UNDERSTANDING DATA SCIENCE AND ITS CHALLENGES FOR ASPIRANTS
Posted On: 2019-08-02 Author : Dataperts Team


Data Science has become a buzz word in today's context and is becoming a key shaper of the industry in times to come. To be clear, data science is a field that does not work in isolation. It comes to life through the integration of statistics, computing and deep domain knowledge of the function. It is a field that comes to life through various tools and associated areas of knowledge such as Machine Learning, Big Data, Data Mining etc. These sub-parts of data science are implemented through powerful coding languages such as R, Python, Java etc.
It is estimated that over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. It's no wonder when we hear the statistic that some 90% of the world's data has been created only in the last two years. For a layman, this data is a waste but for a data analyst, this huge bulk of data is a mine that can be explored to get solutions to various problems that the organisation and businesses are facing.

Some Data Science Terms & Technologies
Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. Big Data could be composed of structured, semi-structured, unstructured or a combination of all these forms of data. Big data can be made amenable to further manipulation and analysis with the help of the tools such as MapReduce, Hadoop etc.
Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Some Challenges
For a data scientist, there are several challenges. These range from defining the problem correctly, identifying and getting access to relevant data and data cleansing knowing that big data can come in different forms and structures.
Ultimately, data science requires all three - a structured approach to problem-solving, knowledge of tools and technologies as well as creative bent of mind to come up with insightful solutions to real-life problems.

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