Data science is the magic that keeps you glued to social media sites. Airlines employ it to predict weather patterns, analyse sensor data from aircraft and rockets, and increase flight safety.

Data scientists must first comprehend the importance of their data. Being able to comprehend the fundamentals of programming (Python and R are the most popular), statistics and machine learning algorithms and data visualization is crucial to solving real-world problems.

Data Preparation

The other key skill is being able to prepare raw data for analysis. This includes tasks such as handling missing data or normalising features. Also, it involves encoding categorical variables as well as splitting data in training and test sets to test models. This will ensure a high-quality data set that is ready for analytic processing.

Then, data scientists employ various statistical methods to discover patterns, trends, and insights. These include descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Descriptive analytics gives a description of a collection of data using visual and easily comprehensible formats such as mean mode, median, standard deviation and variance. This lets users make informed decisions based on their findings. Diagnostic analytics rely on past data to predict future outcomes. This is utilized by credit card companies to anticipate default risk. Predictive analytics can detect patterns in data from the past to predict future trends, such as sales or prices for stocks.

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