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Curriculum / Phase 3 · Career Pathways

Quantitative Data Engineering

Explore the planned quantitative data engineering programme, from reliable financial datasets to reproducible research pipelines.

This programme is planned. The outcomes below describe its intended direction; you can use the external resources now to prepare independently.

Why study Quantitative Data Engineering?

A research result is only as trustworthy as the dataset that produced it. This planned programme will focus on data structures and workflows that preserve timing, quality and lineage from ingestion to research use.

Who this is for

For Python users with basic database familiarity who want to support reliable financial research. The programme is planned; start with the open resources below.

Intended learning outcomes

  • Design financial datasets with explicit keys and timestamps.
  • Understand ingestion, quality, lineage and versioning.
  • Prepare point-in-time research data and reproducible queries.

An example of the work this prepares you for

An example goal for independent preparation is a versioned dataset with quality checks, documented timestamps and a reproducible query.

Prerequisites and suggested preparation

You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.

The goal is to use the foundations in context: explain the assumptions, carry out a calculation and interpret the result. If one of those steps feels unfamiliar, review the linked preparation first.

A preview of the learning journey

The proposed learning journey is organised around these capabilities.

Design a useful dataset

Represent financial entities, observations and changes consistently.

Preserve the information history

Distinguish an event date from the date information became available.

Make a pipeline inspectable

Trace a research table back to its source and processing choices.

Free resources to start learning

Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.

  1. University course · Free

    Introduction to Database Systems

    UC Berkeley · CS 186

    Study relational modelling, query execution and storage through university materials.

  2. Documentation · Free

    PostgreSQL tutorial

    PostgreSQL Global Development Group

    Practise SQL and relational concepts with the official tutorial.

  3. Textbook · Free

    Python for Data Analysis

    Wes McKinney · third edition

    Use the data-cleaning and file-format chapters for research preparation.

  4. University course · Free

    The Missing Semester of Your CS Education

    MIT · notes and video lectures

    Review version control and automation practices for repeatable workflows.

Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.

Find more papers and learning materials in the SelfMadeQuant Directory.

Where this programme fits

  • Portfolio, Factor and Asset-Management Quant · elective programme
  • Quant Developer and Trading Systems · required programme

Continue preparing with the free resources, or explore the programmes available now.

Explore the curriculum
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