Jessica Marie Hernandez

This Express Entry Analysis is a comprehensive Microsoft Fabric Power BI case study examining Canadian Express Entry immigration program data. The project uses Python scripts to extract data from IRCC (Immigration, Refugees and Citizenship Canada) websites, including Express Entry rounds of invitations and 2026-2028 immigration plans. Built using Microsoft Fabric and Power BI, this multi-page interactive dashboard provides insights into program demographics, invitation trends, CRS score analysis, and immigration pathways to help international students and temporary workers understand their immigration options.

Dashboard Pages

1. 2026-2028 Immigration Plans for Permanent Residents

Analysis of immigration targets by category (Economic, Family, Refugees and Protected Persons) with breakdowns by year. Highlights top targets and recommendations for Federal High Skilled and French-speaking pathways.

2. Express Entry Invitations to Apply

Competition analysis using CRS-to-Invitation ratios across round types (Agriculture, CEC, Education, Federal Skilled Trades, French Language, Healthcare, PNP, STEM, Trade Occupations). Compares selected year vs previous year performance.

3. Express Entry Invitations Trend

Monthly trend analysis across Express Entry round types using three key metrics: CRS-to-Invitation Ratio, Total Invitations, and Lowest CRS Score. Includes average line for comparison.

4. Express Entry Invitation Draw Details

Detailed breakdown of individual invitation rounds with respective measures and filtering capabilities.

Data Extraction & Processing

Script 1 (script1.py)

Extracts Express Entry rounds of invitations data from IRCC JSON API endpoint:

Fetches data from: https://www.canada.ca/content/dam/ircc/documents/json/ee_rounds_123_en.json
Converts JSON response to pandas DataFrame for Power BI import
Contains historical invitation round data with CRS scores and invitation counts

Script 2 (script2.py)

Web scrapes 2026-2028 Immigration Plans table from IRCC website:

Scrapes from: IRCC Supplementary Immigration Levels 2026-2028 page
Uses BeautifulSoup to parse HTML and extract target table by ID
Converts table to pandas DataFrame containing immigration targets by category and year

Key Features & Capabilities

Automated Data Collection

Python scripts for automated extraction from IRCC websites and APIs

Multi-Page Analysis

Comprehensive dashboard with 4 pages covering different aspects of Express Entry

CRS Score Analysis

CRS-to-Invitation ratio calculations for competitive pathway analysis

Trend Visualization

Monthly trend analysis with year-over-year comparisons

Interactive Filters

Dynamic year slicers and filters for focused analysis

Microsoft Fabric Integration

Modern data analytics platform with Power BI integration

Technical Implementation

Data Extraction: Python scripts using requests, pandas, BeautifulSoup for web scraping and API calls
Platform: Microsoft Fabric with Power BI
Data Sources: IRCC JSON API and HTML tables from IRCC website
Data Processing: Exploratory data analysis with Jupyter notebooks
Visualization: Interactive Power BI dashboard with dynamic filters, drill-through, and multi-page navigation
Tools: Python, Power BI, DAX, Microsoft Fabric, Jupyter Notebooks, BeautifulSoup, Pandas

Key Insights & Recommendations

Immigration Plans Insights

Top 3 targets: Economic, Family, and Refugees & Protected Persons categories
Focus on Federal High Skilled (Economic category) for highest targets and simpler processing
French-speaking pathways offer significant opportunities (~30k/year targets)

Express Entry Competition Analysis

French Language Proficiency has lowest CRS-to-Invitation ratio (1.05%) - most favorable path
Canadian Experience Class (2.17%) and Healthcare are competitive options
Trade Occupations (40.40%) and Education (13.20%) are most competitive
Learning French increases CRS scores for both CEC and French Language routes

Express Entry Analysis

Interactive Dashboard

About This Analysis