Python for Data Science & Analytics Curriculum

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Curriculum

Python for Data Science & Analytics

Structured, hands-on learning path for Python for Data Science & Analytics with detailed weekly outcomes and practical delivery.

Duration: 12 Weeks
Level: Beginner
Study Time: 2 hours/week + labs
School: Hexadigitall Academy
12 WeeksBeginnerProject-Based

Welcome to Python for Data Science & Analytics! 🎓

This curriculum for Python for Data Science & Analytics follows a Bloom-aligned progression from practical foundations to measurable professional outcomes, with weekly evidence, labs, and portfolio outputs matched to beginner expectations.

Each week advances from comprehension and application toward evaluation and creation, ensuring progressive learning and capstone readiness.

Your success is our priority. By the end, you will produce portfolio-ready artifacts and confidently explain your technical decisions. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality.

Prerequisites

  • SQL proficiency: complex joins, window functions, query optimization, and performance analysis
  • Hands-on experience with data visualization tools (Tableau, Looker, Power BI, or similar)
  • Understanding of relational and dimensional modeling, fact/dimension tables, and star schema design
  • Familiarity with data quality frameworks: validation rules, anomaly detection, and completeness checks

Essential Resources

  • SQL optimization guides, query pattern library, and performance tuning references
  • Data modeling templates, dimensional design patterns, and quality framework schemas
  • Analytics cookbook with common calculations, transformations, and business metric definitions

Complementary Courses

Advanced Analytics & Machine Learning

Learn predictive modeling, statistical methods, and experimentation frameworks

Data Architecture & Engineering

Master ETL/ELT patterns, data warehouse design, and pipeline orchestration

Business Intelligence & Reporting

Deepen dashboard design, storytelling with data, and executive communication

Learning Roadmap

  • Early Weeks: SQL fundamentals, data modeling, and quality basics
  • Middle Weeks: Complex transformations, performance optimization, and BI tools
  • Late Weeks: Data governance, advanced analytics, and strategic reporting

Detailed Weekly Curriculum

Each week includes outcomes and practical lab work aligned to the curriculum structure.

Week 1

Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Explain Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 2

Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Explain Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 3

Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Explain Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) and enforce fail-fast criteria.
  • Execute a staged promotion for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) and validate rollback safety under a controlled failure.
Week 4

Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1)

2 hours + labs
Learning Outcomes
  • Identify the principles of Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Explain Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 5

Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Analyze Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 6

Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Analyze Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 7

Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Analyze Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 8

Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1)

2 hours + labs
Learning Outcomes
  • Apply the principles of Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) and link them to course outcomes through progressive practical delivery milestones.
  • Analyze Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) queries or transformations.
  • Publish Python for Data Science & Analytics: Data Governance and Documentation (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 9

Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) and link them to course outcomes through progressive practical delivery milestones.
  • Evaluate Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) queries or transformations.
  • Publish Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 10

Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) and link them to course outcomes through progressive practical delivery milestones.
  • Evaluate Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) queries or transformations.
  • Publish Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 11

Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) and link them to course outcomes through progressive practical delivery milestones.
  • Evaluate Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) and enforce fail-fast criteria.
  • Execute a staged promotion for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) and validate rollback safety under a controlled failure.
Week 12

Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) and link them to course outcomes through progressive practical delivery milestones.
  • Evaluate Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready analytics quality workbook for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) queries or transformations.
  • Publish Python for Data Science & Analytics: Exploratory Analysis and Story Framing (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.

Capstone Projects

Project 1: Python for Data Science & Analytics Foundation Build

Deliver a concrete foundation implementation covering the first phase of the curriculum.

  • Implement and validate Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 1).
  • Integrate Python for Data Science & Analytics: SQL and Data Modeling (Sprint 1) with reusable workflow standards.
  • Publish evidence for Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 1) with test and quality artifacts.

Project 2: Python for Data Science & Analytics Integrated Systems Build

Combine mid-program competencies into a production-style integrated workflow.

  • Build an end-to-end flow around Python for Data Science & Analytics: Dashboard Design and KPI Communication (Sprint 1) and Python for Data Science & Analytics: Statistical Testing and Validation (Sprint 1).
  • Add controls, observability, and rollback paths for reliability.
  • Document architecture decisions and trade-offs tied to Python for Data Science & Analytics: Forecasting and Scenario Analysis (Sprint 1).

Project 3: Python for Data Science & Analytics Capstone Delivery

Ship a portfolio-ready capstone with measurable outcomes and stakeholder-ready presentation.

  • Deliver a complete implementation centered on Python for Data Science & Analytics: Data Foundations and Quality Assurance (Sprint 2).
  • Validate readiness for Python for Data Science & Analytics: SQL and Data Modeling (Sprint 2) using objective acceptance checks.
  • Present final defense and roadmap based on Python for Data Science & Analytics: Transformation and Pipeline Hygiene (Sprint 2) outcomes.