Quantitative
Analysis
—
Hard
Skills
CO NT E NT S
01
Mathematics
&
Statistical
Foundations
02
Programming
&
Data
Engineering
03
Quantitative
Modeling
&
Machine
Learning
04
Financial
&
Market
Quantitative
Methods
05
Risk
&
Performance
Analytics
06
Research
,
Backtesting
&
Validation
07
Data
Visualization
&
Reporting
08
Analytical
Tools
&
Platforms
How
to
read
this
list
:
Hard
skills
are
the
measurable
,
teachable
,
and
verifiable
technical
competencies
below
.
Clusters
1–3
are
the
analytical
core
,
clusters
4–6
apply
them
to
markets
,
risk
,
and
research
,
and
clusters
7–8
cover
the
delivery
and
tooling
layer
.
The
cross
-
reference
table
at
the
end
maps
each
cluster
to
its
representative
skills
and
the
tools
most
commonly
used
to
execute
them
.
01
Mathematics
&
Statistical
Foundations
Probability
theory
&
stochastic
processes
Statistical
inference
(
frequentist
&
Bayesian
)
Hypothesis
testing
,
confidence
intervals
,
p
-
values
Linear
regression
(
OLS
,
WLS
,
panel
,
logistic
)
Time
series
analysis
(
ARIMA
,
GARCH
,
VAR
,
cointegration
)
Linear
algebra
&
matrix
decomposition
Multivariable
calculus
&
constrained
optimization
Monte
Carlo
simulation
Principal
component
&
factor
analysis
Non
-
parametric
methods
&
bootstrapping
Experimental
design
&
A
/
B
testing
Numerical
methods
&
numerical
stability
analysis
1 / 4
02
Programming
&
Data
Engineering
Python
for
analytics
(
NumPy
,
pandas
,
SciPy
,
statsmodels
)
R
for
statistical
computing
(
tidyverse
,
data
.
table
)
SQL
&
analytical
query
optimization
MATLAB
/
Octave
for
numerical
work
C
++
for
performance
-
critical
computation
Julia
for
high
-
performance
numerical
analysis
ETL
/
ELT
pipeline
design
Distributed
computing
(
Spark
,
PySpark
)
Workflow
orchestration
(
Airflow
,
dbt
)
API
data
ingestion
&
web
scraping
Version
control
&
reproducible
environments
(
Git
,
containers
)
Cloud
compute
&
storage
(
AWS
,
GCP
,
Azure
)
03
Quantitative
Modeling
&
Machine
Learning
Supervised
learning
(
regression
,
classification
,
ensembles
)
Unsupervised
learning
(
clustering
,
dimensionality
reduction
)
Gradient
boosting
(
XGBoost
,
LightGBM
,
CatBoost
)
Neural
networks
&
deep
learning
Feature
engineering
&
feature
selection
Model
validation
,
cross
-
validation
&
regularization
Bayesian
modeling
(
MCMC
,
PyMC
,
Stan
)
Natural
language
processing
for
text
and
filings
Reinforcement
learning
Model
interpretability
(
SHAP
,
LIME
,
partial
dependence
)
04
Financial
&
Market
Quantitative
Methods
Asset
pricing
models
(
CAPM
,
Fama
-
French
,
APT
)
Derivatives
pricing
(
Black
-
Scholes
,
binomial
trees
,
Monte
Carlo
)
Fixed
income
analytics
(
duration
,
convexity
,
curve
construction
)
Volatility
modeling
&
surface
calibration
Factor
modeling
&
risk
premia
research
Portfolio
optimization
(
Markowitz
,
Black
-
Litterman
,
risk
parity
)
Market
microstructure
&
order
book
analysis
Execution
algorithms
&
transaction
cost
analysis
Systematic
strategy
&
alpha
signal
design
Event
studies
&
causal
impact
measurement
2 / 4
05
Risk
&
Performance
Analytics
Value
at
Risk
(
VaR
) &
Expected
Shortfall
Stress
testing
&
scenario
analysis
Drawdown
&
tail
-
risk
analysis
Performance
attribution
&
risk
-
adjusted
return
metrics
Credit
&
counterparty
risk
modeling
Liquidity
risk
analysis
Regulatory
capital
frameworks
(
Basel
III
,
FRTB
,
IFRS
9)
06
Research
,
Backtesting
&
Validation
Backtest
design
&
control
of
look
-
ahead
bias
Walk
-
forward
&
out
-
of
-
sample
testing
Signal
decay
,
turnover
&
capacity
analysis
Data
quality
validation
&
survivorship
bias
checks
Reproducible
research
workflows
&
experiment
tracking
Academic
literature
review
&
paper
replication
07
Data
Visualization
&
Reporting
Python
visualization
(
Matplotlib
,
Seaborn
,
Plotly
)
R
visualization
(
ggplot
2,
lattice
)
Business
intelligence
dashboards
(
Power
BI
,
Tableau
,
Looker
)
Statistical
chart
selection
&
encoding
accuracy
Report
authoring
(
LaTeX
,
Quarto
,
R
Markdown
)
Advanced
spreadsheet
modeling
(
Excel
,
VBA
)
08
Analytical
Tools
&
Platforms
Bloomberg
Terminal
LSEG
/
Refinitiv
Eikon
&
Datastream
WRDS
,
Compustat
&
CRSP
databases
Kdb
+/
q
for
high
-
frequency
data
Cloud
data
warehouses
(
Snowflake
,
BigQuery
,
Redshift
)
Statistical
packages
(
SAS
,
Stata
,
EViews
,
SPSS
)
Jupyter
&
notebook
-
based
analysis
environments
Interactive
computing
&
pipeline
notebooks
(
Databricks
)
Source
control
&
CI
for
analytical
code
(
GitHub
Actions
)
3 / 4
09
Cross
-
Reference
:
Clusters
,
Skills
&
Tools
SKILL
CLUST E R
→
RE PRE SE NTAT IVE
SKILLS
→
CO MMO N
T O O LING
CLUSTER
REPRESENTATIVE
SKILLS
COMMON
TOOLS
Mathematics
&
Statistics
Probability
,
inference
,
regression
,
time
series
,
optimization
,
Monte
Carlo
NumPy
,
SciPy
,
statsmodels
,
R
,
MATLAB
Programming
&
Data
Python
,
SQL
,
pipelines
,
distributed
computing
,
version
control
pandas
,
Spark
,
Airflow
,
dbt
,
Git
,
AWS
Modeling
&
ML
Supervised
and
unsupervised
learning
,
boosting
,
deep
learning
,
Bayesian
methods
scikit
-
learn
,
XGBoost
,
PyTorch
,
PyMC
,
Stan
Financial
Quant
Methods
Asset
pricing
,
derivatives
,
fixed
income
,
volatility
,
portfolio
optimization
QuantLib
,
Bloomberg
,
Python
,
R
Risk
&
Performance
VaR
,
Expected
Shortfall
,
stress
testing
,
attribution
,
regulatory
frameworks
Risk
platforms
,
Python
,
SQL
,
Excel
Research
&
Validation
Backtesting
,
out
-
of
-
sample
testing
,
bias
control
,
replication
Jupyter
,
Git
,
custom
backtest
engines
Visualization
&
Reporting
Statistical
charts
,
dashboards
,
technical
report
authoring
Matplotlib
,
Plotly
,
ggplot
2,
Power
BI
,
Tableau
,
LaTeX
Tools
&
Platforms
Market
data
terminals
,
research
databases
,
warehouses
,
statistical
suites
Bloomberg
,
WRDS
,
CRSP
,
Kdb
+/
q
,
Snowflake
,
SAS
4 / 4
