Master Data analytics Ranking master Data analytics

Choose a Master, an MS or an MBA in Data analytics

Pictogramme The sector of data analytics

Data analytics is comprised of acquiring and constructing information from big data sets. This information is vital for developing better security systems, or useful statistics to help increase growth for companies, and even individuals. Due to the exponential rise of technology and the Internet, data analytics is used in almost every major sector. It is especially used in sectors like banking and securities, communications and media, healthcare, education, manufacturing, insurance, consumer trade, transportation, energy, and sports.

Pictogramme Follow a Masters/MS/MBA in data analytics

Pursuing a Masters/MS/MBA in data analytics helps students develop the skills necessary to help companies formulate strategies in order to make the most efficient and productive decisions. Since data analytics is used in so many different sectors, universities often have classes for specific kinds of analytics, including but not limited to business analytics, consumer analytics, retail analytics, sports and people analytics, marketing research, and social dynamics and network analytics. These classes teach students how to spot trends between consumers and products, and also helps companies maintain a competitive advantage.

Pictogramme To work in the specialty's sector

Due to the increase use of technology and statistics by a majority of companies, data analytics are increasingly growing in demand. Students with a degree can have the opportunity to work in corporations that specialize in finance, marketing, technology, consulting and reporting, telecommunications, construction and utility, retail and trade, and entertainment. The most dominant jobs for prospective workers would be statisticians, data analysts, business intelligence reporting official, data mining or big data engineer, and program/project manager.

Pictogramme What about the area of this specialty in 2019

The need for workers with backgrounds in data analytics continues to grow in 2019, however the landscape of technology continues to change. With specific programs and algorithms becoming more sophisticated while others becoming less secure and unnecessary, there are a multitude of trends beginning to apply themselves to the data world. Specific technologies like Self-service BI, mobile dashboards, R Language, deep neural networks, Tensorflow, MXNet, Microsoft Cognitive Toolkit 2.0, SciKit Learn, Cloud storage and analysis, and Jupyter Notebooks have been on the rise in 2019, and continue to gain steam due to their effectiveness at presenting current data in real time. Other technologies like Hadoop, IoT, Batch analysis, Caffe, and monthly BI reports are slowly becoming less utilized throughout the business world, because they are mainly either not as effective, take up too much data space, or have become obsolete.