Transitioning from engineering to data science or big data analytics can be challenging, but the difficulty level depends on your background, skills, and willingness to adapt. Below is a breakdown of key aspects that can determine the difficulty level:

1. Technical Overlap: Advantage for Engineers
Programming Skills: Engineers, especially those from software, computer science, or IT backgrounds, often already have a strong foundation in programming (e.g., Python, Java, or SQL), which is essential in data science and big data analytics.
Mathematics and Statistics: Engineers familiar with linear algebra, calculus, and basic probability will find the transition smoother since these are fundamental to machine learning and analytics.
Problem-Solving Mindset: Engineers are trained to solve complex problems systematically, which aligns with the analytical thinking required in data science.
Difficulty Level: Moderate for technical engineers; non-technical engineers may need to work harder on statistics and coding.

2. Learning Curve for Data Science Concepts
New Skills: Transitioning engineers must learn specialized data science concepts like:
Machine learning algorithms
Data wrangling and preprocessing
Big data frameworks (e.g., Hadoop, Spark)
Tools like Tableau, Power BI, and Jupyter Notebooks
Statistics and Probability: A deeper understanding of these topics may require significant effort, especially for engineers with minimal exposure to them during their studies.
Difficulty Level: High if you lack prior experience in statistics, machine learning, or data visualization.

3. Domain Knowledge
Data science and big data analytics often require domain knowledge to interpret results meaningfully.
Engineers may face challenges in transitioning to domains they’re unfamiliar with, such as finance, healthcare, or marketing analytics.
However, leveraging existing domain expertise (e.g., mechanical engineers in IoT data or civil engineers in infrastructure analytics) can ease this transition.
Difficulty Level: Moderate if you can focus on industries related to your engineering background; higher if you're venturing into unfamiliar fields.

4. Tools and Technologies
Engineers must learn data science-specific tools and technologies, such as:
Programming: Python, R, and SQL.
Big Data: Hadoop, Spark, and Kafka.
Databases: NoSQL databases like MongoDB and Cassandra.
Data Visualization: Tableau, Matplotlib, and Power BI.
Cloud Platforms: AWS, Azure, and Google Cloud.
While engineers may already have familiarity with some tools (e.g., databases or cloud platforms), mastering new ones will require dedicated effort.
Difficulty Level: Moderate if you’re used to quickly learning new tools; higher if you need to start from scratch.

5. Time Commitment
Duration: Transitioning to data science typically requires 6 months to 1 year of intensive learning, depending on the depth of skills you need to acquire.
Certifications and Courses: Enrolling in structured programs, bootcamps, or online courses can significantly accelerate the learning process, but it requires discipline and consistent effort.
Difficulty Level: Moderate to High, depending on how much time you can dedicate while managing existing commitments.

6. Adaptation to a Data-Driven Mindset
Engineers transitioning to data science must develop a data-centric mindset, focusing on:
Data interpretation and storytelling.
Understanding biases in data.
Collaborating with business teams to solve non-technical problems.
Difficulty Level: Moderate, as this shift often requires stepping out of a purely technical comfort zone.

Link: https://www.sevenmentor.com/data-science-course-in-pune.php