Python for Data Science & Analytics - Course Curriculum

Hexadigitall Technologies https://hexadigitall.com

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Course Snapshot

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

12 Weeks

Beginner

Project-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 & What You Should Know

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

Recommended 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

Essential Learning 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

Your 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

Week 12 hours + labs

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

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 22 hours + labs

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

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 32 hours + labs

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

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 42 hours + labs

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

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 52 hours + labs

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

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 62 hours + labs

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

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 72 hours + labs

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

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 82 hours + labs

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

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 92 hours + labs

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

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 102 hours + labs

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

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 112 hours + labs

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

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 122 hours + labs

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

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.