Data science is a branch of science that blends math and statistics with specialized programming, advanced analytics techniques such as statistical research, machine-learning and predictive modeling. It is used to discover useful insights that are hidden in large datasets and inform business strategy, planning, and decision making. The job requires a mix of technical skills including analysis, data preparation and mining, in addition to the ability to communicate effectively and with authority to communicate the results to other people.
Data scientists are often creative and inquisitive, as well as passionate about their work. They love intellectually stimulating challenges that require deriving intricate reads from data and discovering new insights. A large portion of them are “data geeks” who cannot help themselves when it comes looking into and analyzing “truths” that are hidden below the surface.
The first step of the process of data science is gathering raw data through a variety of methods and sources, such as spreadsheets, databases, application program interface (API) and images or videos. Preprocessing involves handling missing values by normalising or decoding numerical features, identifying patterns and trends and dividing the data into testing and training sets to evaluate models.
Due to factors such as volume as well as complexity, it is often difficult to delve into the data and identify meaningful insights. Using established methods and techniques for data analysis is essential. Regression analysis allows you to understand how dependent and independent variables relate through a fitted linear formula and classification algorithms such as Decision Trees and tDistributed stochastic neighbour embedding help you reduce the dimensions of data and identify relevant groups.