Curriculum Roadmap & Learning Path

1.2 — Data Analytics Overview & Roles

1.3 — Data Types & Formatting

1.4 — Cell Referencing

1.5 — Excel Tables Architecture

1.6 — Pivot Table Foundations

1.7 — Visual Analytics, Data Types & Chart Selection

1.8 — Data Quality Dimensions and Audit

1.9 — Power Query Fundamentals & M Language Architecture

1.10 — Dimensional Modeling

1.11 — Mentor Message & Professional Mindset

1.12 — CV Portfolio Project Architecture

2.1 — Excel Interface & GUI

M2

Module 2: Data Management, Validation & Hygiene

Primitive data types, custom number formatting, list validation, sorting algorithms, and file formats.

41 Lessons ▼

2.2 — Data Analytics Overview & Roles

2.3 — Data Types & Formatting

2.4 — Sorting & Filtering

2.5 — Data Validation & Integrity

2.6 — Data Transformation Tools

2.7 — Shortcuts & Navigation

2.8 — Workbook File Formats & Extensions

2.9 — Cell Referencing

2.10 — Statistical & Aggregation Functions

2.11 — Logical Functions

2.12 — Lookup & Reference Functions

2.13 — Text Manipulation

2.14 — Date & Time Intelligence

2.15 — Dynamic Arrays

2.16 — Data Analysis Life Cycle

2.17 — Excel Tables Architecture

2.18 — Structured References

2.19 — Table Features & Slicers

2.20 — Pivot Table Foundations

2.21 — Show Values As

2.22 — Grouping & Calculated Fields

2.23 — Slicers & Timelines

2.24 — Visual Analytics, Data Types & Chart Selection

2.25 — Executive Formatting, Decluttering & Pre-Attentive Design

2.26 — Executive Dashboard Architecture & Visual Hierarchy

2.27 — Data Quality Dimensions and Audit

2.28 — Data Cleaning Techniques in Excel

2.29 — Importing Data from Enterprise Sources

2.30 — Enterprise Business Systems Architecture (ERP, CRM, HRIS)

2.31 — Power Query Fundamentals & M Language Architecture

2.32 — Core Data Transformations in Power Query

2.33 — Combining Datasets: Append vs Merge

2.34 — M Formula Language Architecture & API Ingestion

2.35 — Dimensional Modeling

2.36 — Star Schema Relationships & VertiPaq Engine

2.37 — Calculated Columns vs Measures & Dual Engine Architecture

2.38 — Essential DAX Functions & Context Transition

2.39 — VertiPaq Engine Architecture & DAX Optimization

2.40 — Mentor Message & Professional Mindset

2.41 — CV Portfolio Project Architecture

3.1 — Data Validation & Integrity

M3

Module 3: Formulas & Functions Engine

Cell referencing (Relative vs Absolute), math/stats functions, logical IFS, modern XLOOKUP, dates, and dynamic arrays.

15 Lessons ▼

3.2 — Cell Referencing

3.3 — Statistical & Aggregation Functions

3.4 — Logical Functions

3.5 — Lookup & Reference Functions

3.6 — Text Manipulation

3.7 — Date & Time Intelligence

3.8 — Dynamic Arrays

3.9 — Data Analysis Life Cycle

3.10 — Table Features & Slicers

3.11 — Grouping & Calculated Fields

3.12 — Executive Dashboard Architecture & Visual Hierarchy

3.13 — Importing Data from Enterprise Sources

3.14 — Combining Datasets: Append vs Merge

3.15 — Calculated Columns vs Measures & Dual Engine Architecture

4.1 — Data Transformation Tools

M4

Module 4: Excel Tables Architecture

ListObject engine, structured column references, calculated fields, and robust table design.

9 Lessons ▼

4.2 — Lookup & Reference Functions

4.3 — Excel Tables Architecture

4.4 — Structured References

4.5 — Table Features & Slicers

4.6 — Slicers & Timelines

4.7 — Enterprise Business Systems Architecture (ERP, CRM, HRIS)

4.8 — M Formula Language Architecture & API Ingestion

4.9 — Essential DAX Functions & Context Transition

5.1 — Shortcuts & Navigation

M5

Module 5: Pivot Tables & Multi-Dimensional Summaries

Aggregation mechanics, Show Values As percentage calculations, grouping, slicers, and interactive timelines.

7 Lessons ▼

5.2 — Text Manipulation

5.3 — Pivot Table Foundations

5.4 — Show Values As

5.5 — Grouping & Calculated Fields

5.6 — Slicers & Timelines

5.7 — VertiPaq Engine Architecture & DAX Optimization

6.1 — Workbook File Formats & Extensions

M6

Module 6: Data Visualization & Executive Dashboards

Visual analytics, chart selection rules, cognitive load reduction, and KPI dashboard visual hierarchy.

5 Lessons ▼

6.2 — Date & Time Intelligence

6.3 — Visual Analytics, Data Types & Chart Selection

6.4 — Executive Formatting, Decluttering & Pre-Attentive Design

6.5 — Executive Dashboard Architecture & Visual Hierarchy

M7

Module 7: Data Cleaning & Enterprise Governance

The DAMA 6 Dimensions of Data Quality, formula cleaning techniques, and enterprise ERP/CRM data ingestion.

5 Lessons ▼

7.1 — Dynamic Arrays

7.2 — Data Quality Dimensions and Audit

7.3 — Data Cleaning Techniques in Excel

7.4 — Importing Data from Enterprise Sources

7.5 — Enterprise Business Systems Architecture (ERP, CRM, HRIS)

8.1 — Data Analysis Life Cycle

M8

Module 8: Power Query ETL & M Automation

Extract, Transform, Load (ETL) pipeline, column unpivoting, fuzzy merging, append queries, and introduction to M.

5 Lessons ▼

8.2 — Power Query Fundamentals & M Language Architecture

8.3 — Core Data Transformations in Power Query

8.4 — Combining Datasets: Append vs Merge

8.5 — M Formula Language Architecture & API Ingestion

9.1 — Dimensional Modeling

M9

Module 9: Data Modeling, Star Schema & DAX Measures

Dimensional modeling, relationship cardinality (1-to-many), CALCULATE context transition, and business intelligence.

5 Lessons ▼

9.2 — Star Schema Relationships & VertiPaq Engine

9.3 — Calculated Columns vs Measures & Dual Engine Architecture

9.4 — Essential DAX Functions & Context Transition

9.5 — VertiPaq Engine Architecture & DAX Optimization