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For non-technical people, the route to data science may appear daunting. The good news is that you don't need to be a coding expert or experienced mathematician to begin this journey. Today's data science tools are designed to make data analysis more accessible to everyone.
Whether you work in marketing, healthcare, or another field, these technologies enable you to make sense of data without the need for complex written scripts. Excited to get started? Below are the most important tools for data scientists with non-technical backgrounds:
Excel indeed continues to be an indispensable data analysis tool. Most non-technical professionals utilize this tool, making it the perfect platform for beginners. You can manage large datasets, employ basic statistical procedures, and display the outcomes visually in Excel. For starters, understanding PivotTables, VLOOKUP, and data filters, among other simple functions, can help manage large datasets more efficiently.
Key Features:
Data organization and analysis
Basic statistical functions
Easy-to-use graphs and charts
Google Sheets, like Excel, is a cloud-based spreadsheet program that allows users to alter data. Google Sheets allows many people to update a document at the same time. This is useful in collaborative revisions of documents, such as project work. Google Sheets also has its own set of data analysis features and is compatible with other products such as Google Data Studio and Analytics.
Key Features:
Data organization and analysis
Basic statistical functions
Graphs and charts which are user-friendly
Tableau is a data visualization application that can generate many sorts of charts and graphs in only a few minutes. The most notable aspect of Tableau is that it is geared at end users. There is no requirement to have programming abilities. It even has a point-and-click interface for creating dynamic presentations to communicate complicated data.
Key Features:
User-friendly drag-and-drop feature
Ability to perform advanced data visualizations
Dashboards are designed to be engaging
Microsoft Power Bi is another popular tool. Its applications include corporate intelligence and data visualization. The user navigation in its UI makes it accessible to non-technical users. Power BI users may also create interactive reports and dashboards from many data formats, similar to Tableau.
Key Features:
Data visualization and reporting
Integration with other Microsoft tools
Interactive dashboards
KNIME, the Konstanz Information Miner, is a free-access software platform that allows users to visually design data analyses as a series of ‘nodes’. It has numerous built-in nodes for various data science processes including data analysis, algorithms for machine-learning techniques and also for statistical evaluation. Thanks to its very simple interface, even people without any programming background can work with KNIME, since they only need to drag-and-drop boxes on the screen and create the so-called data workflow.
Key Features:
Visual workflow design
Extensive library of pre-built nodes
No coding required
Orange is a visual programming tool for data mining and machine learning. Like KNIME, it uses a drag-and-drop interface, making it accessible to users without a technical background. It’s particularly useful for those interested in machine learning, as it includes many pre-built models for data classification, clustering, and regression.
Key Features:
Visual programming environment
Pre-built machine learning models
Data visualization
DataRobot is a tool for automatic machine learning (AutoML). It enables users to create and deploy machine learning models using minimum code. This tool benefits non-technical users by simplifying the complicated process of training and improving models, making data science more accessible.
Key Features:
Automated machine learning
Easy model deployment
No coding required
RapidMiner is another visual tool that supports data science workflows. It provides a robust platform for data preparation, machine learning, and predictive analysis. Users can build models using its drag-and-drop interface and choose from a wide array of machine-learning algorithms.
Key Features:
Drag-and-drop data science applications
The use of machine learning tools is allowed
Ready to use workloads for all models available for forecasting
For those who want to create simple yet powerful reports, Google Data Studio is a great option. It allows non-technical users to connect multiple data sources and create custom reports. The platform is highly intuitive and integrates well with other Google tools like Google Analytics, BigQuery, and Sheets.
Key Features:
Custom report creation
Integration with multiple data sources
User-friendly interface
Alteryx professes itself as providing an easier route to data preparation and analysis. Thanks to its visual workflow tools, the user can perform such tasks as data blending, data scrubbing, and even predictive services with little or no coding. This has been adopted extensively to provide operational analytics in the business sector.
Key Features:
Visual data workflows
Different data set merging
No code predictive analytics
Such tools are best suited for people who have no educational background related to programming and want to learn data science. The tools make complicated processes easier. Therefore, it allows users to concentrate on analysis and insights rather than bother about codes and algorithms. And even though you may not be a tech person, with these tools, you can take a step into data science which holds countless possibilities.
