Data science is a field that combines statistics and math with specialized programming advanced analytics methods like machine-learning, statistical research, and predictive modeling. Data science is used to uncover valuable insights hidden within large datasets and to inform business strategy, planning, and decision making. The job requires a mixture of skills in data preparation, analysis and mining, in addition to the ability to communicate effectively and with authority to communicate the results to other people.
Data scientists are usually innovative enthusiastic, curious and passionate about their work. They are drawn by challenging intellectual tasks, such as deriving complex insights from data or gaining new insights. Many of them are self-proclaimed “data nerds” who can’t resist when it comes down to investigating and analyzing the “truth” that lies beneath the surface.
The first step of the process of data science is gathering raw data through various methods and sources, including databases, spreadsheets, application program interface (API) and videos or images. Preprocessing includes handling missing values and normalising numerical features in order to identify patterns and trends and breaking the data up into test and training sets to evaluate models.
Due to factors such as volume of data, velocity and complexity it can be difficult to mine the data and identify significant insights. Using established methods and techniques to analyze data is crucial. Regression analysis helps you understand how dependent and independent variables relate by using a linear formula that is fitted and classification algorithms such as Decision Trees and tDistributed stochastic neighbor embedding can help you reduce the data dimensions and identify relevant groups.
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