Data Science as a field has gained a lot of popularity in the recent, couple of years. This field primarily deals with converting huge amounts of data into meaningful marketing strategies. The data dealt with here, refers to the scores and scores of records present online, these records could be anything from a persons shopping choices to daily notifications. This data is gathered, structured and then studied to reach a logical conclusion. While earlier, it was only the IT companies who were the forerunners in this field, today there are companies across the field of finance, e-commerce, retail, health care and more, dealing with data analytics. The process of drawing value based insights from the unstructured data in the virtual space, is known as data analytics. The professionals working in this field are referred to as Data Scientists or Data Analyts and they make use of certain tools to achieve end results.
These tools used for data analytics are namely, SAS Programming, R Programming, Hadoop, Python, SQL and others.
Of these tools, Python is one such tool that has a unique attribute, of being a general purpose programming language as being easy to use, when it comes to analytical and quantitative computing. Python has been in the industry for quite a long time and has been used in industries like scientific computing, oil, gas, finance, physics, signal processing and more. Python has also been used in building applications such as YouTube, has been instrumental in powering Googles internal infrastructure and so on. This tool can coordinate massive clusters of computer graphics servers and aid in the production of movies, this attractive attribute is why companies like Disney, Sony, Dreamworks and the likes depend on it.
When it comes to data science, Python is a very powerful tool, which is also open sourced and flexible, adding more to its popularity. It is known to have massive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. Anyone who is familiar with programming languages such as, Java, Visual Basic, C++ or C, will find this tool to be very accessible and easy to work with. Apart from being an independent platform, this tool has the ability to easily integrate with the existing Infrastructure and can also solve the most difficult of problems. It is said, that this tool is powerful, friendly, easy and plays well with others, apart from running everywhere. A lot of banks use this tool for the purpose of crunching data, some institutions use it for analyzing and visualization. This tool offers the great benefit of using one programming language, across multiple application platforms.
Python has already been proven to be as good as R Programming is, in terms of all the process under data analytics. Any novice, entering the field of data analytics can use this programming language to get started in the data science industry. As a result of its multipurpose uses, there are a lot of institutes, which offer courses in Python.
