The difficulty level of data science can vary depending on various factors, including your background, experience, and the specific tasks or problems you’re working on. Here are a few points to consider:

1. Skill requirements: Data science involves a combination of skills from mathematics, statistics, programming, and domain knowledge. If you’re already familiar with these areas, you may find it easier to grasp the concepts and techniques used in data science. However, if you’re starting from scratch, there will be a learning curve involved in acquiring these skills.

1. Mathematical and statistical concepts: Data science often relies on mathematical and statistical principles to analyze and interpret data. Understanding concepts like probability, linear algebra, and hypothesis testing can be challenging for some individuals. However, with dedication and practice, these concepts can be learned.

1. Programming skills: Proficiency in programming languages like Python or R is crucial for implementing data science algorithms, handling data, and building models. Learning a programming language and becoming comfortable with coding may take some time and effort, especially if you have no prior programming experience.

1. Real-world complexity: Data science projects often involve working with messy, real-world data, which may have missing values, outliers, or inconsistencies. Dealing with such complexities can be time-consuming and require creativity in finding appropriate solutions.

1. Rapidly evolving field: The field of data science is dynamic, with new techniques, algorithms, and tools emerging regularly. Keeping up with the latest advancements and continuously learning can be challenging but also exciting.

Despite the challenges, many find data science to be a rewarding and fulfilling field. It offers opportunities to solve complex problems, make data-driven decisions, and gain insights from vast amounts of information. With perseverance, continuous learning, and hands-on experience, it is possible to overcome the difficulties and succeed in data science.