Data is one of the biggest buzzwords these days. While data science has emerged as one of the most popular fields of study and for work, data engineering is, comparatively, a silent sibling that has witnessed a meteoric rise. But, what is data engineering, how it’s different from data science, what do data engineers do? Read on to get these answers!
In today’s digital culture that holds sway over much of our planet, there’s nothing we consume and create as quickly as data. Sites like Instagram, television shows, and ubiquitous billboards represent only a few sources of data constantly bombarding our senses.
And, because data is ubiquitous, countless organizations and even governments have come up with creative ways to process information and have adapted to their findings. All this has transformed the field of data science, making it an exciting discipline with the potential for growth in untold directions.
In our era, customers can easily hop from one brand to another, leaving companies little room for error. In this context, firms must strive to ensure exemplary customer experience at every interaction. Moreover, because business data is so readily available and competition so fierce, companies face immense pressure to streamline their operations or risk demise.
How do companies develop an understanding of how past action and behavior impact future outcomes? With the field of predictive analytics, it’s easier than ever for companies to anticipate customer expectations.
Doing so enables them to not only preserve the customer experience, but it also allows them to reduce costs, increase efficiency, and improve work conditions, among a myriad of other benefits. With that in mind, let’s take a closer look at predictive analytics.
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
Are you obsessed with finding patterns in data? Do you want to learn more about data analysis and ways to break into this interesting line of work? If you answered yes to these questions, this article is a great place to start.
In this article, we’ll take a deep dive into the process of qualitative data analysis, learn what it is, how it works and some effective methods of analyzing data.
Udacity has trained hundreds of managers and executives since the launch of our first Executive Program — AI for Business Leaders — earlier this year. Today, we’re excited to announce our newest program aimed at preparing executives for digital transformation: The Data Science for Business Leaders Executive Program.
This program — designed in partnership with Alteryx, a leading provider of an end-to-end data science and analytics platform — prepares managers and executives to implement data science initiatives across their businesses.
Why should business leaders consider this course? According to Udacity CEO Gabe Dalporto, “The most successful businesses in the world today put data at the center of their culture and decision making. Udacity’s newest Executive Program equips business leaders to unlock the value of data in their organizations and achieve transformational business outcomes.”
Big data is big business. With 90% of enterprise analysts and business professionals saying data and analytics are the key to digital transformation initiatives, the demand for skilled professionals continues to increase.
Big data can have a big impact for businesses — not only can they save money and time, they can analyze customer trends to develop new products and better understand market conditions. To make the most of this data, companies need the right people with the right skills in the right roles, including data engineers.