Data science is an ever-growing area that bridges several industries. Data science is the process of deriving information and insights from big and multiple sets of data through organizing, processing, and analyzing the data. The programming requirements of data science need a very versatile but supple language that is serene to write the code but can grasp quite complex mathematical processing.
Python is one of the most imperative Data Science languages. Python is most ideal for such requirements as it has already demonstrated itself both as a language for common computing as well as scientific computing. It’s gently easy to learn, it is free, many organizations are using it, and it has a lot of robust statistical and data visualization libraries. There are numerous programming languages that can be used for data science such as SQL, Java, Matlab, SAS, R, and many more, Although Python is the most favored choice by data scientists amongst all the other programming languages.
Python has some skillful features, including:
- Python is very robust and simple so that it is easy to learn the language. You do not want to worry about its syntax if you are a beginner.
- Python helps many platforms like Windows, Mac, Linux, etc.
- Python is a high-level programming language, so you write a program in simple English and this will be internally converted to low-level code.
- Python is an interpreted language that runs code one instruction at a time.
- Python can operate data visualization, data analysis, and data manipulation; NumPy and Pandas are some of the libraries used for manipulation.
- Python serves various robust libraries for machine learning and scientific computations. Various complicated scientific calculations and machine learning algorithms can be accomplished using this language undoubtedly in a relatively simple syntax.
Foremost Python Data Science Frameworks for Beginners
- NumPy: NumPy is brief for Numerical Python. It is the most well-liked library and base for higher-level tools in Python programming for data science. An in-depth grasp of NumPy arrays helps in using Pandas effectively for data scientists. NumPy is versatile in that you can work with multi-dimensional arrays and matrices. NumPy has many built-in features allied to statistical, numerical computation, linear algebra, Fourier transform, etc. NumPy is the standard library for scientific computing with robust tools to integrate with C and C++. If you prefer to learn data science then NumPy is the must learn library.
- SciPy: It is an open-source library used for computing numerous modules such as image processing, integration, interpolation, distinctive functions, optimizations, linear algebra, Fourier Transform, clustering, and many other tasks. This library is used with NumPy to function logically numerical computation.
- SciKit: This library is used for machine learning in data science with various classification, regression, and clustering algorithms, which gives support vector machines, naïve Bayes, gradient boosting, and logical regression. SciKit is designed to interoperate with SciPy and NumPy.
- Pandas: Pandas is well known for presenting data frames in Python. This is a powerful library for data analysis, collated to other domain-specific languages like R. By using Pandas it’s serene to handle absent data, helps working with differently listed data gathered from multiple distinctive resources, and supports automatic data alignment. It additionally offers tools for data analysis and data structures like merging, shaping, or reducing datasets, and in addition, it is very effective in working with data connected to time series through providing robust tools for loading data from Excel, flat files, databases, and brisk HDF5 format.
- Matplotlib: Matplotlib stands for Mathematical Plotting Library in Python. This is a library that is generally used for data visualization, which includes 3D plots, histograms, image plots, scatterplots, bar charts, and power spectra with interactive elements for zooming and panning for notifying in different firms copy formats. It supports nearly all platforms such as Windows, Mac, and Linux. This library further serves as an extension for the NumPy library. Matplotlib has a module pyplot that is used in visualizations, which is frequently compared to MATLAB.
These libraries are good for beginners to begin data science using the Python programming language. There are many other Python libraries available such as NLTK for natural language processing, Pattern for web mining, Theano for deep learning, IPython, Scrapy for web scraping, Mlpy, Statsmodels, and more.
Why Python 2.7?
Excellent group support, this is something you would want in your early days. Python 2 was released in late 2000 and has been in use for beyond 15 years. A plethora of third-party libraries, though many libraries have provided 3.x support nevertheless a large number of modules work only on 2.x versions. If you format to use Python for particular applications like web development with high reliance on external modules, you would possibly be better off with 2.7. Some of the features of 3.x versions have backward compatibility and can work with 2.7 versions.
Why Python 3.4?
They are virtuous and faster, Python developers have fixed some inherent glitches and minor drawbacks in order to set a stronger foundation for the future. These might not be very relevant initially but will matter eventually. It is the future. 2.7 is the last release for the 2.x family and eventually, everyone has to shift to 3.x versions. Python 3 has released stable versions for the past 5 years and will continue the same. There is no certain winner but I suppose the bottom line is that you should focus on learning Python as a language. Shifting between versions should just be a question of time.
Conclusion
Python is certainly a great tool and is becoming an increasingly prominent language among data scientists. It is easy to learn, integrates well with other databases and tools like Spark and Hadoop. Considerably, it has great computational intensity and has powerful data analytics libraries.
The Python for Data Science course is intended for students who wish to learn about the robust Python data science ecosystem in order to apply data analysis techniques, information visualization, market basket analysis, portfolio optimization, and inferential statistical analyses to gain new insights into the data.
So, learn Python to perform the thorough life-cycle of any data science project. It includes reading, analyzing, visualizing, and finally making predictions. The focus is not on computer programming, but most of all on the use of several practical tools and libraries in the Python programming language. To Master, the concepts Enroll NOW!!
