Python for Data Analysis
Analyze and visualize real-world data with pandas, NumPy, and matplotlib. Load, clean, transform, merge, and chart datasets.
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About This Course
Course Curriculum
12 Lessons
NumPy Arrays and Vectorized Operations
NumPy - Lab Exercises
ndarray basics, shape, dtype, array creation (zeros/ones/arange/linspace/random), vectorized operations vs loops, aggregations (sum/mean/std/min/max with axis), boolean indexing and np.where
pandas Fundamentals Series and DataFrames
pandas Fundamentals - Lab Exercises
Series creation and operations, DataFrame from dicts/lists/CSV, column access, .head()/.info()/.describe(), reading data with pd.read_csv/json/excel, selecting with []/loc/iloc/query, adding and modifying columns with .apply()/.map()/np.where
Data Cleaning and Transformation
Data Cleaning - Lab Exercises
Missing data (isna/fillna/dropna strategies), duplicates (duplicated/drop_duplicates), type conversion (astype/to_datetime/to_numeric), string operations (.str accessor), reshaping (melt/pivot_table/stack/unstack)
Grouping Aggregation and Merging
Grouping and Merging - Lab Exercises
groupby single and multi-column, .agg() with named aggregations, .transform(), pd.merge (inner/left/outer joins), pd.concat row and column-wise, time-based grouping with resample and Grouper
Data Visualization with matplotlib
Visualization - Lab Exercises
plt.plot/show, figure and axes objects, chart types (line/bar/scatter/histogram/pie), customization (titles/labels/legends/colors/grid), multi-panel with subplots, saving figures with savefig
Capstone Briefing Client Data Report
Capstone Client Data Report
Capstone combining all data analysis skills: load 3 messy CSVs, clean all datasets, merge into analysis DataFrame, compute business metrics, generate multi-panel matplotlib visualization, export summary CSV and PNG