As a subject data science integrates math and statistics, as well as specialized programming techniques and advanced analytics like machine learning, statistical research and predictive modeling. It is used to find actionable insights in large datasets, and to inform business strategy and planning. The job requires a combination of technical abilities, such as analysis, data preparation, and mining, along with an ability to lead and communicate to communicate the results to other people.
Data scientists are usually fascinated, imaginative and passionate about what they do. They relish intellectually stimulating tasks that require deriving intricate reads from data and generating new insights. A lot of them are “data geeks” who cannot resist exploring and analyzing “truths” that are hidden below the surface.
The first step in the process of data science involves collecting raw data using a variety methods and sources. These include spreadsheets, databases, applications program interfaces (API), as well as videos and images. Preprocessing involves the removal of missing values, normalising or encoding numerical features, identifying patterns and trends and dividing the data into testing and training sets to evaluate models.
Finding the data to extract useful insights can be difficult because of a variety of factors including volume, velocity and complexity. It is crucial to use reliable data analysis methods and techniques. Regression analysis, as an example can help you determine how independent and dependent variables connect through a fitted linear equation, and classification algorithms like Decision Trees and t-Distributed Stochastic Neighbour Embedding can help you reduce the dimensions of your data and identify relevant clusters.
