allen b downey think stats

Bayesian statistical methods are becoming more common and more … This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. And the problem with the way that it is taught is that the formulas don't mean anything, and the student may know her way around menus, but that does not mean that she knows under what circumstances to use what method. Python: Beginner's Guide to Programming Code with Python

At Kobo, we try to ensure that published reviews do not contain rude or profane language, spoilers, or any of our reviewer's personal information.Would you like us to take another look at this review?You've successfully reported this review. Data Science from Scratch: First Principles with Python Good book for professional statisticians who wants to revise the basics. Please try again Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. Please try againSorry, we failed to record your vote.

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It pains me to think that trees died so this book could be printed. Think Stats. En savoir plus. The discussion of statistics is insufficiently supported from a mathematical perspective and the Python is little more than a shameless plug for the author's own package.

Moreover, the wording of his text seems almost intentionally obfuscated to avoid telling readers there are pre-existing functions in common packages such as NumPy and matplotlib which make his function irrelevant. If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. Think Stats: Probability and Statistics for Programmers, O’Reilly Media, June 2011. Python Data Science Handbook: Essential Tools for Working with Data

The main issue, when you tackle any math-heavy stuff - is that you are bombarded with a huge amount of formulas, but can't "feel" the practical side of the subject. Good book on statistical techniques to software developerQuite summarized content, not comprehendable at times. My quest for a really helpful stats book goes on. BeAllen Downey is a professor of Computer Science at Olin College and the author of a series of open-source textbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. To get the free app, enter your mobile phone number.Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. The book presents a case study using data from the National Institutes of Health. starts with bootstrapping to calculate confidence intervals of the mean instead of analytical methods). I really liked this book. The code Downey includes with the book is worth the price of the book. Please try againSorry, we failed to record your vote.

I can see it having value within a course or as a supplement to other material but limited value elsewhere.

Short and snappy! This is not the book for someone trying to learn statistics or trying to learn Python. My one criticism is the implementation in Python: the author provides pre-defined functions and classes which, whilst they greatly help learning concepts, sometimes overlook the teaching of coding.

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Modern because it focuses on computational methods (e.g. Please try againSorry, we failed to record your vote. Amazon calculates a product’s star ratings based on a machine learned model instead of a raw data average. The github source code that accompanies the book is probably more useful as a reference than the book.

It would've been nice to have that presented alongside the initial information with simulations and re-sampling guiding an understanding of the analytic methods.Very nice book.

Publication date 2015 Usage Attribution-Noncommercial-Share Alike 3.0 Topics Python, statistics, textbook Collection opensource Language English. 1449307116

Good book to start using statistics in your data analysis problems. But I do have one major criticism in that they built and reference their own custom python library thinkstats2 rather than using the standard ones used in the field. Instead, the author frustratingly plugs his own self-written functions that are used throughout the entire book. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Berkeley and Master's and Bachelor's degrees from MIT. Plus a lot of the functions the author uses he wrote himself. I bought it to review stats concepts and see the python programming examples, but I think it could serve as a first/ introduction to stats book as well. Download this book in PDF. Berkeley.

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