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Market Intelligence & Financial Data EngineeringEnrollment opening soon

Financial News & Sentiment Data Engineering

Engineer pipelines that collect, clean, deduplicate, tag and score financial news and text data, producing point-in-time sentiment datasets suitable for research.

  • Duration7 weeks
  • LevelIntermediate
  • Modules7
  • Lessons28
Enroll nowView curriculum
Financial News & Sentiment Data Engineering
Proposed fee$999USD · 7 weeks

Course overview

This course covers the data engineering side of financial text: getting news, filings and other documents into clean, time-stamped, entity-tagged datasets. You will build ingestion with respect for source terms, deduplicate and normalize text, link documents to securities, and generate sentiment features with both lexicon methods and transformer models. A strong focus on timestamps and point-in-time correctness ensures that downstream research does not leak future information.

  • Text data sourcing
  • Cleaning and deduplication
  • Entity linking
  • Sentiment scoring
  • Point-in-time datasets
  • Text pipeline operations

What you will learn

  • Design ingestion for news, filings and text feeds that respects source terms and rate limits
  • Clean, normalize and deduplicate documents, including near-duplicate detection
  • Link documents to companies and securities using named entity recognition and reference data
  • Generate sentiment features with lexicon methods and pretrained transformer models
  • Publish point-in-time, versioned text datasets with documented timestamps and lineage

Curriculum

7 modules · 28 lessons · 7 weeks. Curriculum preview; the final syllabus may change before enrollment opens.

  • News, filings, transcripts and releases
  • Licensing, terms of use and robots rules
  • Document metadata and timestamps
  • Designing a text data model

  • Polling, feeds and scheduled crawls
  • Rate limiting and retry logic
  • Raw versus processed storage layers
  • Full-text search with PostgreSQL

  • HTML and PDF text extraction
  • Language detection and normalization
  • Exact and near-duplicate detection
  • Story clustering basics

  • Named entity recognition with spaCy
  • Mapping mentions to securities
  • Handling ambiguous and changing names
  • Evaluating linking accuracy

  • Lexicon-based financial sentiment
  • Transformer models for classification
  • Aggregating scores by entity and time
  • Measuring feature quality and drift

  • Publication versus ingestion timestamps
  • Revisions and corrections
  • Avoiding look-ahead leakage
  • Versioned datasets and data cards

  • Monitoring volume and model drift
  • Cost and throughput management
  • Reproducible reprocessing
  • Capstone: a research-ready sentiment dataset

Practical projects

01

Build an ingestion pipeline for public filings and news-style documents with deduplication

02

Create an entity linking step that maps company mentions to a securities master

03

Produce a point-in-time sentiment dataset with versioning and a data card

Skills you will practice

  • Python
  • spaCy
  • Hugging Face Transformers
  • PostgreSQL
  • Text deduplication
  • Data lineage

Requirements

Who it's for

  • Data engineers working with unstructured financial content
  • NLP practitioners who need production-quality financial datasets
  • Researchers building text-based features for quantitative studies

Prerequisites

  • Financial Data Engineering Foundations or equivalent pipeline experience
  • Working Python and pandas skills
  • NLP & Financial News Intelligence is a helpful companion course

Learning format

  • Self-paced lessons with notebook and pipeline labs
  • Weekly engineering assignments on a shared text dataset
  • Code and data quality reviews
  • Live sessions on model and pipeline design in cohort runs

Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.

Frequently asked questions

Self-paced lessons with notebook and pipeline labs Weekly engineering assignments on a shared text dataset Code and data quality reviews

Financial Data Engineering Foundations or equivalent pipeline experience. See the full list under Requirements.

Yes. Completing the lessons and assessed projects earns a Quantzivo certificate of completion. It is a completion credential issued by Quantzivo, not an accredited qualification.

The $999 USD fee is a proposed launch price. Final fees, schedules, refund terms and any regional options are confirmed before any payment is taken.

Quantzivo teaches technical, data and research skills. It does not provide investment advice, trading signals or any promise of returns. Trading involves risk, including loss of capital.
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