Analyst Dojo

You are reading the 2026 curriculum (exams up to November 2026). Sitting in February 2027 or later? Go to the 2027 Quantitative Methods guide.

Learning Module 11 of Quantitative Methods 6–9% of the exam

Introduction to Big Data Techniques

3 lessons. About 16 minutes in cram mode, 70 minutes in full.

Start with lesson 1 Learn with Sensei Read the module summary

Lessons

  1. 1
    Big Data, the four Vs and alternative data

    describe 6 min cram, 24 min full

  2. 2
  3. 3

What the exam expects you to do

  1. Describe aspects of “fintech” that are directly relevant for the gathering and analyzing of financial data.Big Data, the four Vs and alternative data
  2. Describe Big Data, artificial intelligence, and machine learning.Big Data, the four Vs and alternative data
  3. Describe applications of Big Data and Data Science to investment management.Data science: processing, visualization and text analytics

Module summary

The whole module on one sheet, organised by lesson: what to memorize, how the ideas fit together, the formulas and the key terms. No paragraphs, just what you need to revise.

How deeply to learn each part
Know the idea
Understand it in general. It frames other topics, but you will not calculate with it.
Use it
Understand it well enough to solve a question with it. Recognize when it applies.
Memorize
Learn it by heart. The exam will not give it to you.
1

Big Data, the four Vs and alternative data

Known

MemorizeThe four Vs

VAsksThink
VolumeHow much data?millions or billions of data points
VelocityHow fast is it created and needed?real time or near real time
VarietyHow many forms?structured, semi-structured, unstructured
VeracityCan it be trusted?credibility and reliability of the source

Memory hook V-V-V then the one that is about trust: three describe the data, the fourth (veracity) describes whether you should believe it. Variety = forms, Veracity = verify.

Know it cold Three Vs define Big Data; veracity is added when Big Data is used for inference or prediction.

MemorizeThree sources of alternative data

SourceUsuallyExamples
Individualsunstructuredreviews, posts, web searches, clicks
Business processesstructuredcard transactions, scanner data, supply-chain records (corporate exhaust)
Sensors (incl. IoT)unstructured, enormous volumesatellites, phones, cameras, smart meters

Memory hook People, Processes, Probes. Ask who or what created the data.

Know it cold Card transaction data are business-process data. Business-process data are often leading, near real-time indicators; company reports lag.

Key terms

  • fintech Know the idea
  • Big Data Memorize
  • volume Memorize
  • velocity Memorize
  • variety Memorize
  • veracity Memorize
  • structured data Memorize
  • unstructured data Memorize
  • alternative data Memorize
  • corporate exhaust Use it
  • internet of things Use it
  • scraping Know the idea
What makes data "Big"
Big Data
  • Volume Memorize how much
  • Velocity Memorize how fast it is created and delivered
  • Variety Memorize how many forms
    • Structured tables, fixed fields
    • Semi-structured tagged pages such as HTML
    • Unstructured text, images, audio, video
  • Veracity Memorize credibility and reliability, added for inference
Where alternative data come from
Alternative data
  • Individuals Memorize mostly unstructured
    • Social media, news, reviews
    • Web searches, clicks
  • Business processes Memorize mostly structured
    • Transactions and sales data
    • Corporate exhaust supply chain, banking records, scanner data
  • Sensors Memorize huge volume
    • Satellites, cameras, phones
    • Internet of Things connected devices and buildings
2

AI and machine learning: supervised, unsupervised, deep

Known

MemorizeThree kinds of machine learning

TypeDataTaskExample
Supervisedlabeled inputs and outputspredict or classifydefault or no default, from past bonds with known outcomes
Unsupervisedno labelsfind structure, groupclusters of stocks by return behavior, no sector labels
Deep learningeitherlayered, non-linear patternsimages, speech, text

Memory hook Supervised has a teacher (the labels). Unsupervised has none. Deep learning is a technique (many hidden layers), not a third kind of data.

Know it cold Deep learning can be supervised or unsupervised. ML = "find the pattern, apply the pattern".

MemorizeOverfitting vs underfitting

OverfittedUnderfitted
What it didlearned the data too precisely, noise as signaltreated real patterns as noise
Model istoo complextoo simple
In-sampleexcellentpoor
New datapoorpoor

Memory hook Over = memorized the past (cannot generalize). Under = never learned it. Compare training score with test score.

Know it cold Three data sets: training (learn), validation (tune), test (judge on unseen data).

Key terms

  • artificial intelligence (AI) Memorize
  • expert system Know the idea
  • neural networks Know the idea
  • machine learning (ML) Memorize
  • training data Use it
  • supervised learning Memorize
  • unsupervised learning Memorize
  • deep learning Memorize
  • overfitting Memorize
  • underfitted Memorize
Types of machine learning
Machine learningfind the pattern, apply the pattern
  • Supervised learning Memorize labeled inputs and outputs; predicts or classifies
  • Unsupervised learning Memorize unlabeled data; finds structure and groups
  • Deep learning Memorize neural networks with many layers
    • Can be supervised
    • Can be unsupervised
Getting the fit right
Model fit
  • Underfitted Memorize too simple; misses real patterns
  • Well fitted captures signal; generalizes
  • Overfitted Memorize too complex; learns noise; fails on new data
3

Data science: processing, visualization and text analytics

Known

MemorizeFive data processing methods

StepIt is aboutExample
Capturecollecting data and converting to a usable format; low vs high latencymillisecond price feed
Curationcleaning: find bad data, adjust for missing datadrop duplicate trades
Storagehow data are recorded, archived, accessed; database designtables vs document store
Searchquerying large data to find contentevery mention of a supplier
Transfermoving data from source or storage to the analysis tooldirect exchange feed

Memory hook C-C-S-S-T: Capture, Curate, Store, Search, Transfer. Clean, store, find, move. Curation = cleaning, not archiving.

Know it cold Low latency matters for automated trading on real-time prices; high-latency systems do not need real-time data.

MemorizeVisual tools and NLP

ToolShows
Tag cloudword frequency (bigger = more often)
Mind maphow concepts relate
Heat mapvalues by color (e.g. correlations)
NLPinterprets human language, text and speech: translation, speech recognition, sentiment, topic analysis

Memory hook Cloud = counts, map = connections.

Know it cold Structured data: tables, charts, trends. Unstructured or big data: heat maps, tree diagrams, network graphs, interactive 3D.

Key terms

  • data science Memorize
  • data capture Memorize
  • latency Use it
  • data curation Memorize
  • data storage Memorize
  • search Memorize
  • transfer Memorize
  • data visualization Use it
  • tag cloud Memorize
  • mind map Memorize
  • heat map Know the idea
  • text analytics Memorize
  • natural language processing (NLP) Memorize
  • sentiment analysis Use it
Data processing methods
Data processing
  • Capture Memorize collect and format; latency
  • Curation Memorize clean; fix errors and gaps
  • Storage Memorize record, archive, access; database design
  • Search Memorize query to locate content
  • Transfer Memorize move to the analysis tool
Visualization tools
Data visualization
  • Structured data
    • Tables, charts, trend lines
  • Big or unstructured data
    • Heat maps, tree diagrams, network graphs
    • Interactive 3D graphics color, size, shape add dimensions
  • Text data Memorize
    • Tag cloud Memorize word frequency
    • Mind map Memorize links between concepts

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