Data is everywhere. From your photographs on the cloud to your documents on the drive, from your Instagram pictures to your about info on Facebook, all the information is there on the internet in one way or another. Don’t we need someone to handle all these data, store it and make use of it if needed in the future? And should be the qualification of this Data Scientist? And how will they work on this data?
What is Data Science?
Data Science is the technique of extracting useful knowledge and insights from data by using scientific methods. Now, what are these scientific methods? These scientific methods include:
Programming is the basic requirement of data science. A data scientist should be an efficient programmer to be qualified to handle big and important data and work and analyse it. Statistics is another essential discipline of data science. To take decisions and make predictions regarding something, we need to have properly clustered data with proper indications and insights.
If you are working in the marketing sector and you are asked to design and launch a product at a particular time of the year, when will its sale be on the peak? Business skills come into play here. You need to cluster all the information and find out the most liked and marketable product. Then you need to decide the best time to launch that product. For example, the primary reason behind Paytm’s success was its launch time. It was launched during demonetisation when everyone needed a digital wallet.
Now the question arises of how to use these stats and work on data? Obviously through algorithms coded in a programming language but which language? Programming is done in many languages like C++, Java, Python, R, etc. but Python holds a special place among them and is used everywhere.
Python Programming Language:
Python was first introduced in 1980. But after further updates and improvements, it was officially launched in 1991. Guido van Rossum created it. Python’s inbuilt functions and libraries make it the first choice for data scientists. Unlike C++ and Java, where you need to code all the algorithms and functions, Python is privileged with inbuilt functions where you just need to write the function name with its parameters, and your task is done.
Python is a dynamically typed language. It supports a number of programming paradigms, such as procedural, object-oriented, and functional programming. It’s easy to write syntax makes it less complex than other programming languages. It does not require any compilation and can be run directly
Python’s formatting is visually uncluttered, and it often uses English keywords whereas other languages like C and C++ use punctuation (C++ uses ‘;’ to end a statement). It uses white space indentation rather than curly brackets to delimit blocks and specify the scope of a variable. It is a free and open-source language. It is extensible. It has a large library and provides a rich set of modules and functions for rapid application development. It can be easily integrated and is a cross-platform language, i.e. it can run equally on various platforms like the Windows, Linux, UNIX, Macintosh, etc. Graphical User Interfaces (GUI) can be developed using Python.
Python’s Importance in Data Science:
Python has become popular and the most important programming language in a very short time. Data scientists have to deal with a huge amount of data. With a broad set of libraries and inbuilt methods, Python has become the most convenient and accessible language to handle big data.
Some of its features which make it important in data science are:
- Easy to use-
It’s easy-to-use syntax, and better readability makes it more understandable. It has a simple and fast learning curve. It is a dynamically typed language, i.e. the variables are defined automatically. It has inbuilt methods for the majority of the mathematical functions. So, rather than writing the whole algorithm, we can just write the function name and our work will be done. It provides a large variety of applications used in data science.
Complex data sets can be simplified using Python easily. It is also relatively easier than other programming languages like Java, C, C#, etc. It also provides more flexibility and ease in the field of machine learning and deep learning.
As we move forward as a programmer, two factors are used to define them majorly. First, memory used, and second, time is taken. The code can be written much faster in Python rather than other languages and hence a lot of time, and mechanical power is saved. It can help data scientists in developing machine learning models, web services, data mining, classification, etc.
- Builds better analytics tools-
One of the most vital parts of data science is data analytics. Its library ‘numpy’ provides a lot of information about various matrices, and the matrix is used the most to store data. It provides better insight, understand patterns and correlate data from big datasets.
- Important for deep learning-
Machine learning and Deep learning is becoming popular and influential as time is passing. Python provides a lot of packages like Tensorflow, Keras, and Theano, which helps to develop deep learning algorithms. Deep learning algorithms are based on the human brain neural networks and deals with building artificial neural networks that simulate the behaviour of the human brain. Python provides better support when it comes to deep learning algorithms.
There are a lot of platforms to learn any programming language. CaddGild Technologies is one such platform. It is one of the best Computer Training Centre and provides a lot of courses with outstanding teaching and various tests that will evaluate and enhance one’s skills. Some of the courses that CaddGild Technologies offer are Data Science with Python, Data Analytics, Digital Marketing Course, Microsoft Certification, VBA & Macros Training, R language and Machine Learning. This is one of the best platforms to learn Python and having a good command on Python is very important if you want to become a Data Scientist.