Build the core data-handling skill every ML project depends on: loading, exploring and manipulating real datasets with NumPy and pandas. You will vectorise numerical work, wrangle DataFrames, clean missing values, merge tables and produce quick exploratory plots — the everyday groundwork before any model is trained.
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Why NumPy — arrays versus Python lists — free to watch, no account needed.
What you'll learn
- Load, explore and manipulate real datasets using NumPy and pandas
- Write vectorised NumPy operations instead of slow Python loops
- Select, filter and group DataFrame rows to answer questions about data
- Handle missing values and merge multiple tables into one tidy dataset
- Produce quick exploratory plots with matplotlib to understand a dataset
Syllabus
NumPy — Arrays and Vectorised Thinking
Why NumPy — arrays versus Python listsFree preview
Vectorised operations, broadcasting and indexing
pandas — Series and DataFrames
Series, DataFrames and loading CSV files
Indexes, dtypes and selecting columns
Selecting, Filtering and Grouping
Selecting and filtering with loc, iloc and masks
Grouping and aggregation with groupby
Cleaning, Combining and Exploring
Handling missing values
Merging tables and quick exploratory plots
Lab — 10 Exercises & Solutions
Exercises 1–5
Exercises 6–10