Data Science
Data science services include data science consulting, development and support to enable companies to run experiments on their data in search of business insights.
Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies.
- Operational intelligence
- Supply chain management
- Customer churn
As a result, data scientists (as data science practitioners are called) require computer science and pure science skills beyond those of a typical data analyst. A data scientist must be able to do the following:
- Apply mathematics, statistics, and the scientific method
- Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods
- Extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models
- Write applications that automate data processing and calculations
- Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding
- Explain how these results can be used to solve business problems
Data scientists must be able to build and run code in order to create models. The most popular programming languages among data scientists are open source tools that include or support pre-built statistical, machine learning and graphics capabilities.
An open source programming language and environment for developing statistical computing and graphics, R is the most popular programming language among data scientists. R provides a broad variety of libraries and tools for cleansing and prepping data, creating visualizations, and training and evaluating machine learning and deep learning algorithms. It’s also widely used among data science scholars and researchers.