Data science is a subject that combines statistics and math with specialized programming advanced analytics methods like machine-learning, statistical research and predictive modeling. It is used to uncover actionable insights hidden in large data sets and guide business strategy, planning and making. The job requires a mixture of technical skills, including initial data preparation in addition to mining and analysis as well as the ability to communicate effectively and to share data with others.
Data scientists are usually innovative and inquisitive, as well as passionate about their work. They love intellectually stimulating challenges which require obtaining complex readings from data, and uncovering new insights. Many of them are self-proclaimed “data geeks” who cannot help themselves when it comes down to investigating and analyzing the “truth” that lies beneath the surface.
The first step in the data science process involves collecting raw data through various methods and sources. These include databases, spreadsheets and APIs (application program interfaces) (API) as well as images and videos. Preprocessing involves handling missing values, normalising or encoding numerical features, identifying patterns and trends and dividing the data into testing and training sets for model evaluation.
Due to factors like volume of data, velocity and complexity it is often difficult to delve into the data and find significant insights. Using proven methods and techniques to analyze data is crucial. Regression analysis aids in understanding how dependent and independent variables are linked through a fitted linear formula and classification algorithms like Decision Trees and tDistributed stochastic neighbour embedding assist in reducing the data dimensions and identify relevant groups.
