Birchwood University 4 credits MDS
This course identifies the foundational concepts of Exploratory Data Analysis, considering its critical role in data analysis and decision-making. Students use Python's Pandas library to load datasets from formats such as CSV and Excel, applying cleaning and preprocessing techniques to handle missing values using fillna() and dropna(). Learners explain continuous and categorical data types, performing conversions, normalization, and standardization with tools like sklearn's MinMaxScaler and StandardScaler. The course covers data manipulation using groupby() and aggregate functions, time series datasets, and data merging through concatenation, key-based merging, and join operations. Students use Matplotlib and Seaborn for univariate and bivariate visualizations, including histograms, boxplots, KDE plots, pair plots, and heatmaps, and identify outliers using Z-Scores and IQR values.
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