Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease …
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: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.
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.
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.
For readers who know the basics of Python. No prior data science or machine learning skills required.
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!Case study 1: Finding the winning strategy in a card game
Chapter 1. Computing probabilities using Python This section covers
Chapter 1. Problem 2: Analyzing multiple die rolls
Chapter 2. Plotting probabilities using Matplotlib
Chapter 2. Comparing multiple coin-flip probability distributions
Chapter 3. Running random simulations in NumPy
Chapter 3. Computing confidence intervals using histograms and NumPy arrays
Chapter 3. Deriving probabilities from histograms
Chapter 3. Computing histograms in NumPy
Chapter 3. Using permutations to shuffle cards
Chapter 4. Case study 1 solution
Chapter 4. Optimizing strategies using the sample space for a 10-card deck
Case study 2: Assessing online ad clicks for significance
Chapter 5. Basic probability and statistical analysis using SciPy
Chapter 5. Mean as a measure of centrality
Chapter 5. Variance as a measure of dispersion
Chapter 6. Making predictions using the central limit theorem and SciPy
Chapter 6. Comparing two sampled normal curves
Chapter 6. Determining the mean and variance of a population through random sampling
Chapter 6. Computing the area beneath a normal curve
Chapter 7. Statistical hypothesis testing
Chapter 7. Assessing the divergence between sample mean and population mean
Chapter 7. Data dredging: Coming to false conclusions through oversampling
Chapter 7. Bootstrapping with replacement: Testing a hypothesis when the population variance is unknown 1
Chapter 7. Bootstrapping with replacement: Testing a hypothesis when the population variance is unknown 2
Chapter 7. Permutation testing: Comparing means of samples when the population parameters are unknown
Chapter 8. Analyzing tables using Pandas
Chapter 8. Retrieving table rows
Chapter 8. Saving and loading table data
Chapter 9. Case study 2 solution
Chapter 9. Determining statistical significance
Case study 3: Tracking disease outbreaks using news headlines
Chapter 10. Clustering data into groups
Chapter 10. K-means: A clustering algorithm for grouping data into K central groups
Chapter 10. Using density to discover clusters
Chapter 10. Clustering based on non-Euclidean distance
Chapter 10. Analyzing clusters using Pandas
Chapter 11. Geographic location visualization and analysis
Chapter 11. Plotting maps using Cartopy
Chapter 11. Visualizing maps
Chapter 11. Location tracking using GeoNamesCache
Chapter 11. Limitations of the GeoNamesCache library
Chapter 12. Case study 3 solution
Chapter 12. Visualizing and clustering the extracted location data
Case study 4: Using online job postings to improve your data science resume
Chapter 13. Measuring text similarities
Chapter 13. Simple text comparison
Chapter 13. Replacing words with numeric values
Chapter 13. Vectorizing texts using word counts
Chapter 13. Using normalization to improve TF vector similarity
Chapter 13. Using unit vector dot products to convert between relevance metrics
Chapter 13. Basic matrix operations, Part 1
Chapter 13. Basic matrix operations, Part 2
Chapter 13. Computational limits of matrix multiplication
Chapter 14. Dimension reduction of matrix data
Chapter 14. Reducing dimensions using rotation, Part 1
Chapter 14. Reducing dimensions using rotation, Part 2
Chapter 14. Dimension reduction using PCA and scikit-learn
Chapter 14. Clustering 4D data in two dimensions
Chapter 14. Limitations of PCA
Chapter 14. Computing principal components without rotation
Chapter 14. Extracting eigenvectors using power iteration, Part 1
Chapter 14. Extracting eigenvectors using power iteration, Part 2
Chapter 14. Efficient dimension reduction using SVD and scikit-learn
Chapter 15. NLP analysis of large text datasets
Chapter 15. Vectorizing documents using scikit-learn
Chapter 15. Ranking words by both post frequency and count, Part 1
Chapter 15. Ranking words by both post frequency and count, Part 2
Chapter 15. Computing similarities across large document datasets
Chapter 15. Clustering texts by topic, Part 1
Chapter 15. Clustering texts by topic, Part 2
Chapter 15. Visualizing text clusters
Chapter 15. Using subplots to display multiple word clouds, Part 1
Chapter 15. Using subplots to display multiple word clouds, Part 2
Chapter 16. Extracting text from web pages
Chapter 16. The structure of HTML documents
Chapter 16. Parsing HTML using Beautiful Soup, Part 1
Chapter 16. Parsing HTML using Beautiful Soup, Part 2
Chapter 17. Case study 4 solution
Chapter 17. Exploring the HTML for skill descriptions
Chapter 17. Filtering jobs by relevance
Chapter 17. Clustering skills in relevant job postings
Chapter 17. Investigating the technical skill clusters
Chapter 17. Exploring clusters at alternative values of K
Chapter 17. Analyzing the 700 most relevant postings
Case study 5: Predicting future friendships from social network data
Chapter 18. An introduction to graph theory and network analysis
Chapter 18. Analyzing web networks using NetworkX, Part 1
Chapter 18. Analyzing web networks using NetworkX, Part 2
Chapter 18. Utilizing undirected graphs to optimize the travel time between towns
Chapter 18. Computing the fastest travel time between nodes, Part 1
Chapter 18. Computing the fastest travel time between nodes, Part 2
Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis
Chapter 19. Computing travel probabilities using matrix multiplication
Chapter 19. Deriving PageRank centrality from probability theory
Chapter 19. Computing PageRank centrality using NetworkX
Chapter 19. Community detection using Markov clustering, Part 1
Chapter 19. Community detection using Markov clustering, Part 2
Chapter 19. Uncovering friend groups in social networks
Chapter 20. Network-driven supervised machine learning
Chapter 20. The basics of supervised machine learning
Chapter 20. Measuring predicted label accuracy, Part 1
Chapter 20. Measuring predicted label accuracy, Part 2
Chapter 20. Optimizing KNN performance
Chapter 20. Running a grid search using scikit-learn
Chapter 20. Limitations of the KNN algorithm
Chapter 21. Training linear classifiers with logistic regression
Chapter 21. Training a linear classifier, Part 1
Chapter 21. Training a linear classifier, Part 2
Chapter 21. Improving linear classification with logistic regression, Part 1
Chapter 21. Improving linear classification with logistic regression, Part 2
Chapter 21. Training linear classifiers using scikit-learn
Chapter 21. Measuring feature importance with coefficients
Chapter 22. Training nonlinear classifiers with decision tree techniques
Chapter 22. Training a nested if/else model using two features
Chapter 22. Deciding which feature to split on
Chapter 22. Training if/else models with more than two features
Chapter 22. Training decision tree classifiers using scikit-learn
Chapter 22. Studying cancerous cells using feature importance
Chapter 22. Improving performance using random forest classification
Chapter 22. Training random forest classifiers using scikit-learn
Chapter 23. Case study 5 solution
Chapter 23. Exploring the experimental observations
Chapter 23. Training a predictive model using network features, Part 1
Chapter 23. Training a predictive model using network features, Part 2
Chapter 23. Adding profile features to the model
Chapter 23. Optimizing performance across a steady set of features
Chapter 23. Interpreting the trained model