Abstract numbers and names of data technologies

Welcome to Numbers Around Us, your go-to resource for mastering analytics programming, business intelligence tools, and the art of data-driven thinking. Whether you’re diving into R, Python, or SQL, exploring Tableau and Power BI, or rethinking how you approach data projects, you’ll find practical insights, tools, and solutions here.

What You Will Find

1. Analytics Programming (R, SQL, Python)

Unlock the potential of your data with step-by-step tutorials, advanced tips, and innovative solutions. Our resources cover:

  • R: From data wrangling to advanced visualizations, learn how to harness the power of R.
  • Python: Explore its versatility, from automation to machine learning.
  • SQL: Master the language of databases for efficient querying and analysis.

2. Business Intelligence (Tableau and Power BI)

Visualize and communicate your insights effectively. Learn how to:

  • Create stunning dashboards and reports in Tableau.
  • Build dynamic, actionable visuals in Power BI.
  • Integrate BI tools into your analytics workflow.

3. Data Philosophy

Analytics is more than tools—it’s a mindset. In this section, we explore:

  • Data management: Best practices for clean and reliable data.
  • Project planning: Strategies for successful analytics projects.
  • Ethics and governance: Ensuring responsible use of data.

Solve Challenges, Gain Insights

One of our standout features is our Challenge Solutions section. Here, we tackle real-world analytics challenges from LinkedIn, offering:

  • Detailed solutions in R and Python.
  • Insights into problem-solving techniques.
  • Tips for applying these skills to your own work.

Why Choose Numbers Around Us?

We combine technical expertise with a passion for data-driven storytelling. Whether you’re a beginner looking for guidance or an experienced analyst refining your craft, our content is designed to inspire and empower you.

Start Your Journey

Dive into our latest articles, explore the challenge solutions, or check out the Data Philosophy section to rethink how you work with data. Let’s build a smarter, more insightful data world—together.

Github LinkedIn