Data analytics is the process of extraction of meaningful information from data, increasingly with the aid of specialized tools and techniques. Data analytics help organizations and scientists make more informed business decisions. Python has been around since the late 1980’s but has only really started making its presence felt in the data science community recently.
A good selection of data analytics libraries along with the ability to build web applications due to the full-fledged programming nature of Python and easy to learn syntax gives it an edge in quickly becoming a favorite in the data science community for implementing algorithms. It is primary language Google used for creating the tensorflow the deep learning framework, Facebook uses the Python library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications and several banks and researchers use python libraries for crunching numbers.
While there are many libraries available, these ones are almost always encountered while performing data analysis in Python:
- NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
- SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
- Pandas, also built on top of NumPy, offers data structures and operations for manipulating numerical tables and time series.
- Matplotlib is a 2D plotting library that can generate such data visualizations as histograms, power spectra, bar
- Scikit-learn is a machine learning library built on NumPy, SciPy, and Matplotlib that implements classification, regression, and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, and gradient boosting.
Once the data needed is in place, the first steps are to cleanse and prepare the data, which involves removing erroneous and duplicate data that could affect the accuracy of analytics applications. After cleansing the next step is to build analytical models using tools provided in Python libraries. The model is initially run against a partial data set to test its accuracy; typically, it’s then revised and tested again, a process known as“training” the model that continues until it functions as intended. Finally, the model is run in production mode against the full data set, something that can be done once to address a specific information need or on an ongoing basis as the data is updated. The results from these analyses can then be used to trigger business actions or they may be visualized in reports that provide business insights to domain experts.
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