Data Science using Python

With growing innovations and technological advancements, data has become abundant and crucial than ever before. It is critical for organizations to manage and visualize a large amount of data to aid business decisions and provide better customer services. Advancements in Artificial Intelligence (AI) and Internet of Things (IoT) propels the growth and demand for data science. Data science is becoming vital in driving new business insights and efficiency across industries. There is an increased demand for professionals with data analysis and visualization skills.

Why Python for Data science?

Python is useful for data scientists and programmers to address challenges in accessing, capturing, analyzing, visualizing, and storing data. It is becoming popular in Big Data, Data Science, and Data Visualization domains. Due to its simplicity and versatility, Python is one of the preferred languages for data scientists to study and manipulate data. Because it is widely used by data scientists and programmers across geographies, it has a great developer community support with respect to data management queries.

Python Resources for Data science

Python has rich resources for data science. Python data science libraries and packages are readily available, portable, and usable in data management processes. Some of the popular and preferred Python data science libraries and packages are:

  • NumPy is an open source Python Library optimized for numerical operations and scientific computing. It provides precompiled functions and enables you to work with large multidimensional arrays, masked arrays, and matrices. It is useful in computing operations such as selecting, sorting, statistical analysis, shape manipulation and so on. It also contains useful functions such as Fourier transforms, linear algebra, random simulations, and so on.
  • Pandas is an open source Python software package for data manipulation and data analysis. Pandas is built using NumPy and is used for wrangling and prepping data. Pandas is used in the exploratory analysis to manipulate numeric tables and time series. It is a powerful, fast, and flexible data science tool in Python. Some of the key features of Pandas include data alignment, dataset merging, hierarchical axis indexing, label-based slicing and so on.
  • SciPy is a Python-based, open source, numerical library built on NumPy. It is used in technical, scientific, and numerical computing. It provides functions for N-dimensional array analysis,image processing, numerical routines, linear algebra, integration and other elements of data science. Apart from mathematical libraries, SciPy supports data scientists with data-base subroutines, sparse matrices, and parallel programming capabilities. It collaborates and leverages other data science libraries such as NumPy, Matplotlib, and Pandas.
  • Matplotlib is a powerful and flexible 2D plotting, visualization library available in Python. It is used to make pie charts, histograms, professional graphs, line plots, heat plots, and so on. You can save outputs in PDF, GIF, PNG, JPG, and other popular formats. To improve performance for large arrays, it uses other extension libraries such as NumPy. Python is widely used to leverage Matplotlib capabilities such as panning and zooming.

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