5 Important Data Science Tools

Data science, machine learning and artificial intelligence are hot topics of business, social and scientific communities. Are these fields new and did we not knew about them before? The answer is No. In fact these fields have existed for decades and all most all major methods and techniques have been there. Then what happened that all of sudden everyone is talking about data science and machine? The answer is it has happened because of 2 things:

1 - availability of business data, and
2 - cheaper computational power - CPU and storage - to store and process data

Emergence of data science has led to a lot of new development tools. Among those tools, Python is one of the most used data science tool by data scientists. Python is one of the simplest computer languages to learn and use. Most concepts in Python have fewer rules compared to other programming languages. Therefore, industry has recognized value of Python and integrated it as key development platform. Here we will briefly look into important Python based data scienc libraries and packages:

1. Scipy

SciPy is a very popular Pyhton. It provides many user-friendly and efficient numerical routines, such as routine for numerical integration and optimization. Scipy is very efficient at N-dimensional array manipulation due to its optimization, linear algebra, integration and other general functions.

2. Theano

With Theano, data scientist can efficiently define, optimize and evaluate mathematical expressions using multidimensional matrices. Theano is quite similar to Numpy as focus of both tools numerical calculation.

3. StatsModels

StatsModels is very useful Python based statistical package for statistical modeling. Data scientists use it to locate data, estimate statistical models and perform statistical tests. StatsModels is very good at descriptive statistics, statistical tests, conspiracy, and outcome of different types of data and each estimator.

4. Scikit-Learn

Scikit-Learn is a Python library for machine learning built on top of SciPy. It comes with a set of common used machine learning algorithms. Due to its consistent interface, it allows data scientist to implement main machine learning algorithms quickly.

5. NumPy

NumPy is an open source Python library for numerical computation. It provides highly optimized precompiled functions for numerical operations. It’s very easy to work and allows to perform standard mathematical operations on n-dimensional arrays without writing comples loops. NumPy has very hands functions to export data objects to formats acceptable for other libraries and softwares such as C or C++.

Got A Data Science Question?

Ask our experts anything about machine learning, analytics or statistics.