Data science/ML/AI

CHOOSING THE RIGHT DATA ANALYTICS TOOLS

With so many data analytics tools available,
how do you pick the right one?

The truth is—there’s no one-size-fits-all answer.
The best tool depends on your needs, your data, and your goals.

Here’s how to decide:

🔹 For Data Exploration & Cleaning → SQL, Python (Pandas), Excel
🔹 For Dashboarding & Reporting → Tableau, Power BI, Looker
🔹 For Big Data Processing → Spark, Snowflake, Google BigQuery
🔹 For Statistical Analysis → R, Python (Statsmodels, SciPy)
🔹 For Machine Learning → Python (Scikit-learn, TensorFlow)

Ask yourself:
✅ What type of data am I working with?
✅ Do I need interactive dashboards?
✅ Is coding necessary, or do I need a no-code tool?
✅ What does my team/stakeholder prefer?

The best tool is the one that helps you solve problems efficiently.

www.tg-me.com/hk/Data science ML AI/com.datascience_bds/763

2.8K viewsFeb 22 at 08:44

CHOOSING THE RIGHT DATA ANALYTICS TOOLS

With so many data analytics tools available,
how do you pick the right one?

The truth is—there’s no one-size-fits-all answer.
The best tool depends on your needs, your data, and your goals.

Here’s how to decide:

🔹 For Data Exploration & Cleaning → SQL, Python (Pandas), Excel
🔹 For Dashboarding & Reporting → Tableau, Power BI, Looker
🔹 For Big Data Processing → Spark, Snowflake, Google BigQuery
🔹 For Statistical Analysis → R, Python (Statsmodels, SciPy)
🔹 For Machine Learning → Python (Scikit-learn, TensorFlow)

Ask yourself:
✅ What type of data am I working with?
✅ Do I need interactive dashboards?
✅ Is coding necessary, or do I need a no-code tool?
✅ What does my team/stakeholder prefer?

The best tool is the one that helps you solve problems efficiently.

BY Data science/ML/AI