DR COACHINGS ,
Top 15 Data Analysis Tools
Take a look at some of the best data analytics tools available. There's something for every business here.
You can set up MonkeyLearn to automatically analyze large collections of data right away using pre-trained models, or you can create your own customized text analysis models in a simple point-and-click interface.
RapidMiner is a data science platform that helps companies build predictive machine learning models from data. It’s aimed at data analytics teams that want to tackle challenging tasks and handle large amounts of data, so you’ll need a technical background.
KNIME is a free, open-source platform to create data science workflows. It has an intuitive drag and drop interface that allows you to import data from different sources, build advanced machine learning solutions, and visualize data.
Talend offers a suite of cloud apps for data integration. It’s designed to help businesses collect all their data in a single platform so that teams can access the right data when they need it.
Microsoft Excel can be used to filter, organize, and visualize quantitative data, making it the perfect tool for performing simple data analysis. You can use a wide range of formulas, filters, and create pivot tables, charts and graphs, to synthesize the data you’ve gathered. But there’s a limit to the amount of data that Excel can handle, so you may need more powerful tools if you’d like to analyze data at scale.
Airtable is a user-friendly cloud collaboration tool defined as “part spreadsheet, part database”. It provides data analysis and data visualization functions (like other traditional spreadsheet tools) but with a powerful database on the backend. By using “views”, you can easily interact with the database to manage, track, and find data. Plus, developers can connect Airtable with other apps through an API.
7. Power BI
Business intelligence tools, like Microsoft Power BI, are extremely important in the data analysis process because they make it easy for businesses to spot trends, patterns, and insights across large sets of data.Microsoft Power BI allows users to import data from hundreds of sources, and drag and drop elements, to create real-time dashboards and reports. Equipped with AI, an Excel integration, and pre-built and custom data connectors,
Tableau is a powerful analytics and data visualization platform that allows you to connect all your data and create compelling reports and interactive dashboards that update in real-time. It’s easy to use, supports large amounts of data, and can be run on-premise or in the cloud.
ClicData is an end-to-end business intelligence platform with extensive data connectivity, data transformation, automation and visualization features. ClicData is 100% cloud-based and works on all operating systems and devices.
Free and open-source programming languages are a great option if you’re building your own data analysis tools. R is widely used for exploratory data analysis, statistical computing, and data visualization. At first, it was mainly used by researchers and academics but has now branched out into the business world. Learning R is relatively easy, even if you don’t have a programming background.
Python is one of the most in-demand programming languages today and it’s considered the preferred language for machine learning. It stands out for being very flexible, allowing you to build solutions for various use cases. Plus, it’s fairly straightforward to learn and write.
With both cloud and on-premises deployment, Qlik offers helpful tools for those with expansive technical backgrounds or users that are not even fully computer literate. QlikView offers in-memory data processing for super fast results and the visualization of color-coded data relationships makes the results and insights easy to understand.
13. SAS Business Intelligence
The SAS Business Intelligence platform focuses on visualizations that can be easily understood and shared simply across an organization for insights with a clear path to implementing change, in order to streamline processes and improve customer satisfaction.
Looker integrates with existing tools to introduce new, highly-focused data that can show previously unseen data relationships to help teams make more informed decisions.
15. SQL Programming Language
Structured query language (SQL) is the standard language created to communicate with databases and is particularly useful when handling structured data. Used to search, add, update, and delete data, among others, SQL makes it easy to organize structured data.
Join the course to learn more.