Data

Data analytics is the science and methodology of using qualitative and quantitative approaches to extracting valuable insights into data from big data sets. This process involves finding key data points, then categorizing the data to analyze connections, patterns, relationships, and other insights. Because data is used in almost every organization, data analytics is a key part of a company’s approach to learning more about its customers, competitors, market trends, and business processes. Data analytics is usually placed into four main sections—descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.‎

Data engineering is a subfield of data science responsible for designing, building, and maintaining data infrastructure to collect, process, store, and deliver data so that it can be used and analyzed at scale. Data engineering is extremely important for navigating today’s big data landscape because it enables organizations to generate timely data analysis to guide more effective decision-making.

Data engineers are tasked with the responsibility of preparing massive amounts of data for analysis by data scientists. By using frameworks like Apache Spark to pull data from Hadoop data lakes, data engineers can deliver data for analysis quickly. With the use of machine learning platforms such as TensorFlow, they can train and use neural networks to help decipher unstructured data like video, audio, and image files. And, by using cloud database platforms like Cloudera, data engineers can leverage the power and scalability of cloud-based approaches for their work.

Big data is changing the way we do business and creating a need for data engineers who can collect and manage large quantities of data. Learn more about the role of a data engineer and find out how to become one.‎

In today’s era of “big data”, data science has critical applications across most industries. This gives students with data science backgrounds a wide range of career opportunities, from general to highly specific. Some companies may hire data scientists to work on the entire data life cycle, while larger organizations may employ an entire team of data scientists with more specialized positions such as data engineers to build data infrastructure or data analysts, business intelligence analysts, decision scientists to interpret and use this data.

Some tech companies may employ much more specialized data scientists. For example, companies building internet of things (IoT) devices using speech recognition need natural language processing engineers. Public health organizations may need disease mappers to build predictive epidemiological models to forecast the spread of infectious diseases. And firms developing artificial intelligence (AI) applications will likely rely on machine learning engineers.‎