Data Analysis and Visualization with Python

Progress overview

Course sequence

1. Module 1: Data Analysis Mindset and Notebook WorkflowLearners can turn a vague analysis request into a clear data question, identify row meaning, set up a clean notebook outline, and write an early limitation note before touching the data.
2. Module 2: NumPy Foundations for Data WorkLearners can use NumPy arrays for small numeric work, inspect shape and dtype, select with indexes and masks, calculate with vectorized operations, summarize by axis, and run a repeatable simulation.
3. Module 3: Pandas Core Objects and Data ImportLearners can load tabular data with pandas, explain Series and DataFrame objects, inspect rows, columns, dtypes, missing values, and summaries, then begin a data dictionary.
4. Module 4: Selecting, Filtering, Sorting, and Creating ColumnsLearners can select columns, filter rows, sort records, create vectorized derived columns, and document the rule behind each new analysis column.
5. Module 5: Cleaning Messy DataLearners can handle missing values, duplicates, invalid values, type conversion, dates, text categories, and outliers while preserving raw data and recording decisions in a cleaning log.
6. Module 6: Grouping, Aggregation, Pivoting, and ReshapingLearners can create grouped summaries, named aggregations, pivot tables, crosstabs, and long-form reshaped tables while explaining what one row means after each operation.
7. Module 7: Combining Datasets SafelyLearners can stack similar tables, join related tables, check keys, detect missing or duplicate matches, reconcile row counts, and explain why the joined table is trustworthy enough to use.
8. Module 8: Time Series and Rolling AnalysisLearners can parse dates, create date parts, resample by period, calculate rolling averages, compare equal windows, and explain why descriptive time analysis is not forecasting.
9. Module 9: Exploratory Data Analysis and Data QualityLearners can run structured EDA, profile data quality, inspect distributions and relationships, compare segments, investigate missingness and outliers, and write careful findings with evidence and limits.
10. Module 10: Matplotlib and Visualization FoundationsLearners can use Matplotlib to create line, bar, scatter, histogram, and box plots with clear titles, labels, layout, export, and text equivalents.
11. Module 11: Statistical and Presentation Visualization with SeabornLearners can use Seaborn for relationship, distribution, categorical, faceted, and regression-style exploratory visuals while keeping interpretations careful and non-causal.
12. Module 12: Interactive Visuals, Dashboards, and Capstone StoryLearners can build useful Plotly interactive visuals and package the full capstone portfolio with data question, dictionary, cleaning log, summaries, charts, report, limitations, and reproducibility checks.

Local learning achievements

Privacy and reset

Use only supplied fictional or synthetic data. Download your notebooks before clearing browser data.