A Data Scientist extracts insights from raw data and uses them to solve a problem. Data scientists are in high demand as they can help companies make sense of the ever-growing amount of data available. A career in data science can be very rewarding as there are many opportunities for growth and development. The work is interesting, challenging, and intellectually stimulating. Here is a list of the top 10 career prospects in Data Science. Whether you're looking to start a career in data science or want to upgrade your existing skills, you'll need to be prepared to work with massive amounts of data. The demand for data analysts is growing rapidly. While the field has been around for a while, the latest trends and developments show that it is still very much alive and kicking.

Want to join a company where doing good is what we do? The right data can make that happen. As a member of the Enterprise Data Solutions team, you'll have the opportunity to work with data assets and data professionals across the entire organization to improve efficiencies, increase profitability and better manage risk. You'll also perform analysis and develop data solutions to help solve real business problems rather than shortsighted quick fixes. Partnering with your teammates to understand project requirements and user insights, you'll continually focus on supporting Amica's strategic plan to create peace of mind and build enduring relationships.

More and more often, businesses are using data to drive their decisions -- which makes cutting-edge analytics and business intelligence strategies one of the best advantages a company can have. New technologies, especially those driven by artificial intelligence (or AI), are changing how businesses collect and extract usable insights from data. Here are the six trends you should be aware of that will reshape business intelligence in 2020 and throughout the new decade. New data-collection technologies, like internet of things (IoT) devices, are providing businesses with vast banks of minute-to-minute data unlike anything collected before. Matt Turck, an AI and data investor, calls it "the'datafication' of everything" -- as more of the world comes online, it becomes possible to analyze, catalog and turn information into a format analysts, and AI, can break down.

During this stage a framework of statistics is explored for data collection, data cleansing, data collection, data validation, and data exploration. Descriptive analytic methods built with simple, univariate analysis, data visualizations, data insights, and derived variables. The predictive modeling is finalized by identifying number of methods and techniques, identifying the best fit model, interpretation of the model, and finally the model is deployed to the production during the operationalization phase of the data analytics project by involving all the stakeholders leveraging several machine learning, deep learning algorithms in R language for the corporations. The growing complexity of the big data and the emerging technical landscape of connected data platform bring some complex challenges to the organization to support the executive decision-support systems. Several consulting firms adopt the best practices with their methodology and accelerators for implementing the data science projects leveraging data analytics lifecycle best practices.