As a field data science combines math and statistics, specialized programming, and advanced analytics techniques like statistical research, machine learning and predictive modeling. It is used to discover valuable insights hidden within large datasets and to inform business strategy, planning, and decision making. The job requires a combination of technical abilities, including upfront data preparation, mining and analysis, and also a an ability to communicate effectively and to share data with others.

Data scientists are often interested, creative, and enthusiastic about their work. They love intellectually stimulating challenges that require deriving intricate reads from data, and uncovering new insights. A majority of them are “data geeks” who cannot resist exploring and analyzing “truths” that lie under the surface.

The initial stage of the data science process is collecting raw data through different methods and sources. These include databases, spreadsheets and APIs (application program interfaces) (API), as well as images and videos. Processing includes removing missing values as well as normalising numerical elements in order to identify patterns and trends and dividing the data into test and training sets to test models.

Due to factors such as volume, velocity and complexity, it can be difficult to mine the data and find useful insights. Using established methods and techniques for data analysis is essential. Regression analysis for instance allows you to see how independent and dependent variables interact through a fitting linear equation. Classification algorithms such as Decision Trees and t-Distributed Stochastic Neighbour Embedding can help you reduce the dimensions of your data and identify relevant clusters.

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