Data Science Bookcamp - by Leonard Apeltsin (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>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.</b> <p/>In<i> Data Science Bookcamp</i> you will learn: <p/> Techniques for computing and plotting probabilities<br> Statistical analysis using Scipy<br> How to organize datasets with clustering algorithms<br> How to visualize complex multi-variable datasets<br> How to train a decision tree machine learning algorithm <p/> In <i>Data Science Bookcamp</i> 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. <p/> Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. <p/> About the technology<br> 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. <p/> About the book<br> <i>Data Science Bookcamp</i> 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. <p/> What's inside <p/> Web scraping<br> Organize datasets with clustering algorithms<br> Visualize complex multi-variable datasets<br> Train a decision tree machine learning algorithm <p/>About the reader<br> For readers who know the basics of Python. No prior data science or machine learning skills required. <p/> About the author<br> <b>Leonard Apeltsin</b> is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. <p/> Table of Contents<br> CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME<br> 1 Computing probabilities using Python<br> 2 Plotting probabilities using Matplotlib<br> 3 Running random simulations in NumPy<br> 4 Case study 1 solution<br> CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE<br> 5 Basic probability and statistical analysis using SciPy<br> 6 Making predictions using the central limit theorem and SciPy<br> 7 Statistical hypothesis testing<br> 8 Analyzing tables using Pandas<br> 9 Case study 2 solution<br> CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES<br> 10 Clustering data into groups<br> 11 Geographic location visualization and analysis<br> 12 Case study 3 solution<br> CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME<br> 13 Measuring text similarities<br> 14 Dimension reduction of matrix data<br> 15 NLP analysis of large text datasets<br> 16 Extracting text from web pages<br> 17 Case study 4 solution<br> CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA<br> 18 An introduction to graph theory and network analysis<br> 19 Dynamic graph theory techniques for node ranking and social network analysis<br> 20 Network-driven supervised machine learning<br> 21 Training linear classifiers with logistic regression<br> 22 Training nonlinear classifiers with decision tree techniques<br> 23 Case study 5 solution<p/><br></br><p><b> About the Author </b></p></br></br><b>Leonard Apeltsin</b> is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.
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