08 Feb 2023
The world has gone digital, many transactions are happening online, and tons of user data are generated daily. Organisations collect these data and observe users' trends to make business decisions. They deploy mathematical and analytical methods to fast interpret data and make data-driven decisions.
The main objective of data analysis is to examine, purify, transform, and present data in a more readable format. The goal is to find relevant information, make recommendations, and assist decision-making. It can be summed up as a series of operations on a data set to draw conclusions.
Businesses today have access to a pool of data, and with the correct tools, they can grasp different facets of their industry, market, clients, and other insights. They can make the best judgements at every level to keep risks to a minimum and revenues to a maximum with the help of these insights.
Data analytics is the process of extracting insights and information from data. It is a crucial aspect of modern businesses as it enables organizations to make informed decisions based on evidence and data. Here are some of the top applications of data analytics in various industries:
Data analytics has a wide range of applications across different industries. By leveraging data, organizations can improve their processes, increase efficiency, and make informed decisions.
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What Are Some Popular Data Analytics Tools in 2023?
1) Tableau: Tableau is a data visualisation tool used to analyse and present data in an interactive and easy-to-understand way. It enables users to create insightful dashboards and analytical reports quickly. Through drag-and-drop features, users can quickly create visualisations and share insights with colleagues. Tableau also provides an expansive library of resources, including data connectors, tutorials, and templates.
2) Microsoft Excel: It can quickly process large amounts of data, allowing users to visualise, analyse and manipulate it quickly. Excel is also very user-friendly, making creating and modifying data models, charts, and pivot tables easy. Excel has various statistical and financial functions, allowing users to perform complex calculations in seconds. It even boasts various infographic features that make it suitable for data analysis, such as the ability to filter, sort, and summarise data and create sophisticated charts and graphs. These features make it ideal for data analysts to analyse and visualise data sets quickly.
3) Python: It has several built-in libraries, such as NumPy and Pandas, which allow users to perform complex calculations and analyses quickly and easily. Python also has visualisation libraries, such as Matplotlib and Seaborn, which allow users to visualise data interactively. Python can connect to various databases, allowing easy access to large datasets. It also has a large and vibrant community of users, many of whom are data analysts, which makes it easier to find help when needed.
4) Microsoft Power BI: This powerful data visualisation tool can help data analysts quickly transform complex data into interactive visuals. Besides the visualisation tool, it offers many features.
5) SAS: This provides efficient data management and analytics capabilities to help professionals quickly analyse complex data sets and gain deeper insights. Additionally, SAS offers comprehensive data visualisation capabilities that allow analysts to quickly generate charts and graphs to help them understand the data better.
Data analysts can also use SAS to apply sophisticated analytics techniques such as predictive analytics and machine learning to uncover hidden patterns and trends in data. Finally, SAS provides users with numerous business intelligence tools to help them make data-driven decisions.
Skill-Lync's data analytics courses can help you better understand the world of data analysis. The courses teach you how to interpret data, create effective visualisations, and what factors to consider while making data-driven decisions.
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