Data Science Bookcamp
- 18h 1m 33s
- Leonard Apeltsin
- Manning Publications
- 2021
In Data Science Bookcamp, you will learn:
- Techniques for computing and plotting probabilities
- Statistical analysis using Scipy
- Web scraping
- How to organize datasets with clustering algorithms
- How to visualize complex multi-variable datasets
- How to train a decision tree machine learning algorithm
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 audiobook 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 AudiobookData 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.
For listeners who know the basics of Python. No prior data science or machine learning skills required. About the AuthorLeonard Apeltsin is the head of data science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse.
In this Audiobook
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Section 1 - Computing probabilities using Python
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Section 2 - Plotting probabilities using Matplotlib
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Section 3 - Running random simulations in NumPy
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Section 4 - Case study 1 solution
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Section 5 - Basic probability and statistical analysis using SciPy
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Section 6 - Making predictions using the central limit theorem and SciPy
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Section 7 - Statistical hypothesis testing
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Section 8 - Analyzing tables using Pandas
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Section 9 - Case study 2 solution
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Section 10 - Clustering data into groups
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Section 11 - Geographic location visualization and analysis
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Section 12 - Case study 3 solution
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Section 13 - Measuring text similarities
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Section 14 - Dimension reduction of matrix data
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Section 15 - NLP analysis of large text datasets
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Section 16 - Extracting text from web pages
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Section 17 - Case study 4 solution
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Section 18 - An introduction to graph theory and network analysis
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Section 19 - Dynamic graph theory techniques for node ranking and social network analysis
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Section 20 - Network-driven supervised machine learning
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Section 21 - Training linear classifiers with logistic regression
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Section 22 - Training nonlinear classifiers with decision tree techniques
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Section 23 - Case study 5 solution
