Data Science/Analytics/Engineering
Data Engineer
- Develop, construct, test and maintain architectures and processing workflows
- Build robust, efficient and reliable data pipelines
- Develop solutions for data acquisition
- Ensure architecture supports business requirements
- Develop dataset processes for data modeling, mining, and production
- Drive the collection of new data and refinement of existing data sources
- Recommend ways to improve data reliability, efficiency, and quality
Data Analytics
Data analytics enables organizations to analyze all their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate insights to inform and, in some cases, automate decisions, connecting intelligence and action. Today’s best solutions support the end-to-end analytical process from accessing, preparing, and analyzing data to operationalizing analytics and monitoring results.
Data analytics allows organizations to digitally transform their business and culture, becoming more innovative and forward-thinking in their decision-making. Going beyond traditional KPI monitoring and reporting to finding hidden patterns in data, algorithm-driven organizations are the new innovators and business leaders.
Data Science
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
Prerequisites for Data Science
- Machine Learning
- Modeling
- Statistics
- Programming
- Databases
