Using Python for Data Science

Pythonis a powerful, easy to learn programming language that has high-levels of data structures, and a simple yet efficient approach to object-oriented programming. It features dynamic typing, elegant syntax, and effective interpretation capabilities, which make it a preferred language for application development on most platforms.

It is only recently that it made inroads into the field of Data Science as previously, it was rather complex to do critical statistics in this programming language. However, today there is a wide range of tools available in this scripting language to address almost all the elements of scientific computing. Facebook has now turned to Python’s library Pandas for data analytics because it provides the benefit of using a single programming language across different applications.

On the other hand, Bank of America also uses Pythonto analyze and interpret its financial data. It is also being used by the Theoretical Physics Division of Los Alamos National Laboratory to control simulations, as well as analyze and visualize data in the most efficient manner. And this is just a glimpse of how more and more organizations are using this platform for data analysis, visualization, and scientific computing.

Popular Python Libraries for Data Science

While this popular programming language provides a number of libraries to perform data analysis and computing, here are the best ones to get started:

• NumPy: It is the fundamental for effective scientific computing with Python programming language. It supports multi-dimensional and large matrices and arrays, and also includes an array of high-level mathematical functions that can operate on these matrices or arrays.

• Matplotlib: It is typically a 2D plotting library that has the ability to generate data visualizations in the form of power spectra, histograms, scatterplots, bar charts, etc. and requires only a few lines of coding.

• Pandas: It is built on top of NumPy library, and provides data operations and structures to manipulate time series and numerical tables.

• SciPy: It also works with NumPy arrays, and offers routines for effective numerical optimization and integration.

• Scikit-Learn: This machine-learning library is built on NumPy, Matplotlib and SciPy, and implements    classification, clustering, and regression of algorithms, including logistic regression, support vector machines, random forests, gradient boosting, and naïve Bayes. 

R or Python?

Python’s growing use in a wide spectrum of data science applications has put it in stiff competition with R, another software environment and programming language that is designed to perform the type of data analysis and visualization Python is now able to handle. With this, speculations arise whether one of these languages will eventually outshine the other in the field of data science. Therefore, it is crucial to decide which language to learn or what to use in a specific project.

Usability: In the field of data analysis, Python is typically used when the analysis tasks have to be integrated with web applications or the statistics code is to be integrated into production database. On the other hand, R is used when the tasks demand analysis on individual servers or standalone computing.

Tasks: As a comprehensive programming language, the former is good to implement algorithms for use in production. However, R is easier to use for exploratory tasks. With this language, critical statistical models can be easily written with just a few lines of code.

Data Handling Capabilities: Python’s NumPy and Pandas libraries are ideal for data analytics. On the other hand, R is preferred for data analysis because it has a large number of packages, the benefit of using formulas, and easily usable tests. It can be used for basic data analysis without the need to install the packages. However, for big data sets, you can use R packages such as dplyr and data.table.