Python for Data Scientists

Terminologies in data science

Python is an easy language to learn and the programming language-of-choice for data scientists for various purposes, such as data visualization and data analysis. Data visualization helps data scientists create and study data by means of visual representation, such as pictorial or graphical implementations of data. Data mining includes employing computational processes to discover patterns or relations among large sets of collected data. Machine learning is the process of learning particular aspects from data and turning it into actionable knowledge. While web scraping involves extracting information from numerous websites, recommendation engines help data scientists to study and predict information that might be related to consumers’ preferences, such as accessories you might want to buy while shopping for mobile phones, and so on. Data science implementations have a great range, from business intelligence ande-commerce to geoscienceand statistical analysis in the finance sector.

Python: the primary choice for data science

Python is a high-level programming language and possesses a clear and concise syntax,which contributes to an easylearning experience.Its interpreted nature combined with dynamic typing make it strongly suited to scripting and rapid application development. Python, being open source, has an extensive library and a vibrant scientific community. The Python ecosystem is complete with packages and utilities such as Pandas, Numpy, matplotlib, IPython, and SciPy that complement data science significantly. SciPy guarantees fast, accurate, and easy-to-code solutions to numerical and scientific computing applications, avoiding the need to employ difficult-to-maintain code or expensive mathematical engines. NumPy is an extension of Python, which provides highly optimized arrays and numerical operations and replaces a lot of the functionality of Matlab and Mathematica’s specifically vectorized operations, but in contrast to those products is free and open source in addition to enabling high productivity. The IPython notebook offers a fantastic framework for sharing and diffusing your work in the form of rich and interactive web documents. Matplotlib provides a large library of customizable plots and a comprehensive set of backends. You can generate plots, add dimensions to the plots, and also make the plots interactive with just a few lines of code with Matplotlib. Also, Matplotlib integrates well with all common GUI modules.Pandas helps to alleviate genuine complex situations in data analytics libraries and makes it easier to work with tabular datasets with high performance and easy-to-use data structures. Instead of investing a lot of time and effort in writing complicated and comprehensive code, Python and its ever-expanding packages allow you to focus on the essential tasks at hand and study data closely and rapidly.

Python: the way forward

Utilities such asScikit, bokeh, and pytables are the other Python implementations that support researchers in studying as well as implementing the concepts and tasks of data science. With tools such as Nodebox, you can use Python code to create sketches and interactive visualizations. There are solutions such as PiCloud that allow you to send algorithms and data on-the-fly for heavy computations on cloud platforms. OpenCV's Python bindings (OpenCV is a library of programming functions which enable computer vision)allow the development of applications that capture images, modifytheir appearance, and extract information from them, in a high-level language and in a standardized data format that is interoperable with scientific libraries such as NumPy and SciPy. The wealth ofpackages and libraries offered by Python (coupled with benefits such as high speed, intensive flexibility, and excellent productivity)are used for a wide array of functions, ranging from prototyping and visualization to data analysis.Easy code readability and writability with a lower learning curve make Python an obvious choice for writing complex, high performance programs that perform data science tasks.

Books to Consider

Learning scikit-learn: Machine Learning in Python Book Cover
Learning scikit-learn: Machine Learning in Python
$ 17.99
$ 10.00
Python Data Visualization Cookbook Book Cover
Python Data Visualization Cookbook
$ 23.99
$ 10.00
Building Machine Learning Systems with Python Book Cover
Building Machine Learning Systems with Python
$ 29.99
$ 10.00
Learning Geospatial Analysis with Python Book Cover
Learning Geospatial Analysis with Python
$ 29.99
$ 10.00
Python Geospatial Development - Second Edition Book Cover
Python Geospatial Development - Second Edition
$ 29.00
$ 10.00
Learning Cython Programming Book Cover
Learning Cython Programming
$ 19.99
$ 10.00
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