Data science is the secret that keeps you on social media websites. It’s also utilized by airlines to forecast weather patterns and analyse sensors on aircrafts and rockets in order to improve the safety of flights and efficiency.
Data scientists need to first understand the value of their data. In order to solve real-world problems, one must have an understanding of programming (Python or R are the most popular) as well as statistics, machine-learning algorithms, and visualization of data.
Data Preparation
The third key skill is being able to prepare raw data to be analysed. This includes tasks like dealing with missing data, normalizing features, coding categorical variables, as well as splitting datasets into test and training sets for model evaluation. This ensures that the dataset is of high quality and is ready to be analysed.
Data scientists then employ various statistical techniques to detect patterns or trends and provide insights. These include descriptive analytics, diagnostic analytics, prescriptive analytics and predictive analytics. Descriptive analytics provides a summary of a dataset in an appealing and visually accessible format, like median mode, mean, standard deviation and variance. This allows users to make informed decisions using their findings. Diagnostic analytics uses historical data to predict outcomes in the near future. A credit card company utilizes this to predict customer default risk, as an example. Predictive analytics can detect patterns in the data that can be used to anticipate future trends, like sales or stock prices.