Data Foundations
Turn raw data into something you can query, analyze, and explain. Begin with NumPy, then work through cleaning, SQL, and exploratory analysis.
Prepare data
Inspect and transform arrays and clean tabular datasets.
Query data
Answer analytical questions with joins, CTEs, and window functions.
Communicate findings
Create clear charts and explain evidence and limitations.
NumPy Foundations
Before you start
Basic Python and environment setup.
What you’ll learn
- Creating and inspecting arrays
- Shapes, dimensions, sizes, and data types
- Indexing and slicing
- Boolean masks
- Reshaping and transposing
- Broadcasting
- Vectorized operations
- Aggregations and the axis parameter
Learn and practise
NumPy: The Absolute Basics for Beginners
Required. Complete this introductory guide, covering array creation, shapes, indexing, and basic operations before the linear algebra notebooks.
CodeSignal: NumPy Basics
Required. Complete the course and its practice exercises; demonstrate filtering, reshaping, transforming, and summarizing multidimensional arrays.
What you’ll build
You can filter, reshape, transform, and summarize multidimensional data using NumPy without unnecessary Python loops.
Pandas and Data Cleaning
Before you start
Basic Python, environment setup, and introductory NumPy.
What you’ll learn
- Series and DataFrames
- Loading and exporting tabular data
- Inspecting rows, columns, and data types
- Selecting, filtering, and sorting
- Grouping and aggregation
- Missing values
- Duplicate records
- Incorrect data types
- Inconsistent categories
- Creating and transforming columns
- Merging and concatenating datasets
Learn and practise
Kaggle Learn: Pandas
Required. Complete the course and all exercises, including selection, grouping, aggregation, and combining tabular data.
Kaggle Learn: Data Cleaning
Required. Complete the course and all exercises; produce an analysis-ready dataset and explain important cleaning decisions.
What you’ll build
You can identify data-quality problems, produce an analysis-ready dataset, and explain every important cleaning decision.
SQL
Before you start
Basic data-handling concepts; no database experience is required.
What you’ll learn
- Relational tables and keys
- SELECT, aliases, and calculated columns
- WHERE and ORDER BY
- Aggregate functions
- GROUP BY and HAVING
- Inner and outer joins
- Subqueries
- Common table expressions
- CASE
- NULL handling
- Basic window functions
- Indexing intuition
Learn and practise
SQLTutorial.org
Required. Work through the tutorial, including examples and quizzes for the SQL topics listed in this section.
HackerRank SQL
Required. Practise the Basic and Intermediate SQL challenges.
What you’ll build
You can answer analytical questions involving filtering, aggregation, joins, CTEs, conditional logic, and window functions.
Exploratory Analysis and Visualization
Before you start
Pandas and data cleaning, plus core descriptive statistics.
What you’ll learn
- Understanding what rows and columns represent
- Inspecting dataset structure and quality
- Descriptive statistics
- Distributions and unusual values
- Outlier investigation
- Relationships between variables
- Comparing groups
- Selecting appropriate chart types
- Histograms, bar charts, line charts, and scatter plots
- Labels, titles, legends, and readable presentation
- Communicating findings and limitations
Learn and practise
Kaggle Learn: Data Visualization
Required. Complete the course, including its exercises and final project; communicate findings and limitations through readable charts.
What you’ll build
Produce a concise exploratory analysis containing appropriate visualizations, evidence-supported findings, and clearly stated limitations.
Workload
Use the provisional estimates where supplied. Other workloads remain pending pilot. Include implementation, debugging, review, and revision.
Finish this module
Keep the required outputs and explain your decisions. Diagnose a relevant failure during review and revise incomplete evidence after feedback. Resource completion supports progress; the stated practical requirements determine completion.
