Python
Introduction
Python is a popular and high-level programming language. Even for the beginner, it is easy to learn and apply. Python is widely used in Data Science field like Data Mining, Machine Learning, Business Analytics, Predictive Analytics and other advanced fields of Computer Science. Companies worldwide irrespective of their domains are using python to get the deepest insights into their process and make a data-driven decision. Apart from data science, python applications can be found in various domains viz. web development, game development, desktop GUI applications, data analysis, business applications, system scripting, software development and so forth. Python is a powerful programming language and it is easy to learn. Unlike other popular programming languages like C, C++ and Java, Python is easier to complete and has diverse applications in all environments. In python, we can save our program as modules and can be reused with other programs.
What is Python?
Python is an open-source programming language and was created in 1991 by Dutch programmer Guido Van Rossum. It is an object-oriented, interactive, high-level programming language. We can incorporate different modules and programs into packages and can be used in cross-platform environments. Most importantly, it works as a general-purpose programming language and applications can be found in various fields. We can read and translate python codes much easier than other languages like Java, C, C++, etc. Moreover, Python is portable and can be run in Unix, Windows, Mac OS and so on.
Why use Python?
- Free and open-source : Python is a free and open-source programming language. We can download python from the official web address for free. Since it is open-source, we can contribute towards its development in the python community and can also distribute it freely.
- Easy to learn and code : Python is easy to learn and code as compared to other languages like C, C++, Java, etc. Since it is a user-friendly language, anyone can learn python basics in a short span of time. It has a basic data structure, clearly defined syntax, simplex procedure and can easily run in any platform. We can easily code in python, we just need to need to install python with interactive IDE.
- Object-oriented : Python is an object-oriented language and provides all the standard features of object-oriented programming language. It means it supports the concepts of data encapsulation, abstraction, inheritance, polymorphism, etc. In simpler terms, it supports the concepts of class, objects which contain data, method (a form of procedures), message passing and so forth.
- Large standard library : Python has an extensive array of a library which provides a wide set of modules for repetitive use. It will be helpful for us to use in many applications. It comes inbuilt many of the standard modules and packages. We can also call or import the packages. Some of the popular standard library files used in the data science field are Scrapy, BeautifulSoup, NumPy, SciPy, Pandas, Matplotlib, pydot, etc.
- Portable language : Python is a highly portable language. We can run python in various platforms like Linux, Unix, Windows, Mac OS, etc. Suppose we have coded few programs for our business use in Windows platform and we want it to be reused for our different team which have a Mac or Unix OS, they can also easily run this program on their system. Irrespective of platforms, python run equally in all the environments.
- Integrated language : Python can be easily integrated with other languages. Python is very popular among users due to its extensible form. Some of the languages which we can easily integrate with python are C, C++, Java, COM, etc. For example, we can write code in C or C++ and compile with our python code. Thus, it makes Python, a popular programming tool in the industry.
Importance of Data Analytics & Data Analytics with Python
Data Analytics is very well known due to its applications in all the domains. We all know the importance of data analytics in this data-centric world. Data Analytics helps us get the deepest insights into our process and make data-driven decisions. In other words, it helps us to extract valuable information from our process data. Data Analytics is a method of collecting data from any source, apply analytical methods combined with statistics & programming language, and make a data-driven decision in an organisation. In any business scenarios, an organisation must enhance productivity, gain revenues, understand customer’s perspective and insight of business models. Here, data analytics comes into play. Data analytics is applicable in all the departments of an organisation viz. manufacturing, sales, finance, marketing, production, R&D and so on.
Python is a popular programming language used in Data Analytics. There are several reasons “Why it is popular among Data Analyst?” and many others. First, it is an open-source programming language which is free and we can redistribute in the form of various data analytics modules. Python has a large array of standard library packages which are very helpful in data analytics. It helps us to analyse data quickly and in a structured way. We can easily import data from various sources, perform data cleaning & data transformation. Then we can easily visualize with effective data visualization tools and get insights into your toughest business problems. Some of the popular data-centric libraries available in python are Scrapy, BeautifulSoup, NumPy, SciPy, Pandas, Matplotlib, pydot, etc. Most importantly, python is a portable language and we can run in various cross-platform environments viz. Windows, Linux, Mac OS, etc. Moreover, we can also integrate with other programming languages like C, C++, Java and so forth.
Minitab is a Data Analytics Software, where we can predict, visualize, analyse and harness the power of data. Dive deep into the data, forecast your business to make better decisions, reduce costs and stop mistakes before it happens. Identify the significant factors which are affecting your process and uncover the hidden relationship between variables. Business Analytics tools are also available to ease you in your toughest business problems. Minitab applications can be found in various processes like automotive, healthcare, energy, agriculture, pharmaceuticals, marketing, telecom, etc. Regardless of statistical background and programming skills, organisations can use Minitab to analyse small and large datasets for quality improvement, process validation, product development and so forth.
Now with our recent update (Minitab® 19.2020.1 or higher), we can do Python Integration in Minitab. We can run Python scripts easily in Minitab and extend our analytical capabilities by collaborating with the Data Analyst, Data Engineer, Machine Learning Engineer and the Data Science Generalist. We must import or install mtbpy package in python for integration purpose. Mtbpy package is a standard library package developed by Minitab for Python Integration in minitab. After we install the mtbpy package, we’re ready to run Python code from Minitab. The mtbpy package gives you the capability to bring data from Minitab into Python and to return Python results to Minitab. In other words, we can create tables, graphs, messages, and notes in Python and display them in Minitab. Python integration in minitab offers the flexibility of custom Python code within Minitab’s easy-to-use interface, and the results can be saved, stored, and shared in Minitab Project Files. Since python is a general-purpose programming language with many applications in data analytics, we can use Minitab to get the deepest insights into our process data.
We conduct various training programs – Statistical Training and Minitab Software Training. Some of the Statistical training certified courses are Predictive Analytics Masterclass, Essential Statistics For Business Analytics, SPC Masterclass, DOE Masterclass, etc. (Basic to Advanced Level). Some of the Minitab software training certified courses are Minitab Essentials, Statistical Tools for Pharmaceuticals, Statistical Quality Analysis & Factorial Designs, etc. (Basic to Advanced Level).
We also provide a wide range of Analytics Solutions like Business Analytics, Digital Process Automation, Enterprise Information Management, Enterprise Decisions Management and Business Consulting Services for Organisations to enhance their decision support systems.
