Ideal for: storing/editing data, basic analysis, manipulating data, graphs/charts. Best for Qualitative Data Analysis: ATLAS.tiīest for Storing/Editing Data & Creating Graphs: Excel.Cheaper Alternative for Business Intelligence: Power BI.

Best for Business Intelligence & Reporting: Tableau.

Best for Advanced Statistical Analysis: R.

Best for Machine Learning & Automation: Python.

Easiest for Building Prediction Models: Akkio.

Best for Non-Technical Users: Polymer Search.

Best for Storing/Editing Data & Creating Graphs: Excel.

How many people are in your company? What is the budget?.

Do you need any specific types of visualizations? (e.g.

What level of technicality is the job? Are we dealing with data scientists or data analysts? Perhaps marketers & salespeople?.

What size of data are we dealing with? Big Data?.

What type of data does your business have? Do they require certain analysis procedures? (e.g.There are several factors to consider when choosing a data analysis tool: Today I’ll show you exactly what to look for when choosing a tool, and I’ll be going over 10 of the best and most accepted tools by both beginners and data scientists. This can make it challenging for finding the right tools for your needs. Sometimes, tools overlap and serve the same use cases and both have their pros & cons. There are dozens of data analysis tools out there, with each being designed with a certain use case in mind.

What is tools for data analysis