A Data Scientist is a professional who uses their skills in mathematics, statistics, programming, and domain knowledge to extract insights and knowledge from large amounts of data. They are responsible for collecting, analyzing, and interpreting complex data sets to uncover patterns, trends, and correlations that can help organizations make data-driven decisions.

 Key tasks performed by data scientists:

Data Collection and Cleaning: Data scientists gather data from various sources, such as databases, APIs, or external files. They also clean and preprocess the data to remove inconsistencies, missing values, and errors.

Exploratory Data Analysis (EDA): Data scientists conduct EDA to understand the characteristics of the data, identify outliers, visualize data distributions, and find initial patterns or relationships.

Statistical Analysis and Modeling: They apply statistical techniques and mathematical models to analyze the data. This may involve hypothesis testing, regression analysis, clustering, classification, or other advanced algorithms.

Machine Learning: Data scientists often utilize machine learning algorithms to build predictive models and make accurate forecasts or classifications based on the data.

Data Visualization and Communication: They use various visualization techniques and tools to effectively communicate their findings to stakeholders and decision-makers. Visualizations help in presenting complex insights in a more understandable and actionable manner.

High school subjects that can be beneficial.

Mathematics: A strong foundation in mathematics is crucial for data science. Focus on subjects like algebra, calculus, and statistics to develop quantitative and analytical skills.

Computer Science: Take courses that introduce you to programming languages like Python, R, or Java. Programming skills are essential for data manipulation, analysis, and implementing machine learning algorithms.

Statistics: Courses in statistics will help you understand probability, hypothesis testing, and statistical modeling, which are fundamental for analyzing and interpreting data.

Data Analysis and Visualization: Look for courses that cover data analysis techniques, data visualization tools, and exploratory data analysis.               These skills will help you make sense of data and present insights effectively.

Domain-Specific Knowledge: Consider taking courses related to the industries or domains you are interested in. For example: if you are interested in healthcare data analysis, take biology or health science courses to gain domain knowledge.

Remember, Data science is an interdisciplinary field, so developing a well-rounded skill set in math, statistics, programming, and domain knowledge will give you a solid foundation for a career in data science. Additionally, pursuing higher education in data science, computer science, or a related field can provide further specialization and expertise in the field.