Data science may sound like a fancy new buzzword, but in reality this concept has been around for hundreds of years. In the 1740s, Bayes’ Theorem taught us that initial belief plus new data would lead us to an improved belief, and this has become the basis for machine learning and artificial intelligence. The Foundations of Statistics, published in 1954, outlined scientific objectivity in data analysis and how results become more reliable and objective when more data is part of the equation. From there the computer age added further standardizations to data analysis that would eventually pave the way for the modern age of data science and machine learning artificial intelligence. Today the data scientists who handle these processes and turn it into actionable business intelligence are more sought-after than ever.
Between 2011 and 2012, job listings for “Data Scientist” increased 15,000%. Data scientists use data, math, artificial intelligence, and the scientific method to turn big data into actionable intelligence. It sounds simple but it’s a mammoth task. Data scientists have to ensure that data is collected in such a way that it can be analyzed, they have to ensure it is stored properly so nothing corrupts it, and they have to look for and feed in the problems that will get the desired answers.
There are many roles that need to be filled in the management, processing, and execution of big data science. These include:
That’s not to say there’s not a creative element to such a venture. In fact, the analysis of data is completely useless unless you know what to look for. As the old adage goes, garbage in, garbage out. Data scientists will need to know not only how to handle the data, but what kinds of answers can be found within it, as well.
By 2020 there will be more than 2.7 million data scientist job openings. If you want to join their ranks, start by learning things like Java, Python, PyTorch, Apache Spark, Hadoop, and R programming language. The amount of data available to businesses is growing rapidly. In 2013 IBM discovered that 90% of the world’s data had been created in the previous two years, and by 2025 175 billion terabytes of data will be created every day. As the amount of data collected grows, so will the demand for scientists to study it and look for answers from it.
Learn more about the people and tech behind data science from the infographic below. Are you ready to make a career change?
Read next: New AI Will De-Haze and Colorize the Blur Underwater Photos
Between 2011 and 2012, job listings for “Data Scientist” increased 15,000%. Data scientists use data, math, artificial intelligence, and the scientific method to turn big data into actionable intelligence. It sounds simple but it’s a mammoth task. Data scientists have to ensure that data is collected in such a way that it can be analyzed, they have to ensure it is stored properly so nothing corrupts it, and they have to look for and feed in the problems that will get the desired answers.
There are many roles that need to be filled in the management, processing, and execution of big data science. These include:
- Data Engineers: these are the people who create and maintain data collection methods. They need to know things like Java, Aforge.net, and Scikit-learn.
- Software Engineers: these are the people who figure out what data needs to be analyzed and design the software to fit the business. They need to know things like Java, SQL, and Python.
- AI Hardware Specialists: these are the people who tell the AI what information to look for in the data sets. They need to know things like Python, Java, SaaS, and machine learning.
That’s not to say there’s not a creative element to such a venture. In fact, the analysis of data is completely useless unless you know what to look for. As the old adage goes, garbage in, garbage out. Data scientists will need to know not only how to handle the data, but what kinds of answers can be found within it, as well.
By 2020 there will be more than 2.7 million data scientist job openings. If you want to join their ranks, start by learning things like Java, Python, PyTorch, Apache Spark, Hadoop, and R programming language. The amount of data available to businesses is growing rapidly. In 2013 IBM discovered that 90% of the world’s data had been created in the previous two years, and by 2025 175 billion terabytes of data will be created every day. As the amount of data collected grows, so will the demand for scientists to study it and look for answers from it.
Learn more about the people and tech behind data science from the infographic below. Are you ready to make a career change?
Read next: New AI Will De-Haze and Colorize the Blur Underwater Photos