Data Science Bookcamp, video edition
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18:10:38
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1 - Case study 1 - Finding the winning strategy in a card game.mp4
01:46
2 - Chapter 1. Computing probabilities using Python This section covers.mp4
10:24
3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4
11:06
4 - Chapter 2. Plotting probabilities using Matplotlib.mp4
11:05
5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4
10:59
6 - Chapter 3. Running random simulations in NumPy.mp4
07:11
7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4
09:31
8 - Chapter 3. Deriving probabilities from histograms.mp4
10:05
9 - Chapter 3. Computing histograms in NumPy.mp4
09:15
10 - Chapter 3. Using permutations to shuffle cards.mp4
06:36
11 - Chapter 4. Case study 1 solution.mp4
06:54
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4
06:28
13 - Case study 2 - Assessing online ad clicks for significance.mp4
05:09
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4
10:09
15 - Chapter 5. Mean as a measure of centrality.mp4
09:29
16 - Chapter 5. Variance as a measure of dispersion.mp4
12:13
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4
08:37
18 - Chapter 6. Comparing two sampled normal curves.mp4
06:53
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4
10:14
20 - Chapter 6. Computing the area beneath a normal curve.mp4
09:45
21 - Chapter 7. Statistical hypothesis testing.mp4
06:52
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4
07:35
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4
09:02
24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4
08:02
25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4
08:10
26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4
07:28
27 - Chapter 8. Analyzing tables using Pandas.mp4
10:37
28 - Chapter 8. Retrieving table rows.mp4
08:49
29 - Chapter 8. Saving and loading table data.mp4
07:16
30 - Chapter 9. Case study 2 solution.mp4
07:05
31 - Chapter 9. Determining statistical significance.mp4
06:24
32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4
01:38
33 - Chapter 10. Clustering data into groups.mp4
10:28
34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4
10:16
35 - Chapter 10. Using density to discover clusters.mp4
09:01
36 - Chapter 10. Clustering based on non-Euclidean distance.mp4
07:29
37 - Chapter 10. Analyzing clusters using Pandas.mp4
05:04
38 - Chapter 11. Geographic location visualization and analysis.mp4
08:15
39 - Chapter 11. Plotting maps using Cartopy.mp4
06:07
40 - Chapter 11. Visualizing maps.mp4
12:07
41 - Chapter 11. Location tracking using GeoNamesCache.mp4
11:06
42 - Chapter 11. Limitations of the GeoNamesCache library.mp4
12:04
43 - Chapter 12. Case study 3 solution.mp4
07:15
44 - Chapter 12. Visualizing and clustering the extracted location data.mp4
12:14
45 - Case study 4 - Using online job postings to improve your data science resume.mp4
04:28
46 - Chapter 13. Measuring text similarities.mp4
07:11
47 - Chapter 13. Simple text comparison.mp4
08:54
48 - Chapter 13. Replacing words with numeric values.mp4
08:24
49 - Chapter 13. Vectorizing texts using word counts.mp4
08:36
50 - Chapter 13. Using normalization to improve TF vector similarity.mp4
07:37
51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4
07:15
52 - Chapter 13. Basic matrix operations, Part 1.mp4
09:50
53 - Chapter 13. Basic matrix operations, Part 2.mp4
06:46
54 - Chapter 13. Computational limits of matrix multiplication.mp4
08:09
55 - Chapter 14. Dimension reduction of matrix data.mp4
09:28
56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4
07:14
57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4
06:15
58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4
11:56
59 - Chapter 14. Clustering 4D data in two dimensions.mp4
08:49
60 - Chapter 14. Limitations of PCA.mp4
05:54
61 - Chapter 14. Computing principal components without rotation.mp4
08:42
62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4
07:44
63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4
06:22
64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4
08:26
65 - Chapter 15. NLP analysis of large text datasets.mp4
08:12
66 - Chapter 15. Vectorizing documents using scikit-learn.mp4
12:09
67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4
08:54
68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4
08:24
69 - Chapter 15. Computing similarities across large document datasets.mp4
09:04
70 - Chapter 15. Clustering texts by topic, Part 1.mp4
09:53
71 - Chapter 15. Clustering texts by topic, Part 2.mp4
11:20
72 - Chapter 15. Visualizing text clusters.mp4
10:06
73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4
06:50
74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4
06:58
75 - Chapter 16. Extracting text from web pages.mp4
07:37
76 - Chapter 16. The structure of HTML documents.mp4
08:56
77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4
08:13
78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4
06:30
79 - Chapter 17. Case study 4 solution.mp4
06:18
80 - Chapter 17. Exploring the HTML for skill descriptions.mp4
07:52
81 - Chapter 17. Filtering jobs by relevance.mp4
11:58
82 - Chapter 17. Clustering skills in relevant job postings.mp4
10:53
83 - Chapter 17. Investigating the technical skill clusters.mp4
06:41
84 - Chapter 17. Exploring clusters at alternative values of K.mp4
07:36
85 - Chapter 17. Analyzing the 700 most relevant postings.mp4
06:35
86 - Case study 5 - Predicting future friendships from social network data.mp4
11:52
87 - Chapter 18. An introduction to graph theory and network analysis.mp4
10:03
88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4
07:59
89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4
08:10
90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4
10:55
91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4
06:02
92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4
07:08
93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4
11:44
94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4
06:28
95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4
07:25
96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4
06:40
97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4
11:07
98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4
12:06
99 - Chapter 19. Uncovering friend groups in social networks.mp4
07:36
100 - Chapter 20. Network-driven supervised machine learning.mp4
07:43
101 - Chapter 20. The basics of supervised machine learning.mp4
07:28
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4
09:41
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4
09:53
104 - Chapter 20. Optimizing KNN performance.mp4
07:38
105 - Chapter 20. Running a grid search using scikit-learn.mp4
07:51
106 - Chapter 20. Limitations of the KNN algorithm.mp4
07:30
107 - Chapter 21. Training linear classifiers with logistic regression.mp4
10:39
108 - Chapter 21. Training a linear classifier, Part 1.mp4
09:28
109 - Chapter 21. Training a linear classifier, Part 2.mp4
10:55
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4
07:50
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4
06:38
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4
08:58
113 - Chapter 21. Measuring feature importance with coefficients.mp4
11:57
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4
11:28
115 - Chapter 22. Training a nested if else model using two features.mp4
09:43
116 - Chapter 22. Deciding which feature to split on.mp4
11:21
117 - Chapter 22. Training if else models with more than two features.mp4
09:57
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4
09:26
119 - Chapter 22. Studying cancerous cells using feature importance.mp4
09:20
120 - Chapter 22. Improving performance using random forest classification.mp4
08:51
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4
06:57
122 - Chapter 23. Case study 5 solution.mp4
06:59
123 - Chapter 23. Exploring the experimental observations.mp4
07:57
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4
06:27
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4
06:27
126 - Chapter 23. Adding profile features to the model.mp4
08:54
127 - Chapter 23. Optimizing performance across a steady set of features.mp4
07:12
128 - Chapter 23. Interpreting the trained model.mp4
07:03
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View courses O'ReillyO'Reilly Media is an American learning company established by Tim O'Reilly that publishes books, produces tech conferences, and provides an online learning platform. Its distinctive brand features a woodcut of an animal on many of its book covers.
- language english
- Training sessions 128
- duration 18:10:38
- Release Date 2023/11/06
