A few years ago, you would have seen a wise person say that data science using Python is the next big thing, but he won’t say the same thing now as he would rather say, “It is the big thing NOW.” Popular historian and author Yuval Noah Harari, who has impressed tech tycoons like Bill Gates and Mark Zuckerberg, says data is the new God. Without going too much in the details, the key message is that data is important, and thus the science of it, Data Science as well. Python, as you may have heard, is a programming language frequently used for working on data science. With plenty of talks all around about Data Science-Python and quite a few Data Science with Python foundation course being offered, it is time we ask ourselves if this is something to learn.
We will systematically explore the above-mentioned question in this article.
Why is Data Science Important?
Data, in this context, is a vast amount of information, for example, the billions of pairs of letters in a DNA helix. Data Science is the use of scientific methods and processes, particularly computer programs, to extract knowledge or gain an insight into all this data that can be structured or unstructured. In our example, the sequencing of the human genome from the DNA helix data is Data Science. Let us see what it is that data science or a data scientist is supposed to do:
- Identify or define a business problem which requires the application of data science skills.
- Gather large amounts of data from various sources (e.g. email usage statistics of employees in a company, or age and education statistics of a city’s population). Data acquisition can be done from various sources such as databases, web servers, etc.
- Data mining, which is another name for finding important patterns in the collected data (for instance, finding a recurring correlation between the age group 10-14 and a particular video game’s mention).
- Pre-processing or Preparation of data. Data can often need cleaning, meaning it may contain duplicate values or other inconsistencies that may affect the final picture
- Data Analysis and Data Modeling which includes the setting of proper feature variables and then finding the perfect model for Data Modeling so that meaningful knowledge can be extracted out of it.
- Visualisation, because although all the above steps are essential in gaining an insight into data, it cannot be done without proper visualisation, which can employ graphs, charts, or even animations.
Look at the following image. It is compiled from large amounts of data that made no sense before, but after proper analysis and visualisation, it gives meaningful information within a few seconds of looking at it.
So Where Does Python Fit in This, and What Is It?
Imagine you are given the names of a million people and their favourite colours in an unstructured way and your job is to find out which colour is most loved and which least. This can be done manually or by using simple tools like MS Excel, but it will take a huge amount of time, and time is a very expensive resource. Here’s where Python comes into the picture. Now, instead of sorting all the data yourself, you can spend a few minutes (or hours, based on your skills) writing a code that helps you sort the data and give you what you need in the end. Translating the above simple problem to much more complex scenarios beyond names and colours, you can imagine why it seems important to have a highly developed and simple to use programming language. Thus comes Python in the picture.
Python is a high level, object-oriented and simple-to-learn language, according to its creators. It was invented over two decades ago and is still very popular. Following are some reasons why Python is a great language to utilise for data science purposes:
- It’s easy to learn since it employs a syntax that focuses on readability, due to which it is a language that can be used not just by computer programmers but scientists, accountants, and other professionals alike. This is important because no condition requires a data scientist to be strictly a computer programmer/engineer.
- Requires less time and less amount of code.
- Being a high-level language, users don’t have to worry much about memory management (like in C++).
- It is available on various platforms, such as Windows, Mac. or Linux, and it is an open-source license developed product which is free to use.
Does That Mean Great Prospects for a Data Scientist Using Python?
Yes! Certainly. Thomas Davenport and D. J. Patil in their Harvard Business Review’s article from October 2012 call Data Scientist to be the sexiest job in the 21st century. If you are pondering over getting well versed with Python and Data science together, you are having a great idea. The next step is to have a comprehensive Data Science With Python Foundation training first, and once you get the hang of it, you are all set.