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Description

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.

What you will learn

Perform hypothesis testing and understand feature selection and statistical significance to interpret your results with confidence

Implement the core machine learning techniques of regression, classification, clustering and recommendation

Understand the importance of the value of simple statistics and distributions in exploratory data analysis

Scale algorithms to websized datasets efficiently using distributed programming models on Hadoop and Spark

Apply suitable analytic approaches for text, graph, and time series data

Interpret the terminology that you will encounter in technical papers

Import libraries from other JVM languages such as Java and Scala

Communicate your findings clearly and convincingly to nontechnical colleagues

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Frequently bought together

€43.99

Clojure Data Structures and Algorithms Cookbook

€29.99

Clojure Data Analysis Cookbook - Second Edition

€47.99

Total € 121.97

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Stephen Walker Jan 02, 2019

I almost never write reviews (maybe never), but felt that this book deserves more attention. It provides a solid intuition to data science in clojure. Well written and nice to follow through the examples in each chapter. I only hope to hear of more books from Henry Garner!! Selfishly with more depth in time series analysis and online processing of that data.

Amazon Verified review

Dame Edna May 16, 2016

One of the best books for learning data science. Very thorough, practical, well written and interesting.

madeinquant Jun 03, 2020

This book is the best to learn Clojure and data science; Clojure is a unique programming language and it is not a popular programming language, learning Clojure is easy at the beginning but it is very difficult to solve a real world problem. Once you familiarize with Clojure, you will respect the power of LISP (Clojure is a dialect of LISP, Why Clojure?, Uncle Bob) Fortunately, I did a lot of old school programming (i.e. ANSI C, C++, LISP), since there are a lot of original concepts of LISP but learning Clojure is challenging.If you expect to cut and paste the code of your programming, this may not be suitable for you. I was programming data science and algorithms in javascript, C and programming data science in python 3. Python is beautiful, effective and the community has grown, you can find almost all useful data science libraries by googling, however, almost all libraries are difficult to learn the algorithm inside the box even though they are open-source. Learning the algorithm from scratch is a nightmare, I learn to code in Clojure and to migrate an existing code into Clojure. There are a lot of headaches during algorithms and Clojure learning, this book helps me a lot to resolve problems, all that said, this book is suitable for readers who have experienced a lot of programming, read the algorithms, get the concepts and write the algorithms in your own familiar programming language.

skliarpawlo Nov 16, 2017

Great and very useful book for beginners both in Clojure and Data Science. So glad I ordered it

Clojure for Data Science: Statistics, big data, and machine learning for Clojure programmers

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