Data science is a subject that combines statistics and math with specialized programming, advanced analytics techniques such as machine-learning, statistical research and predictive modeling. It is used to find useful insights from large datasets and to guide business strategy and planning. The job requires a variety of technical abilities, such as data preparation, analysis and mining, and excellent leadership and communication skills to communicate the results to others.

Data scientists are usually creative, inquisitive and passionate about their work. They relish intellectually stimulating tasks that require the ability to extract complex readings from data and uncovering new insights. Many of them are “data nerds”, who can’t resist analysing and exploring “truths” that lie below the surface.

The first step of the data science process is to collect raw data using a variety of methods and sources, including databases, spreadsheets, application program interface (API) and videos or images. Preprocessing involves the removal of missing values and normalising or encoding numerical features in order to identify patterns and trends and splitting the data into training and testing sets to evaluate models.

Data mining and identifying useful insights can be difficult due to several factors, including volume, speed and complexity. Using established methods and techniques for data analysis is essential. Regression analysis, for instance can help you determine how dependent and independent variables interact through a fitting linear equation, and classification algorithms like Decision Trees and t-Distributed Stochastic Neighbour http://virtualdatanow.net/3-ways-vdr-can-simplify-the-statutory-reporting-process/ Embedding assist you in reducing dimensions of data and find relevant clusters.