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1 - Case study 1 - Finding the winning strategy in a card game.mp4	6.89 MB
10 - Chapter 3. Using permutations to shuffle cards.mp4	35.40 MB
100 - Chapter 20. Network-driven supervised machine learning.mp4	48.95 MB
101 - Chapter 20. The basics of supervised machine learning.mp4	49.20 MB
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4	37.28 MB
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4	55.24 MB
104 - Chapter 20. Optimizing KNN performance.mp4	35.68 MB
105 - Chapter 20. Running a grid search using scikit-learn.mp4	39.33 MB
106 - Chapter 20. Limitations of the KNN algorithm.mp4	63.16 MB
107 - Chapter 21. Training linear classifiers with logistic regression.mp4	58.26 MB
108 - Chapter 21. Training a linear classifier, Part 1.mp4	43.52 MB
109 - Chapter 21. Training a linear classifier, Part 2.mp4	73.26 MB
11 - Chapter 4. Case study 1 solution.mp4	34.27 MB
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4	43.42 MB
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4	43.12 MB
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4	49.64 MB
113 - Chapter 21. Measuring feature importance with coefficients.mp4	93.13 MB
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4	65.20 MB
115 - Chapter 22. Training a nested if_else model using two features.mp4	53.25 MB
116 - Chapter 22. Deciding which feature to split on.mp4	57.23 MB
117 - Chapter 22. Training if_else models with more than two features.mp4	57.79 MB
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4	51.86 MB
119 - Chapter 22. Studying cancerous cells using feature importance.mp4	59.29 MB
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4	47.10 MB
120 - Chapter 22. Improving performance using random forest classification.mp4	57.38 MB
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4	52.96 MB
122 - Chapter 23. Case study 5 solution.mp4	32.94 MB
123 - Chapter 23. Exploring the experimental observations.mp4	38.99 MB
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4	52.59 MB
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4	53.87 MB
126 - Chapter 23. Adding profile features to the model.mp4	62.03 MB
127 - Chapter 23. Optimizing performance across a steady set of features.mp4	42.55 MB
128 - Chapter 23. Interpreting the trained model.mp4	64.17 MB
13 - Case study 2 - Assessing online ad clicks for significance.mp4	31.40 MB
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4	76.23 MB
15 - Chapter 5. Mean as a measure of centrality.mp4	36.58 MB
16 - Chapter 5. Variance as a measure of dispersion.mp4	73.89 MB
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4	58.61 MB
18 - Chapter 6. Comparing two sampled normal curves.mp4	31.46 MB
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4	55.19 MB
2 - Chapter 1. Computing probabilities using Python This section covers.mp4	56.75 MB
20 - Chapter 6. Computing the area beneath a normal curve.mp4	64.57 MB
21 - Chapter 7. Statistical hypothesis testing.mp4	39.19 MB
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4	68.30 MB
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4	79.88 MB
24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4	53.28 MB
25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4	52.78 MB
26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4	43.69 MB
27 - Chapter 8. Analyzing tables using Pandas.mp4	40.87 MB
28 - Chapter 8. Retrieving table rows.mp4	38.24 MB
29 - Chapter 8. Saving and loading table data.mp4	40.28 MB
3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4	60.89 MB
30 - Chapter 9. Case study 2 solution.mp4	33.60 MB
31 - Chapter 9. Determining statistical significance.mp4	43.58 MB
32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4	6.60 MB
33 - Chapter 10. Clustering data into groups.mp4	61.40 MB
34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4	61.20 MB
35 - Chapter 10. Using density to discover clusters.mp4	52.23 MB
36 - Chapter 10. Clustering based on non-Euclidean distance.mp4	68.79 MB
37 - Chapter 10. Analyzing clusters using Pandas.mp4	40.48 MB
38 - Chapter 11. Geographic location visualization and analysis.mp4	46.58 MB
39 - Chapter 11. Plotting maps using Cartopy.mp4	33.23 MB
4 - Chapter 2. Plotting probabilities using Matplotlib.mp4	53.74 MB
40 - Chapter 11. Visualizing maps.mp4	58.27 MB
41 - Chapter 11. Location tracking using GeoNamesCache.mp4	62.35 MB
42 - Chapter 11. Limitations of the GeoNamesCache library.mp4	69.19 MB
43 - Chapter 12. Case study 3 solution.mp4	34.63 MB
44 - Chapter 12. Visualizing and clustering the extracted location data.mp4	70.72 MB
45 - Case study 4 - Using online job postings to improve your data science resume.mp4	23.95 MB
46 - Chapter 13. Measuring text similarities.mp4	36.28 MB
47 - Chapter 13. Simple text comparison.mp4	44.00 MB
48 - Chapter 13. Replacing words with numeric values.mp4	42.07 MB
49 - Chapter 13. Vectorizing texts using word counts.mp4	44.50 MB
5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4	65.57 MB
50 - Chapter 13. Using normalization to improve TF vector similarity.mp4	48.56 MB
51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4	41.64 MB
52 - Chapter 13. Basic matrix operations, Part 1.mp4	48.78 MB
53 - Chapter 13. Basic matrix operations, Part 2.mp4	27.15 MB
54 - Chapter 13. Computational limits of matrix multiplication.mp4	47.81 MB
55 - Chapter 14. Dimension reduction of matrix data.mp4	61.74 MB
56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4	38.99 MB
57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4	37.56 MB
58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4	64.72 MB
59 - Chapter 14. Clustering 4D data in two dimensions.mp4	54.44 MB
6 - Chapter 3. Running random simulations in NumPy.mp4	36.35 MB
60 - Chapter 14. Limitations of PCA.mp4	30.77 MB
61 - Chapter 14. Computing principal components without rotation.mp4	47.80 MB
62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4	44.67 MB
63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4	34.38 MB
64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4	68.60 MB
65 - Chapter 15. NLP analysis of large text datasets.mp4	47.16 MB
66 - Chapter 15. Vectorizing documents using scikit-learn.mp4	87.06 MB
67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4	56.59 MB
68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4	48.13 MB
69 - Chapter 15. Computing similarities across large document datasets.mp4	60.24 MB
7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4	47.59 MB
70 - Chapter 15. Clustering texts by topic, Part 1.mp4	73.30 MB
71 - Chapter 15. Clustering texts by topic, Part 2.mp4	87.08 MB
72 - Chapter 15. Visualizing text clusters.mp4	58.90 MB
73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4	50.57 MB
74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4	58.83 MB
75 - Chapter 16. Extracting text from web pages.mp4	39.55 MB
76 - Chapter 16. The structure of HTML documents.mp4	62.95 MB
77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4	40.42 MB
78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4	46.78 MB
79 - Chapter 17. Case study 4 solution.mp4	37.42 MB
8 - Chapter 3. Deriving probabilities from histograms.mp4	57.63 MB
80 - Chapter 17. Exploring the HTML for skill descriptions.mp4	59.65 MB
81 - Chapter 17. Filtering jobs by relevance.mp4	73.18 MB
82 - Chapter 17. Clustering skills in relevant job postings.mp4	66.54 MB
83 - Chapter 17. Investigating the technical skill clusters.mp4	41.46 MB
84 - Chapter 17. Exploring clusters at alternative values of K.mp4	69.37 MB
85 - Chapter 17. Analyzing the 700 most relevant postings.mp4	40.95 MB
86 - Case study 5 - Predicting future friendships from social network data.mp4	80.40 MB
87 - Chapter 18. An introduction to graph theory and network analysis.mp4	74.88 MB
88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4	30.92 MB
89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4	53.06 MB
9 - Chapter 3. Computing histograms in NumPy.mp4	52.99 MB
90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4	57.39 MB
91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4	32.12 MB
92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4	49.04 MB
93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4	75.08 MB
94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4	40.21 MB
95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4	48.36 MB
96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4	44.66 MB
97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4	60.05 MB
98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4	75.21 MB
99 - Chapter 19. Uncovering friend groups in social networks.mp4	57.99 MB

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Description

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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