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Data is information that is collected through observation and used to inform discussion, reasoning, or planning. Data can take digital and print forms and can be analyzed and organized by way of database management and data visualizations. Analysis of data drives policy and business decision-making across all industries.

Elizabeth Gleesing

Elizabeth Gleesing is a university instructor and content writer at Noble Desktop. She earned her PhD in English from Syracuse University in 2023 with research focusing on how documentaries incorporate digital and new media technologies. Her research into artificial intelligence, data analysis, and emerging technologies informs her content writing as she frequently works on articles related to careers in data science, data analytics, AI, and related software.Elizabeth also has over ten years of experience teaching undergraduates in universities and colleges in New York and Washington state and won the Syracuse University English department’s Elson Teaching Award in 2019. Her experience in instruction allows her to coherently explain difficult topics to a variety of audiences.

Data Topics

Data Analytics

Data analytics refers to the science of data analysis. Data analysis takes place in every industry – gaming companies analyze player data and use that to inform reward schedules and new features, manufacturing companies use data analysis to measure productivity, machine runtime, and capacity, and content companies use data to monitor user behavior. The analysis of raw data yields insights that would not otherwise be evident.

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Data Science

Data science draws from multiple disciplines to understand data. Combining machine learning, data analysis, statistics and domain knowledge, data science has been referred to as the “fourth paradigm of science”. Using algorithms and other statistical methods, data scientists can uncover knowledge and insights from structured and unstructured data that might otherwise go unnoticed.

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Data Visualization

Data visualization creates visual representations of data, making it easier to read and understand. Trends and outliers are more easily recognized and understand when data is presented in graph, chart, or map form. Many industries benefit from data visualization techniques, including marketing, retail, service industries, education, government, and finance. Data visualizations can take the form of timelines, radial trees, Gantt charts, and infographics, among many others.

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Machine Learning

Machine learning refers to the process by which a system can “learn” from experience and adjust algorithms to improve effectiveness. Used in natural language processing, computational biology, image processing, and computational finance, machine learning is a potent tool for collecting, organizing, and understanding data. Learning occurs both supervised and unsupervised. Unsupervised machine learning can detect hidden patterns of significance in data and adjust algorithms as needed.

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Python Data Science

Python is a powerful, general-purpose programming language with broad applications for in data analytics, data visualization, and data science. With Python, users can create scatter plots, line plots, histograms, and other plots to visualize data and facilitate analysis. Matplotlib is Python’s most widely used 2D plotting library, used for the creation of such data visualizations. Using libraries such as BeautifulSoup and scikit-learn, Python can collect and organize assembled data for analysis. Python has broad machine learning capabilities that make it agile and adaptive. The Python programming community offers many free code blocks to make the creation of visualizations easy and intuitive.

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SQL

SQL stands for Structured Query Language. Data analysts rely on the query functions of SQL to analyze massive quantities of data using relational models. SQL defines three distinct types of data, including predefined, constructed, and user-defined types. Standard SQL commands such as “Create,” “Update,” “Insert,” “Delete,” “Select,” and “Drop” can be used to do almost anything a data analyst needs to do with data. SQL powers many database management systems, such as Oracle, Access, and Microsoft SQL Server.

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Tableau

Tableau is an interactive data visualization tool that produces cloud databases, query relational databases, online analytical processing cubes, and spreadsheets. It offers even novice users the ability to change or transform the raw set of data. Using the Tableau Desktop, data scientists can create rich, interactive data visualizations, deploy data drilling and mining tools, and provide in-depth analysis that uncovers hidden patterns. Tableau’s intuitive, drag-and-drop interface makes it easy to get started.

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Excel

Excel is more than just a tool for creating databases. It can also be used to analyze data. Excel can organize data using filters, sorting, and conditional formatting. Charts, pivot tables, and other visualizations can then be created from the assembled data sets. Users can also download and install the Analysis ToolPak, which provides additional data analysis tools specifically for financial and engineering data.

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Python Machine Learning

Python is a preferred programming language for machine learning due to its simplicity, versatility, and extensive library ecosystem, which includes tools like TensorFlow. In the realm of data, it enables efficient manipulation, analysis, and the development of complex algorithms, making it essential for deriving insights and predictions from large datasets.

Python for Finance

Python for finance is a specialized application of Python programming that leverages its powerful libraries and tools for financial analysis and modeling. It's a critical subset of data science, enabling professionals to analyze market trends and value assets and make data-driven financial decisions.

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