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Data science is the technology that keeps you glued to social media websites. Airlines use it to predict weather patterns, analyze the data from sensors on aircraft and rockets, and improve flight safety.

Understanding the value of data is the first step towards becoming a data scientist. For solving real-world issues, you require an understanding of programming (Python or R are the most well-known) as well as statistics, machine-learning algorithms, and data visualisation.

Data Preparation

The other key skill is being able to prepare raw data for analysis. This includes tasks such as dealing with missing data, normalizing features, encoding categorical variables, and splitting datasets into training and test sets to evaluate models. This ensures that the dataset is of high-quality and ready for analysis.

Data scientists then use different statistical techniques to find patterns as well as trends and insights. These include descriptive analytics, diagnostic analytics, prescriptive analytics, and predictive analytics. Descriptive analytics presents a data set in an attractive and easily accessible format, like mean, median mode, standard deviation, and variance. This lets users make informed decisions using their findings. Diagnostic analytics uses previous data to predict the outcome in the near future. A credit card company employs this method to predict customer default risk, as an example. Predictive analytics makes use of patterns to predict future trends such as stock prices and sales.