Python Data Analysis: Exploratory Data Analysis (EDA)

Learn exploratory data analysis (EDA) with Python to understand your datasets before building machine learning models. Use Pandas, NumPy, Matplotlib, and Seaborn to inspect data, investigate missing values, visualize distributions, and detect outliers. This course helps you move from knowing basic Python to understanding what your data contains, which problems need attention, and how your findings can guide further analysis and preprocessing. Build Your Foundation in Python Data AnalysisLoading a dataset is only the beginning. Before using it for machine learning, you need to understand its structure, identify potential quality issues, and interpret its numerical and categorical features. You will learn a structured approach to data analysis using Python, with explanations and practical demonstrations that connect individual techniques to the wider EDA workflow. What You Will LearnUnderstand exploratory data analysis and its role in data science and machine learning. Explain the difference between EDA and data preprocessing. Load datasets and inspect their structure using Python and Pandas. Examine data types, dataset size, and memory usage. Identify missing values and visualize missing-data patterns. Consider how missing data can affect your analysis and subsequent decisions. Perform univariate analysis on numerical and categorical features. Interpret measures of central tendency and dispersion. Explore distributions, skewness, and kurtosis. Detect outliers and consider what they mean in the context of your dataset. Examine category frequencies and Boolean feature counts. Tools Used in the CourseYou will work with Pandas for dataset inspection and analysis, NumPy for numerical operations, and Matplotlib and Seaborn for data visualization. You w…