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What Is Data Science?

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Data science is the magic that keeps you engaged on social media websites. Airlines use it to forecast weather patterns, analyse sensor data from aircraft and rockets, and improve the safety of flights.

Understanding the importance of data is the first step to becoming a data scientist. Solving real-world problems requires a solid background in programming (Python or R are the most well-known), statistics machines-learning algorithms, visualisation of data.

Data Preparation

The second ability is to prepare raw data. This includes tasks such as handling missing data or normalising features. This also includes encoding categorical variables and splitting datasets in training and test sets to evaluate models. This ensures that the dataset is of high quality and is ready to be analysed.

Then, data scientists employ various statistical methods to discover patterns, trends and patterns. These include descriptive analytics as well as diagnostic analytics, prescriptive analytics and predictive analytics. Descriptive analytics provides a summary of a collection of data using simple and easy-to-read formats like mean median, mode, standard deviation and variance. This lets users make informed decisions using their findings. Diagnostic analytics uses data from the past to predict what will happen in the near future. A credit card company uses this to predict customer default risk, for instance. Predictive analytics can detect patterns in data from the past to predict future trends such as sales or prices of stocks.

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