This course is designed to help you learn data analysis using Python in a practical and job-focused way. You will start with the foundations of Python that are most useful for analytics, and then move into working with real datasets using popular libraries like Pandas and NumPy. The learning style is hands-on, so you will practise with datasets, solve small tasks, and build confidence through repeated application.
You will learn how to clean messy data, handle missing values, and prepare datasets for analysis. Then you will explore how to find patterns, trends, and relationships through Exploratory Data Analysis (EDA). You will also learn how to visualise data using charts that communicate insights clearly. Along the way, you will build the habit of writing structured analysis: define the question, prepare the data, analyse results, and summarise findings in a simple, decision-friendly way.
By the end of the course, you should be able to take a raw dataset and convert it into useful insights using Python. You will also create portfolio-ready notebooks and mini projects that show your skills in cleaning, analysis, and reporting. This course is suitable for beginners and for learners who want to strengthen their analytics foundation for internships, entry-level roles, or business-focused projects.
