Insight comes from investigation before modelling, and this course focuses on that stage. It covers loading and inspecting data with Pandas, handling missing values and outliers, applying descriptive statistics, building static visualizations with Matplotlib and Seaborn, and creating interactive plots with Plotly.

This Course Offers

• Exploratory data analysis fundamentals and where EDA fits within the wider data science workflow
• Data loading, inspection, and manipulation using Pandas, with cleaning for missing values, outliers, and incorrect data types
• Descriptive statistics covering distributions, central tendencies, correlations, and how to interpret them
• Static visualization using Matplotlib and Seaborn for univariate and bivariate analysis
• Interactive visualization with Plotly for dynamic exploration and presentation, plus basic feature engineering to enhance understanding

Why We Love This Course

1. Basic Python knowledge is expected, so the course focuses on analysis rather than programming fundamentals.
2. It explains the why behind each technique, which matters more than knowing which function to call.
3. Plotly is included alongside Matplotlib and Seaborn, giving you interactive options for presentation.
4. Real world datasets and case studies run throughout, so the workflow connects to actual analysis tasks.

Data analysis starts with understanding what you have, and skipping that step is how models get built on flawed assumptions. Do you want to jump straight to modelling, or know your data well enough to trust the results? Enrollment is open, and no prior data science experience is needed.

• This course is perfect for aspiring data analysts and data scientists building a foundation in EDA.
• It suits students and professionals applying Python skills to real world data problems.
• Business intelligence professionals enhancing data storytelling, and researchers needing to interpret survey or experimental data, will find the visualization work directly useful.

• A basic understanding of Python programming concepts, including variables, data types, loops, and functions, is expected.
• Familiarity with basic Python data structures like lists and dictionaries will help.
• No prior experience with data science, machine learning, or advanced statistics is required.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.