Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

Full BioNand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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### Top 15 Scala Libraries for Data Science in 2018

**Data analysis and math**

**Breeze**(Commits: 3316, Contributors: 84)

- Matrix and vector operations for creating, transposing, filling with numbers, conducting element-wise operations, inversion, calculating determinants, and much more other options to meet almost every need.
- Probability and statistic functions, that vary from statistical distributions and calculating descriptive statistics (such as mean, variance and standard deviation) to Markov chain models. The primary packages for statistics are breeze.stats and breeze.stats.distributions
- Optimization, which implies investigation of the function for a local or global minimum. Optimization methods are stored in the breeze.optimize package.
- Linear algebra: all basic operations rely on the netlib-java library, making Breeze extremely fast for algebraic computations.
- Signal processing operations, necessary for work with digital signals. The examples of important operations in Breeze are convolution and Fourier transformation, which decomposes the given function into a sum of sine and cosine components.

- Vec (1D vector)

- Mat (2D matrix)

- Series (1D indexed matrix)

- Frame (2D indexed matrix)

- Index (hashmap-like)

- MLlib is an RDD-based library that contains core machine learning algorithms for classification, clustering, unsupervised learning techniques supported by tools for implementing basic statistics such as correlations, hypothesis testing, and random data generation.
- ML is a newer library which, unlike MLlib, operates on data frames and datasets. The main purpose of the library is to give the ability to construct pipelines of different transformations on your data. The pipeline can be considered as a sequence of stages, where each stage is either a Transformer, that transforms one data frame into another data frame or an Estimator, an algorithm that can fit on a data frame to produce a Transformer.

*The article was originally published in kdnugget*