Data analytics can be tough, but if it's «hard» depends on your background, learning preference, and how high a level of mastery you're looking to achieve. Here's the breakdown to provide a better insight:

✅ What Makes Data Analytics Accessible:
Foundational Skills Are Learnable: Stuff like data cleaning, simple statistics, and tool usage such as Excel, SQL, or Tableau is highly learnable even for an absolute beginner.
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Resources Are Plentiful: There are plenty of beginner-friendly tutorials, courses, and online communities.

Tools Are Getting Easier: Tools like Power BI, Google Data Studio, and even Python libraries such as Pandas simplify many of the processes.

⚠️ What Can Make It Hard:
Math & Stats: You don't have to be a genius with numbers, but a good understanding of statistics does matter—particularly for advanced analysis and predicting.

Coding Skills: To do more complex analytics, you need to learn Python, R, or SQL. If you don't have coding experience, there's a learning curve.
Data Complexity: Data in the real world is messy, incomplete, or inconsistent. Data cleaning and preparation take time.
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Problem-Solving Mindset: You must be at ease thinking analytically, raising the right questions, and critically interpreting results.

TL;DR:
Simple to begin, more challenging to master.
If you're comfortable with logical thinking, learning new tools, and a bit of math/stats, you'll probably find data analytics difficult but very achievable.
If you want to be a data scientist or machine learning specialist, that's a more technical route that demands greater technical prowess.
Would you like assistance determining where to begin or what analytics job best fits your background?

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