Introduction:
Becoming a data scientist requires a strong foundation in mathematics, statistics, programming, and analytical skills. While academic performance is an important aspect, it is not the sole determinant of your ability to become a successful data scientist. Let's evaluate your current marks and their implications for your future as a data scientist.

Evaluation of Marks:
1. Half-Yearly Examination: 85%
2. Annual Examination (Last Year): 89%

Importance of Academic Performance:
Academic performance is an important factor in determining your capabilities and understanding of the subjects required for data science. Good grades indicate a solid understanding of the concepts taught in class, which is crucial for higher-level analysis and problem-solving.

Mathematics and Statistics:
1. Data science heavily relies on mathematical and statistical concepts such as calculus, linear algebra, probability, and regression analysis.
2. Your performance in these subjects, as reflected by your marks, will influence your ability to grasp and apply these concepts effectively.

Programming Skills:
1. Proficiency in programming languages like Python, R, or SQL is essential for data manipulation, analysis, and visualization.
2. While your academic performance may not directly reflect your programming skills, it is advantageous to have a strong foundation in coding.

Analytical Skills:
1. Data scientists need to possess strong analytical skills to interpret, analyze, and draw insights from large datasets.
2. Your academic performance can indicate your ability to think critically, solve problems, and analyze complex situations effectively.

Other Factors:
1. Extracurricular activities: Participation in activities such as coding competitions, data science projects, or internships can demonstrate your practical skills and passion for the field.
2. Self-learning and continuous improvement: Data science requires a lifelong commitment to learning and staying updated with the latest industry trends. Your motivation and dedication to self-improvement are equally important.

Conclusion:
While your current marks demonstrate a good level of academic performance, they alone do not determine your potential to become a data scientist. Focus on developing a strong foundation in mathematics, statistics, programming, and analytical skills. Engage in extracurricular activities, pursue self-learning opportunities, and stay motivated to excel in the field of data science. With the right combination of skills, knowledge, and practical experience, you can certainly achieve your goal of becoming a data scientist.