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Data science is a subject that combines math and statistics with specialized programming advanced analytics methods like machine-learning, statistical research and predictive modeling. It helps to uncover actionable insights in large datasets and to guide business strategy and planning. The job requires a combination of technical abilities, such as analysis, data preparation and mining, as well as an ability to lead and communicate to communicate the results to other people.

Data scientists are often enthusiastic, creative and passionate about what they do. They love intellectually stimulating challenges that require deriving intricate reads from data and generating new insights. Many of them are self-proclaimed «data nerds» who are not able to resist when it comes down to investigating and analyzing the «truth» that lies below the surface.

The first step in the data science process is to collect raw data using a variety of methods and sources, such as spreadsheets, databases, applications program interface (API) and videos or images. Preprocessing involves removing missing values as well as normalising numerical elements, identifying trends and patterns, and splitting the data up into training and test sets to evaluate models.

Due to factors like volume, velocity and complexity, it isn’t easy to sift through the data and discover relevant insights. Utilizing proven methods and techniques to analyze data is essential. Regression analysis for instance can help you understand how independent and dependent variables connect through a fitted linear equation. Classification algorithms such as Decision Trees and t-Distributed Stochastic Neighbour Embedding aid in reducing dimensions of data and find relevant clusters.