Data

Mining

—

Hard

Skills

Programming

Scripting

SKILLS

Python

for

Data

Analysis

pandas

NumPy

scikit

learn

pipelines

virtual

environments

and

packaging

Advanced

SQL

Window

functions

CTEs

recursive

queries

execution

plan

tuning

and

indexing

strategy

tidyverse

data

table

and

caret

for

statistical

modelling

and

exploratory

analysis

Scala

Java

Writing

and

debugging

native

Spark

jobs

for

high

throughput

production

workloads

Shell

Pipeline

Automation

Bash

scripting

cron

scheduling

and

CLI

tooling

for

repeatable

data

runs

Git

Version

Control

Branching

models

pull

request

review

code

review

standards

and

hooks

Mathematics

Statistics

SKILLS

Probability

Theory

Distributions

conditional

probability

Bayes

theorem

and

likelihood

estimation

Inferential

Statistics

Hypothesis

testing

confidence

intervals

ANOVA

chi

square

and

power

analysis

Regression

Analysis

OLS

logistic

and

Poisson

models

;

ridge

lasso

and

elastic

net

regularisation

Linear

Algebra

for

Data

Matrix

decomposition

eigenvectors

and

their

role

PCA

and

SVD

Time

Series

Statistics

Stationarity

testing

autocorrelation

ARIMA

seasonal

decomposition

Experimental

Design

Sampling

methods

test

design

controlling

for

bias

and

confounding

Core

Data

Mining

Algorithms

SKILLS

Classification

Random

forest

gradient

boosting

(

XGBoost

LightGBM

SVM

and

naive

Bayes

Clustering

means

DBSCAN

hierarchical

clustering

and

Gaussian

mixture

models

Association

Rule

Mining

Apriori

Growth

and

Eclat

;

support

confidence

and

lift

interpretation

Anomaly

Detection

Isolation

Forest

local

outlier

factor

and

one

class

SVM

for

fraud

and

fault

signals

Pattern

Sequence

Mining

PrefixSpan

sequential

pattern

discovery

and

frequent

itemset

workflows

Recommender

Systems

Collaborative

filtering

matrix

factorisation

and

content

based

ranking

Dimensionality

Reduction

PCA

SVD

SNE

and

UMAP

for

feature

compression

and

visual

exploration

1 / 4

Data

Preparation

Wrangling

SKILLS

Data

Cleaning

Missing

value

imputation

deduplication

type

coercion

and

outlier

treatment

Feature

Engineering

Binning

encoding

interaction

terms

lag

features

and

domain

driven

derived

variables

Feature

Selection

Boruta

recursive

feature

elimination

and

variance

inflation

checks

ETL

ELT

Pipeline

Design

Idempotent

transforms

incremental

loads

schema

contracts

and

backfill

strategy

Imbalanced

Data

Handling

SMOTE

class

weighting

stratified

sampling

and

threshold

tuning

Big

Data

Distributed

Computing

SKILLS

Apache

Spark

PySpark

DataFrames

RDDs

Spark

SQL

partitioning

and

shuffle

optimisation

Hadoop

Ecosystem

HDFS

MapReduce

Hive

and

Pig

for

batch

processing

petabyte

scale

Streaming

Ingestion

Apache

Kafka

Spark

Structured

Streaming

and

event

time

windowing

Parallel

Python

Dask

and

Ray

for

out

core

computation

and

distributed

model

training

Distributed

Query

Engines

Trino

Presto

and

Apache

Drill

for

federated

analysis

across

heterogeneous

sources

Databases

Storage

Systems

SKILLS

Relational

Databases

PostgreSQL

MySQL

and

SQL

Server

—

indexing

partitioning

and

query

tuning

NoSQL

Stores

MongoDB

document

modelling

Cassandra

wide

column

design

Redis

caching

Neo

graphs

Cloud

Data

Warehouses

Snowflake

Google

BigQuery

and

Amazon

Redshift

—

clustering

cost

control

dbt

models

Columnar

File

Formats

Parquet

and

ORC

compression

predicate

pushdown

and

row

group

tuning

Lakehouse

Architectures

Delta

Lake

and

Apache

Iceberg

tables

ACID

guarantees

and

time

travel

2 / 4

Tools

Libraries

Frameworks

SKILLS

Modelling

Libraries

scikit

learn

XGBoost

LightGBM

and

CatBoost

reproducible

training

pipelines

Deep

Learning

Frameworks

TensorFlow

Keras

and

PyTorch

for

neural

architectures

structured

and

unstructured

data

Experiment

Tracking

MLflow

and

Weights

Biases

for

run

comparison

artefact

storage

and

model

registry

Workflow

Orchestration

Apache

Airflow

and

Dagster

DAGs

for

dependency

aware

retry

safe

scheduling

Notebook

Compute

Environments

Jupyter

Databricks

and

managed

notebook

workspaces

with

cluster

configuration

Text

Mining

Natural

Language

Processing

SKILLS

Text

Preprocessing

Tokenisation

stop

word

removal

stemming

lemmatisation

and

gram

construction

Vectorisation

IDF

and

bag

words

plus

Word

Vec

GloVe

and

contextual

embeddings

Topic

Modelling

Latent

Dirichlet

Allocation

and

NMF

for

unsupervised

theme

discovery

Information

Extraction

Named

entity

recognition

sentiment

analysis

keyphrase

and

relation

extraction

Transformer

Models

BERT

family

fine

tuning

and

spaCy

pipelines

for

classification

and

tagging

tasks

Model

Evaluation

Validation

SKILLS

Validation

Strategies

fold

stratified

and

grouped

cross

validation

;

walk

forward

splits

for

time

series

Performance

Metrics

Precision

recall

ROC

AUC

RMSE

and

MAPE

selection

problem

type

Error

Analysis

Confusion

matrix

breakdowns

calibration

curves

and

segment

level

failure

review

Statistical

Lift

Testing

Significance

testing

model

gains

and

guardrail

metric

monitoring

Visualization

Reporting

SKILLS

Programmatic

Plotting

matplotlib

seaborn

Plotly

and

ggplot

for

reproducible

analytical

charts

Dashboards

Tableau

and

Power

—

data

modelling

calculated

fields

drill

down

and

refresh

schedules

Data

Storytelling

Translating

model

output

into

decision

ready

narratives

for

non

technical

stakeholders

Automated

Reporting

Parameterised

report

generation

scheduled

exports

and

self

serve

metric

definitions

3 / 4

Cloud

MLOps

Deployment

SKILLS

Cloud

Data

Platforms

AWS

(

SageMaker

Glue

Athena

GCP

(

BigQuery

Vertex

Azure

(

Synapse

Containerization

Docker

images

and

Kubernetes

orchestration

for

portable

training

and

serving

for

Models

Automated

test

suites

model

versioning

staged

rollouts

and

rollback

plans

Model

Serving

REST

endpoints

with

FastAPI

Flask

batch

scoring

jobs

and

latency

budgets

Monitoring

Drift

Detection

Data

drift

concept

drift

feature

quality

alerts

and

retraining

triggers

Governance

Ethics

Compliance

SKILLS

Privacy

Regulation

GDPR

and

CCPA

handling

lawful

basis

retention

limits

and

data

subject

requests

Anonymisation

Techniques

PII

detection

tokenisation

anonymity

diversity

and

differential

privacy

Fairness

Bias

Auditing

Protected

attribute

testing

disparate

impact

measurement

and

mitigation

methods

Documentation

Lineage

Data

dictionaries

model

cards

provenance

tracking

and

audit

ready

records

4 / 4

Data Mining Hard Skills

Prompt to Clone This Data Mining Hard Skills

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

What are you generating today?

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