We have always relied on the powers of oracles in order to find out what happens next. That is because we want to make the right decision and do not want to miss anything as the future is always uncertain. It is soothing to know that we can depend on technologies, knowledge, and insights that allow us to take wise decisions and secure our future. Business relies on these entities to make decisions in order to secure its future and thrive. But not every business is able to make sense out of the enormous data it has. Nokia, for instance, had millions of data points collecting data from its customers and funneling it into its business intelligence. Yet, it was not able to predict the rise of smartphones and remained biased towards its traditional business model. The once unchallengeable company is now struggling to gain grounds over its competitors who took the right decision at the right time.
Making sense out of data is as crucial as collecting it. Why companies like Nokia fail to utilize their data is that the two sides involved in the whole decision making process are polar opposites. On one hand are the business people who know what data they need and can define requirements, but do not possess skills to design a data architecture that gives them the data they need. Technology people, those who provide data, don’t understand the business requirements, but can design the data architecture. Thus when these two sets of experts fail to find common ground, business misses insights that are crucial for business intelligence.
Data Science has been a trending word in the industry for a long time. It is the middle path of the business aspect and the technology aspect of decision making. Data science analyses data to provide actionable insights. At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them by incorporating computer science, data modeling, statistics, analytics, and mathematics. With data points such as mobile apps, web apps, websites, point of sales, IoT increasing geometrically, the role and impact of data science can only grow in the future.
Linkedin, in its initial days, was growing fast but its users were not making connections with people already on the site. The traditional analysis was not helping it. Then one executive employed Data Science in order to create more engagement. The process saw unprecedented increase in use connections. Uber, the unicorn start-up, runs detailed predictive analysis of data to check when the demand for cabs is bound to rise and uses surge pricing. It uses similar data science to promote driver loyalty by providing them incentives. In short, Data Science is becoming a crucial discipline and a reliable system for making business decisions across domains.
One of the biggest misconceptions is that you need a sciences or math Ph. D to become a legitimate data scientist. Data Scientists use many technologies such as Hadoop, Spark, and Python. These technologies do not warrant a Ph. D.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. In simple words, Hadoop is a framework that allows you to store Big Data in a distributed environment so that you can process it parallel.
Apache Spark is an open-source engine built around speed, ease of use, and sophisticated analytics and developed specifically for handling large-scale data processing and analytics. It allows users to access data in across sources, such as Hadoop Distributed File System (HDFS), Amazon S3 etc. Internet behemoths such as Netflix, Yahoo, and eBay have deployed Spark massively, collectively processing multiple petabytes of data on clusters of thousands of nodes.
Python or Monty Python is a general purpose programming language which has overtaken R as the primary language of Data analytics, Data Science owing to its capabilities such as easier learning curve, wide reach, bigger user base and support groups, flexibility and better app integration.
Mastering these technologies can open the avenues for an aspiring Data Scientist. There aren’t enough Data scientists to cater to the growing needs of the industry.
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