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
Learn to analyze real-world data using Python's most powerful tools. This course covers NumPy arrays and vectorized operations, pandas DataFrames for data manipulation, cleaning messy datasets (missing values, duplicates, type issues), grouping and merging data from multiple sources, and creating professional visualizations with matplotlib. Every exercise uses realistic messy data not pre-cleaned textbook examples. The capstone project has you clean, merge, and visualize a multi-file client dataset into a business report. Requires Python Data Structures and OOP or equivalent. Course 4 of 6 in the Python Learning Path.
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
This course includes:
- 24/7 AI Instructor Support
- Live Lab Environments
- 6 Hands-on Lessons
- Completion Badge
Earn Your Badge
Complete all lessons to unlock the Python for Data Analysis achievement badge.
Python for Data Analysis
Demonstrates proficiency in data analysis with pandas, NumPy, and matplotlib.
Skills You'll Earn
Complete all lessons in this course to earn this badge