Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

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.
Table of Contents (12 chapters)
11
Index

Time series decomposition

One of the problems that we have modeling the military time series is that there is simply not enough data to be able to produce a general model of the process that produced the series. A common way to model a time series is to decompose the series into a number of separate components:

  • Trend: Does the series generally increase or decrease over time? Is the trend an exponential curve as we saw with the population?
  • Seasonality: Does the series exhibit periodic rises and falls at a set number of intervals? For monthly data it is common to observe a period cycle of 12 months.
  • Cycles: Are there longer-term cycles in the dataset that span multiple seasons? For example, in financial data we might observe multi-year cycles corresponding to periods of expansion and recession.

Another way of specifying the issue with the military data is that there is not enough information to determine whether or not there is a trend, and whether the observed peak is part of a seasonal or cyclic...