Data Science Bookcamp

  • 11h 37m
  • Leonard Apeltsin
  • Manning Publications
  • 2021

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

In this Book

  • Computing Probabilities Using Python
  • Plotting Probabilities Using Matplotlib
  • Running Random Simulations in NumPy
  • Case Study 1 Solution
  • Basic Probability and Statistical Analysis Using SciPy
  • Making Predictions Using the Central Limit Theorem and SciPy
  • Statistical Hypothesis Testing
  • Analyzing Tables Using Pandas
  • Case Study 2 Solution
  • Clustering Data into Groups
  • Geographic Location Visualization and Analysis
  • Case Study 3 Solution
  • Measuring Text Similarities
  • Dimension Reduction of Matrix Data
  • NLP Analysis of Large Text Datasets
  • Extracting Text from Web Pages
  • Case Study 4 Solution
  • An Introduction to Graph Theory and Network Analysis
  • Dynamic Graph Theory Techniques for Node Ranking and Social Network Analysis
  • Network-Driven Supervised Machine Learning
  • Training Linear Classifiers with Logistic Regression
  • Training Nonlinear Classifiers with Decision Tree Techniques
  • Case Study 5 Solution


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