Data Science Bookcamp: Five real-world Python projects

706 Pages·2021·42.41 MB·English

by  Leonard Apeltsin

Five real-world Python projects
Leonard Apeltsin
M A N N I N G
Core algorithms inside the book
Algorithm Use case First introduced
K-means Clustering Section 10
DBSCAN Clustering Section 10
Jaccard similarity computation Text comparison Section 13
Cosine similarity computation Text comparison Section 13
Principal component analysis Dimension reduction Section 14
Singular value decomposition Dimension reduction Section 14
Power iteration Eigenvector computation Section 14
TFIDF vectorization Text comparison Section 15
Shortest path length computation Network path optimization Section 18
PageRank Network centrality measurement Section 19
Markov clustering Social network clustering Section 19
K-nearest neighbors Supervised classification Section 20
Cross-validation Model performance testing Section 20
Perceptron Supervised classification Section 21
Linear regression Supervised classification Section 21
Decision tree Supervised classification Section 22
Random forest Supervised classification Section 22
A trained logistic regression classifier distinguishes between two classes of points by slicing like a
cleaver through 3D space (see section 21).
Data Science Bookcamp

Data Science
Bookcamp
FIVE PYTHON PROJECTS
LEONARD APELTSIN
MANNING
SHELTER ISLAND
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To my teacher, Alexander Vishnevsky,
who taught me how to think

brief contents
CASE STUDY 1 FINDING THE WINNING STRATEGY IN
A CARD GAME......................................................1
1 ■ Computing probabilities using Python 3
2 ■ Plotting probabilities using Matplotlib 17
3 ■ Running random simulations in NumPy 33
4 ■ Case study 1 solution 58
CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR
SIGNIFICANCE ...................................................69
5 ■ Basic probability and statistical analysis
using SciPy 71
6 ■ Making predictions using the central limit
theorem and SciPy 94
7 ■ Statistical hypothesis testing 114
8 ■ Analyzing tables using Pandas 137
9 ■ Case study 2 solution 154
vii
viii BRIEF CONTENTS
CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS
HEADLINES......................................................165
10 ■ Clustering data into groups 167
11 ■ Geographic location visualization and analysis 194
12 ■ Case study 3 solution 226
CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE
YOUR DATA SCIENCE RESUME ...........................245
13 ■ Measuring text similarities 249
14 ■ Dimension reduction of matrix data 292
15 ■ NLP analysis of large text datasets 340
16 ■ Extracting text from web pages 385
17 ■ Case study 4 solution 404
CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM
SOCIAL NETWORK DATA ...................................445
18 ■ An introduction to graph theory and network
analysis 451
19 ■ Dynamic graph theory techniques for node ranking
and social network analysis 482
20 ■ Network-driven supervised machine learning 518
21 ■ Training linear classifiers with logistic regression 548
22 ■ Training nonlinear classifiers with decision
tree techniques 586
23 ■ Case study 5 solution 634