All you need to know about Python

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By Inversa Technosoft

What is Python?

Python is the most widely used software for statistical analysis and machine learning. Machine Learning is a subset of Artificial Intelligence, and is the science which is used to perform a specific task, without using explicit instructions. Instead, task is done by understanding existing data patterns and then making inference about future events. In its application across business problems, machine learning is referred to as predictive analytics. Examples of machine learning solutions would be predicting whether prospective customer will buy a product or not, how much should be advertisement expenses for the next month, etc.

“I am working as a Report Analyst in a MNC Company and the only tool I use for work in Microsoft Excel. I am cleaning data, making graphs, creating complicated dashboards using micros and its fun”

What is the use and role of Python?

Python has a very large user base, hence it has very nice documentation, lots of quality tutorials, and a lot of activity on its technical support website “Stack Overflow”. Python has more machine learning packages than any other language. Python is fairly easy to learn and easy to work with, especially in the machine learning packages, deep learning, natural language processing, image recognition, data visualization, and data analysis.

What is the use and role of Python in Data Science?

Data science involves extrapolating useful information from large datasets and multiple statistical computation. These datasets are usually unsorted and difficult to correlate with meaningful accuracy. Machine learning can make connections between disparate datasets, but it requires serious computational sophistication and power.

Python fills this need by being a general-purpose programming language. It allows us to create output, in easy data reading format, in a spreadsheet. Alternatively, more complicated file outputs that can be ingested by machine learning clusters for computation. Python can do this because it is lightweight and efficient at executing code, and it is multi-functional. Python can support object-oriented, structured and functional programming styles, meaning it can find an application anywhere.

Python offers many libraries which are geared towards data science. The most popular data analysis library is called pandas. It is a high-performance set of applications that make data analysis in Python a much simpler task. Other libraries which are extremely useful for data science are Numpy (for scientific computing), Scipy & Matplotlib (for creating graphs & charts), Scrapy (for web scraping), Scikitlearn (for machine learning), TensorFlow (for deep learning), Nltk (for natural language processing), and others.

No matter what data scientists are looking to do with Python, be it descriptive, predictive or prescriptive analytics, Python has the toolset to perform a variety of powerful functions.