Ebook: Data Science Bookcamp: Five real-world Python projects
Author: Leonard Apeltsin
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Machine Learning, Probabilistic Models, Natural Language Processing, Decision Trees, Data Science, Supervised Learning, Python, Clustering, Data Visualization, Statistics, Logistic Regression, scikit-learn, Web Scraping, NumPy, matplotlib, pandas, Graph Theory, NetworkX, Graph Algorithms, Geospatial Data, Probability Theory, Hypothesis Testing, Network Analysis, Statistical Inference, Text Processing, Markov Models, Cartopy, Elementary, Monte Carlo Simulations
- Year: 2021
- Publisher: Manning Publications
- City: Shelter Island, NY
- Edition: 1
- Language: English
- pdf
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 learn:
• 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.
What's inside
• Web scraping
• Organize datasets with clustering algorithms
• Visualize complex multi-variable datasets
• Train a decision tree machine learning algorithm
About the reader
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.
In Data Science Bookcamp you will learn:
• 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.
What's inside
• Web scraping
• Organize datasets with clustering algorithms
• Visualize complex multi-variable datasets
• Train a decision tree machine learning algorithm
About the reader
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.
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