This content originally appeared on DEV Community and was authored by Bharath Prasad
If you’ve ever looked at how Amazon recommends products or how Swiggy personalises food options, you’ve already seen data analytics at work. But here’s the thing—raw data is messy. To make it useful, we rely on specific tools.
Why Tools Matter in Analytics
The volume of data generated today is massive—transactions, social media activity, IoT devices, app usage. Analysing this by hand is not realistic. Tools step in to:
Clean and prepare datasets
Create visuals that reveal patterns
Run predictive models
Manage large-scale data efficiently
Categories of Data Analytics Tools
Beginner Tools – Ideal if you’re starting out.
Excel: Still the default entry point
Google Data Studio: Free, shareable dashboards
Tableau Public: Good for learning visualisation
Enterprise/Advanced Tools – Used when scale and speed matter.
Power BI: Strong integration with Microsoft services
Qlik Sense: Real-time dashboards
SAS: Trusted in industries like finance and healthcare
Open Source Tools – Loved by developers and researchers.
R: Focused on statistical modelling
Python (Pandas, NumPy, Matplotlib): Covers everything from cleaning to machine learning
Apache Hadoop: Handles massive datasets
Final Thoughts
If you’re a student or fresher in India, tools like Excel, Power BI, and Tableau are a solid starting point. Once you’re comfortable, dive into Python or R to move toward advanced analytics and machine learning.
This content originally appeared on DEV Community and was authored by Bharath Prasad