Data Science: a powerful tool for interpreting vast amounts of raw data
Technologies
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The growth of digital data is an undeniable fact. Statista forecasts that global data volume will reach 180 zettabytes by 2025, nearly tripling the amount recorded in 2020.
Processed digital data helps us understand the world better, make better decisions, increase the efficiency of processes, detect fraud, create new products and services, and more.
Processing large amounts of raw data is not an easy task. Thus, the work of data scientists is becoming more and more important. Their ability to work with data allows them to draw informed conclusions and notice typical patterns.
Processing Data
Data Science consists of many disciplines: computer science, mathematics, statistics, machine learning, and domain understanding of target data. Data scientists use a range of tools and technologies to process data, including programming languages such as Python and R; Pandas and NumPy statistical libraries; and rendering libraries such as Matplotlib and Seaborn.
Data processing usually involves several stages. The first is the collection of relevant data from various sources, such as databases, network APIs, targeted surveys, archives, or relevant sensors. Collected data must be cleaned of unnecessary values, inconsistencies, and outliers. Exploratory Data Analysis (EDA) is the next step. This includes analyzing data to identify trends, patterns, and correlations through visualization and statistical summaries.
Processing specialists’ data can create mathematical models based on the selection of target parameters with the help of pre-processing. Because they are created by “learning” from input data, they are also called machine learning models. People use this type of model for forecasting. Finally, the finished implementation of the model is built into a finished product that is used to solve scientific or commercial problems.
