Becoming a data scientist involves acquiring a combination of technical skills, domain knowledge, and practical experience. Here is a roadmap to help guide you through the key steps: Educational Background: Foundational Knowledge: Obtain a strong foundation in mathematics and statistics. Focus on areas like linear algebra, calculus, probability, and inferential statistics. Data Manipulation and Analysis: Gain proficiency in libraries like Pandas and NumPy for data manipulation and analysis. Programming Skills: Learn a programming language commonly used in data science such as Python or R. Python is widely preferred for its versatility and extensive libraries. Advanced Data Analysis and Visualization: Data Visualization: Learn data visualization tools like Matplotlib, Seaborn, and Plotly to effectively communicate insights. Exploratory Data Analysis (EDA) Master techniques for exploratory data analysis to understand the characteristics of datasets. Machine Learning: Basic Concepts: Und