Predictive

Analytics

—

Hard

Skills

S KIL L

DO M AIN S

Statistical

Foundations

Modeling

Machine

Learning

Predictive

Modeling

Domain

Specific

Predictive

Applications

Programming

Query

Languages

Data

Engineering

Pipelines

Deployment

MLOps

Governance

Visualization

Reporting

Communication

Experimentation

Measurement

Statistical

Foundations

Modeling

SKILLS

Regression

Analysis

OLS

logistic

Poisson

quantile

and

regularized

variants

(

Ridge

Lasso

Elastic

Net

)

with

correct

link

functions

and

diagnostics

Time

Series

Modeling

ARIMA

SARIMA

Holt

Winters

exponential

smoothing

state

space

models

and

Prophet

for

seasonal

and

trended

series

Bayesian

Inference

Hierarchical

models

prior

specification

posterior

sampling

via

MCMC

and

variational

inference

PyMC

Stan

Multivariate

Methods

Principal

component

analysis

factor

analysis

discriminant

analysis

and

dimensionality

reduction

for

high

dimensional

feature

sets

Experimental

Design

Randomized

controlled

trials

power

and

sample

size

calculation

sequential

testing

and

variance

reduction

(

CUPED

Hypothesis

Testing

Parametric

and

non

parametric

tests

confidence

intervals

bootstrap

resampling

and

multiple

comparison

correction

Survival

Analysis

Kaplan

Meier

estimation

Cox

proportional

hazards

time

varying

covariates

and

competing

risks

models

Causal

Inference

Difference

differences

propensity

score

matching

instrumental

variables

regression

discontinuity

and

causal

DAGs

1 / 5

Predictive Analytics Hard Skills — page 2

Machine

Learning

Predictive

Modeling

SKILLS

Gradient

Boosting

XGBoost

LightGBM

and

CatBoost

tuned

for

tabular

prediction

problems

including

categorical

handling

and

early

stopping

Ensemble

Methods

Bagging

random

forests

stacking

and

blending

combine

base

learners

and

stabilize

predictions

Linear

Kernel

Models

Regularized

regression

GLMs

and

support

vector

machines

with

appropriate

kernels

and

feature

scaling

Tree

Based

Methods

Decision

trees

isolation

forests

and

tree

based

anomaly

detection

for

interpretable

baseline

models

Neural

Networks

Multilayer

perceptrons

sequence

models

(

LSTM

GRU

)

and

transformer

architectures

for

sequential

and

unstructured

inputs

Feature

Engineering

Lag

and

rolling

window

features

target

and

weight

evidence

encoding

interaction

terms

and

feature

selection

methods

Model

Evaluation

AUC

ROC

and

curves

lift

and

gain

charts

RMSE

MAE

MAPE

calibration

curves

and

business

metric

validation

Hyperparameter

Optimization

Bayesian

with

Optuna

Hyperopt

nested

cross

validation

and

leakage

safe

tuning

workflows

Unsupervised

Learning

means

DBSCAN

hierarchical

clustering

and

autoencoders

for

segmentation

and

dimensionality

reduction

2 / 5

Predictive Analytics Hard Skills — page 3

Domain

Specific

Predictive

Applications

SKILLS

Churn

Retention

Modeling

Survival

based

churn

scoring

hazard

curves

and

targeting

logic

for

save

offers

and

retention

campaigns

Customer

Lifetime

Value

Probabilistic

CLV

models

(

NBD

Gamma

cohort

forecasting

and

value

tier

segmentation

Credit

Risk

Scoring

LGD

EAD

modeling

scorecard

development

WOE

binning

and

regulatory

validation

practices

Fraud

Anomaly

Detection

Supervised

classifiers

rule

plus

hybrids

imbalanced

learning

techniques

and

real

time

scoring

pipelines

Demand

Forecasting

Hierarchical

forecasting

intermittent

demand

methods

(

Croston

forecast

reconciliation

and

safety

stock

inputs

Marketing

Mix

Attribution

Marketing

mix

modeling

multi

touch

attribution

media

response

curves

and

incrementality

validation

Pricing

Elasticity

Price

response

curves

promotion

lift

estimation

and

markdown

optimization

modeling

Uplift

Modeling

Treatment

effect

estimation

with

learner

and

causal

forest

approaches

for

incremental

response

Programming

Query

Languages

SKILLS

Python

pandas

NumPy

scikit

learn

statsmodels

and

PyMC

reproducible

production

oriented

analytics

workflows

tidyverse

tidymodels

caret

forecast

and

survival

packages

for

statistical

modeling

and

reporting

SQL

Window

functions

CTEs

joins

and

query

tuning

against

large

analytical

tables

and

warehouse

schemas

PySpark

Scala

Distributed

feature

engineering

and

model

training

over

large

scale

datasets

Spark

environments

Notebook

Engineering

Jupyter

and

parameterized

notebooks

(

papermill

)

for

reproducible

analysis

and

scheduled

scoring

runs

Git

Version

Control

Branching

strategies

code

review

dependency

pinning

and

experiment

versioning

API

Development

Scripting

FastAPI

Flask

scoring

endpoints

REST

integration

and

scheduled

batch

job

scripting

3 / 5

Predictive Analytics Hard Skills — page 4

Data

Engineering

Pipelines

SKILLS

ETL

ELT

Development

Batch

and

incremental

load

pipelines

dbt

models

and

dimensional

data

design

for

analytical

consumption

Workflow

Orchestration

Airflow

Prefect

Dagster

DAG

authoring

dependency

management

retries

and

scheduling

Data

Quality

Validation

Schema

contracts

expectation

testing

(

Great

Expectations

)

and

drift

aware

input

monitoring

Missing

Data

Handling

MICE

KNN

and

model

based

imputation

plus

missingness

indicators

and

deletion

bias

assessment

Outlier

Noise

Treatment

Winsorization

robust

scaling

anomaly

flagging

and

segment

level

data

cleaning

rules

Feature

Stores

Offline

online

feature

parity

point

time

correctness

and

reusable

feature

definitions

Big

Data

Tooling

Spark

Databricks

and

Snowflake

BigQuery

warehouse

patterns

for

large

volume

modeling

data

Deployment

MLOps

Governance

SKILLS

Model

Deployment

Containerized

scoring

services

(

Docker

Kubernetes

)

supporting

both

batch

and

low

latency

real

time

inference

Cloud

Platforms

AWS

SageMaker

Azure

Machine

Learning

and

Google

Vertex

for

training

hosting

and

pipeline

automation

Model

Monitoring

Performance

decay

tracking

data

and

concept

drift

detection

using

Evidently

WhyLabs

custom

dashboards

Experiment

Tracking

Registry

MLflow

Weights

Biases

for

run

tracking

model

lineage

and

promotion

through

environments

for

Machine

Learning

Automated

testing

and

retraining

canary

releases

and

shadow

deployment

new

model

versions

Model

Explainability

SHAP

LIME

permutation

importance

and

partial

dependence

for

interpretable

and

reviewable

predictions

Fairness

Bias

Testing

Disparate

impact

analysis

group

wise

error

metrics

and

mitigation

strategies

for

protected

segments

Model

Risk

Documentation

Model

cards

validation

reports

assumption

logs

and

audit

ready

governance

documentation

4 / 5

Predictive Analytics Hard Skills — page 5

Visualization

Reporting

Communication

SKILLS

Platform

Development

Tableau

Power

and

Looker

dashboard

builds

with

governed

reusable

data

sources

Python

Visualization

Matplotlib

Seaborn

Plotly

and

Altair

for

analytical

charts

diagnostics

and

interactive

exploration

Dashboard

Design

Metric

hierarchy

drill

down

paths

and

self

serve

analytical

views

that

support

operational

decisions

Executive

Storytelling

Translating

model

output

and

uncertainty

into

concise

decision

ready

narrative

for

non

technical

audiences

Automated

Reporting

Scheduled

reports

templated

deliverables

and

threshold

based

alerting

for

recurring

stakeholders

Stakeholder

Elicitation

Converting

business

questions

into

well

defined

modeling

problems

with

measurable

success

criteria

Experimentation

Measurement

SKILLS

Multivariate

Testing

Test

design

guardrail

metric

selection

and

sequential

group

sequential

analysis

results

Causal

Impact

Measurement

Geo

tests

switchback

designs

holdout

construction

and

long

term

effect

estimation

Incrementality

Testing

Uplift

and

incrementality

validation

campaigns

media

channels

and

intervention

programs

Forecasting

for

Planning

Scenario

modeling

driver

based

forecasts

and

rolling

planning

cycles

tied

operational

targets

KPI

Definition

Tracking

Metric

trees

north

star

and

counter

metrics

and

definition

governance

across

teams

5 / 5

Predictive Analytics Hard Skills

Prompt to Clone This Predictive Analytics Hard Skills

Apply this example's core structure, strategic pacing, and structural hierarchy directly to your own topic.

What are you generating today?

Tax Preparation Hard Skills

Payroll Processing Hard Skills

Auditing Hard Skills

Accounts Payable Hard Skills

Accounts Receivable Hard Skills

Cost Accounting Hard Skills

Investment Analysis Hard Skills

Search Engine Optimization Hard Skills

Search Engine Marketing Hard Skills

Google Analytics Hard Skills

Email Marketing Hard Skills

Content Marketing Hard Skills

Marketing Automation Hard Skills

Market Research Hard Skills

Sales Forecasting Hard Skills

Lead Generation Hard Skills

Customer Relationship Management Hard Skills

Similar Examples