• Basic understanding of computer operations and file management.
• Introductory or elementary programming knowledge (preferably Python, but not required).
• Ability to install software and work with environments like Jupyter Notebook or VS Code.
• Basic familiarity with statistical concepts such as averages and standard deviation (optional but helpful).
• Willingness to troubleshoot coding errors and technical issues.
• Ability to read raw datasets and understand their structure.
• Problem-solving skills and readiness to learn through experimentation.
• Interest in data-driven thinking and analytical decision-making.

• Write and execute Python code specifically for data analysis tasks.
• Use NumPy and Pandas for data manipulation, cleaning, and transformation.
• Import data from various sources including CSV, Excel, and APIs.
• Clean datasets and handle missing, duplicated, and inconsistent data.
• Reshape, sort, filter, and merge multiple datasets effectively.
• Perform descriptive and statistical analysis to uncover trends and patterns.
• Create a wide range of visualizations using Matplotlib and Seaborn.
• Develop visual reports and basic dashboards to present insights clearly.
• Interpret relationships between variables and derive data-driven conclusions.
• Complete an end-to-end data analysis project from collection to reporting.
• Apply analytical thinking and problem-solving to real-world scenarios.
• Communicate analytical findings in a clear and professional manner to decision-makers.

This course provides a comprehensive and practical introduction to data analysis using Python, designed to equip learners with the essential skills needed to work confidently with real-world data. Throughout the program, students will explore the complete data analysis workflow—from gathering raw datasets to cleaning, transforming, analyzing, visualizing, and presenting meaningful insights. Learners will gain hands-on experience with the most widely used Python libraries in the data analytics field, including NumPy, Pandas, Matplotlib, and Seaborn. The course emphasizes practical learning through interactive notebooks, step-by-step exercises, and real datasets from domains such as business, finance, marketing, technology, and public data. In addition to technical proficiency, the course focuses on developing the analytical mindset required to approach data-driven challenges. Students will learn how to ask the right questions, structure analytical problems, identify patterns, and communicate findings effectively to both technical and non-technical audiences. By the end of the course, learners will be capable of performing end-to-end data analysis projects, building visual reports, and supporting data-informed decision making—skills that are highly valuable in fields such as data analytics, business intelligence, research, software development, and more.