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Data analytics: A disruptive innovation

Video about the disruptive innovation in the age of analytics.
(upbeat music) In 2012, Harvard Business Review claimed that data scientists is the sexiest job of the 21st century. Don’t you think this is relevant even today? But why so?
In its simplest form, data is information of any kind which can be vast and confounding. However, when synthesized and analyzed, it provides answers in the form of a wealth of insight into every aspect of the world, from complex business functions, to interpreting unseen ecological events, even positing hypothesis about ancient human history. Data analytics is the process of finding, loading, cleaning, transforming, and modeling data to gain insights that can inform business decision-making. As the complexity of your analysis increases, different types of data analytics can be used to answer a series of questions. What happened, why did this happen? What will happen? How can I make this happen?
Thanks to our widespread technology adoption, we are now generating volumes of data, and in many cases have unprecedented access to these datasets. The evolution of analytics supported by advancements in data interpretations gives us access to highly evolved tools and sophisticated functionality that can analyze large quantities of data quickly and effectively. As early as 2012, IBM stated 90% of the world’s data has been created in the last two years. And since then new data has continues to be created exponentially. For example, mobile devices are being used to read over 150 billion emails sent every day.
Google processes 63,000 searches a second, which translates into 3.8 million searches per minute, 228 million searches per hour, 5.6 billion searches per day, and at least 2 trillion searches per year. Facebook’s data warehouse can now hold over 300 petabytes of data or about 30,000 times more information than is stored by the U.S. Library of Congress. There’s exponential growth of big data supported by increasingly sophisticated algorithms, enhanced computer power and increasingly cheap data storage has brought us to the age of analytics. Deloitte Access Economics 2018 forecast that in Australia alone, the data science workforce will grow from 301,000 people in 2016 to 17, to 339,000 people in 2021 to 22.
Our future workforce won’t all be data scientists, but a large percentage of them will need to be able to manage and analyze data to do their jobs. This trend is being seen in finance, marketing, healthcare, education, and most other professions as illustrated in an IBM and Burning Glass Report. This diversity and disruption point to a new reality. Success in the jobs of the future will depend on essential transcendental skills. The main purpose of this course is to ease your way through the daunting data analytics concepts using the powerful Python. Python hosts useful libraries that do most of the work for you and leaves you time for looking for specific answers with specific tasks.
Throughout the course you will understand more about the importance and various methods to conduct analysis right from fundamental statistics using Excel to advanced analytics, using Python on large sets of data.

Watch this introductory video to understand the disruptive innovation that is data analytics, and how this course is going to help you be a better analyst.

Reflect and share

You just saw how data analytics is an ever-expanding field in the professional world. Perhaps not everyone chooses to work in a profession solely because it’s famous. Think about your own motivations:

  • Why do you like data analytics?

  • What prompted you to learn more about data analytics?

  • What are you most interested in learning about with this course?

Share your thoughts in the Comments below.

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Data Analytics and Python Fundamentals

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