandas.mp4 (40.5 MB)
  • 38 - Chapter 11. Geographic location visualization and analysis.mp4 (46.6 MB)
  • 39 - Chapter 11. Plotting maps using Cartopy.mp4 (33.2 MB)
  • 4 - Chapter 2. Plotting probabilities using Matplotlib.mp4 (53.7 MB)
  • 40 - Chapter 11. Visualizing maps.mp4 (58.3 MB)
  • 41 - Chapter 11. Location tracking using GeoNamesCache.mp4 (62.3 MB)
  • 42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 (69.2 MB)
  • 43 - Chapter 12. Case study 3 solution.mp4 (34.6 MB)
  • 44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 (70.7 MB)
  • 45 - Case study 4 - Using online job postings to improve your data science resume.mp4 (23.9 MB)
  • 46 - Chapter 13. Measuring text similarities.mp4 (36.3 MB)
  • 47 - Chapter 13. Simple text comparison.mp4 (44.0 MB)
  • 48 - Chapter 13. Replacing words with numeric values.mp4 (42.1 MB)
  • 49 - Chapter 13. Vectorizing texts using word counts.mp4 (44.5 MB)
  • 5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 (65.6 MB)
  • 50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 (48.6 MB)
  • 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4 (41.6 MB)
  • 52 - Chapter 13. Basic matrix operations, Part 1.mp4 (48.8 MB)
  • 53 - Chapter 1

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