ITLearning

Data Science Course Curriculum

Data Science Training (Junior & Intermediate) — 6–9 Months

Data Science Training — Junior (6 Months) & Intermediate (9 Months)

Hands-on, project-based curriculum that maps directly to common LinkedIn/Indeed job requirements for roles listed as “1–3 years” experience.

Global Job Market Snapshot (as of 6 Sep 2025)

US ~11.6k
Active Data Scientist postings
AU ~1.7k
Active postings (Australia)
NZ ~340+
Active postings (New Zealand)
UK ~2.6–2.7k
Active postings (UK)

Program 1: Junior Data Scientist — 6 Months (0–1 yr roles)

Structure

25–30 hrs/week
24 weeks • 4–5 case studies
Months 1–2: Foundations, Analytics & SQL

Course 1 — Data Foundations & Business Analytics (3 wks)

Spreadsheets → KPIsEDABusiness metricsStorytelling
Project: Executive KPI workbook for a subscription business (retention, LTV, CAC, churn breakdowns).
Interview-ready: “Built a retention dashboard used by leadership to spot a 12% churn driver.”

Course 2 — SQL for Analytics (2 wks)

JoinsCTEsWindow functionsQuery tuning
Project: Supply-chain bottleneck analysis from normalized warehouse schema; SLA breach root-cause report.

Course 3 — Python for Data (3 wks)

pandasnumpymatplotlibcleaning
Project: Data quality pipeline that fixes schema drift & missingness; publishes daily CSV/Parquet to S3/Azure Blob.
Months 3–4: Supervised ML + Experimentation

Course 4 — ML Fundamentals (4 wks)

RegressionClassificationCross-valMetrics (AUC, F1)
Project: Lead-score model (logistic regression & tree-based). Bias checks, calibration curve, cost-sensitive thresholding.

Course 5 — Experimentation & A/B Testing (2 wks)

Power calcLift CUPED Sequential tests
Project: Homepage variant test with pre-post analysis; write a PRD-style results memo for product managers.
Months 5–6: NLP/Time-series + Production & BI

Course 6 — NLP or Time-Series (choose 1) (2 wks)

NLP: TF-IDF → embeddingsTS: ARIMA/Prophet
NLP Project: Review-sentiment insights with topic labeling → CX backlog.
Time-Series Project: Weekly sales forecast with holidays & promotions; backtesting & MAPE.

Course 7 — “From Notebook to Stakeholders” (2 wks)

Dashboards (Power BI/Tableau)Data contractsVersion control
Capstone (Junior): End-to-end BI + ML mini-stack: SQL source → Python model → dashboard; hiring-manager deck & demo video.
Portfolio: 4–5 case studies with repos, READMEs, and 2–3 short Loom videos.

Program 2: Intermediate Data Scientist — 9 Months (1–3 yr roles)

Structure

30–35 hrs/week
36 weeks • 6–8 case studies + 2 client projects
Months 1–3: Accelerated Core + Feature Stores

Course A — Advanced pandas/SQL + Data Contracts (4 wks)

Dimensional modelingdbtGreat Expectations
Project: Build a star schema & data tests; publish certified tables for analytics & ML.

Course B — Feature Engineering & Feature Stores (4 wks)

Categoricals/Target enc.Leakage controlFeast/Tecton (concepts)
Project: Real-time vs batch features for a fraud model; offline/online consistency checks.

Course C — Model Selection & Interpretability (4 wks)

XGBoost/LightGBMSHAPFairness & drift
Project: Credit-risk playground with monotonic constraints; fairness audit & policy memo.
Months 4–6: MLOps & Generative AI

Course D — MLOps & CI/CD for ML (6 wks)

MLflowDockerFastAPIUnit/integ testsMonitoring
Project: Train/registry/serve pipeline with canary rollout; model/card + alerting on drift.

Course E — Practical GenAI for DS (3 wks)

EmbeddingsRAGEvaluation
Project: Retrieval-augmented analytics assistant over your warehouse; eval via task success + latency budget.
Months 7–9: Domain Projects + Client Work

Course F — Domain Tracks (choose 1–2) (6 wks)

FinTechHealth/MedTechRetail/e-commerceB2B/SaaS
Project (examples): Pricing elasticity model; claims anomaly detection; LTV uplift modeling with treatment optimization.

Course G — Client Projects & Interview Pack (6 wks)

Stakeholder mgmtScopingReadouts
Capstone (Intermediate): Deliver 2 mentored client-style projects (NDA-safe datasets), publish write-ups, and present live demo.

Portfolio & Capstones (Interview-Ready)

Junior Portfolio (6 Projects)

  • Subscription KPI dashboard (Excel/Power BI)
  • SQL supply-chain SLA analysis
  • Python data quality pipeline
  • Lead-score model + business thresholding
  • NLP or Time-series forecasting project
  • End-to-end Mini-stack (SQL→ML→Dashboard)

Intermediate Portfolio (8–12 Projects)

  • Advanced feature store + FE notebook
  • Credit-risk with SHAP + fairness audit
  • MLOps pipeline (train→serve→monitor)
  • GenAI analytics assistant (RAG)
  • Domain project(s): FinTech / Health / Retail / B2B
  • Two client-style deliveries (docs + demos)
Each project includes: repo, README, data dictionary, metrics, stakeholder deck, and a 3–5 min demo video.

Skills ↔ Common Job Requirements (LinkedIn/Indeed/SEEK)

Must-Haves (Junior)

Python (pandas/numpy)
SQL (joins, windows)
EDA & data cleaning
Dashboards (Power BI/Tableau)
A/B testing basics
Storytelling & docs

Must-Haves (Intermediate)

Modeling (tree-based, regularized)
Feature engineering
MLOps (MLflow, Docker, CI/CD)
Monitoring & drift
Cloud data (S3/ADLS, SQL engines)
Experiment design

Nice-to-Haves

GenAI (RAG, eval)
dbt / data testing
Time-series & NLP
Feature store concepts
GxP/PII compliance awareness

These align with wording commonly seen in current “Data Scientist (1–3 yrs)” listings across Glassdoor/Indeed/SEEK snapshots cited above.

Outcomes, Certifications & Support

Outcomes

  • 4–5 (Junior) or 8–12 (Intermediate) case studies
  • GitHub portfolio + demo videos
  • Mock interviews & take-home assignment practice

Certifications (recommended)

  • Microsoft Certified: Azure Data Scientist Associate (or) AWS Machine Learning – Specialty
  • Databricks Data Engineer/ML Associate (where relevant)
  • Power BI Data Analyst Associate

Career Services

  • Resume & LinkedIn optimization (keyword mapping)
  • Portfolio review with hiring-manager rubric
  • Negotiation & offer review workshops

Ready to build an interview-proof portfolio?

Start Junior Program Start Intermediate Program

FAQ

Can someone with no IT background reach “1–3 years” job readiness in 6–9 months?

Yes — if you focus on the exact deliverables hiring teams assess: reproducible code, clear metrics, and business impact. The curriculum above mirrors those artifacts.

Which tech stack should I set up for the projects?

Python 3.11+, Jupyter/VS Code, GitHub, a SQL engine (Postgres/BigQuery/Snowflake), and a dashboard tool (Power BI or Tableau). For Intermediate, add MLflow + Docker.

How is each project packaged for interviews?

Each includes: a README with problem framing, data dictionary, modeling notebook(s), evaluation metrics, a short slide deck, and a 3–5 minute demo video link.