The difficulty level of learning data analytics depends on several factors, including your prior knowledge and experience with data analysis, your technical background, and the level of complexity of the analytics tools and techniques you are learning. Here are some things to consider when assessing whether data analytics is hard to learn:

1. Technical skills: Data analytics requires proficiency in statistical analysis, data modeling, and data visualization. If you have a technical background in computer science, engineering, or mathematics, you may find it easier to learn these skills. However, even without a technical background, it is possible to learn data analytics with dedication and practice.

2. Tools and software: There are many tools and software available for data analytics, such as Excel, R, Python, SQL, and Tableau. These tools have varying levels of complexity and require different levels of technical knowledge. Some tools may be more user-friendly than others, but mastering any tool or software requires time and practice.

3. Data quality and availability: The quality and availability of data can also affect the ease of learning data analytics. Some datasets may be clean and well-structured, while others may be noisy and unstructured, making it harder to draw insights.

4. Learning resources: The availability of quality learning resources, such as online courses, tutorials, and forums, can make a big difference in the ease of learning data analytics.
   
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Overall, learning data analytics can be challenging, but with dedication, practice, and access to quality learning resources, it is possible to master the necessary skills. It is important to start with the basics, gain proficiency in the tools and techniques, and continue to learn and adapt to new trends and advancements in the field.