Data Engineering & AI Analytics
Build production data pipelines from SQL and NoSQL databases to visualization platforms. Master Power BI with AI tools, Python, cloud ETL, and modern data engineering.
- 3 Months
- Live Online
- Certificate
- Expert Support
Technologies You'll Master
Excel & Power Query
Advanced formulas, Pivot Tables, Power Query ETL transformations, and Power Pivot data models.
SQL & NoSQL
Relational databases (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB) for diverse data sources.
Python & Pandas
Data manipulation, pipeline scripting, API integrations, and automated data workflows.
Power BI & DAX
Interactive dashboards, DAX formulas, data modeling, and AI-assisted report generation.
Data Pipelines
Build ETL/ELT pipelines from source databases to data warehouses and visualization layers.
AWS Analytics
S3 data lakes, AWS Glue ETL, Athena serverless querying, and Redshift data warehousing.
AI Tools
AI tools that generate dashboards, SQL, and pipeline code from prompts.
Git & Deployment
Version control for analytics projects, CI/CD for pipelines, and portfolio building.
Curriculum
3-Month Course Curriculum
From raw data to production dashboards. Trainees commit 10+ hours of self-study weekly.
Phase 1: Data Foundations & Extraction
- Advanced Formulas: XLOOKUP, Dynamic Arrays (FILTER, UNIQUE, SORT), Nested IFs
- Data Cleaning: Handling duplicates, text-to-columns, conditional formatting for quality checks
- Pivot Tables: Slicers, Timelines, Calculated Fields for rapid exploration
- Power Query (ETL): Importing from web/files/databases, unpivoting, merging queries, parameterized transforms
- Power Pivot: Data Model relationships, basic DAX measures, and calculated columns
Project
Sales data cleaning and analysis pipeline using Power Query
- Foundations: SELECT, JOINs (Inner, Left, Right, Full Outer, Cross), Set Operations (UNION, INTERSECT)
- Advanced SQL: Window Functions (RANK, LEAD, LAG, ROW_NUMBER, NTILE), CTEs, recursive queries
- Data Pipeline SQL: CREATE TABLE AS, INSERT INTO...SELECT, MERGE/UPSERT patterns
- Stored Procedures & Views: Encapsulating transformation logic for reusable pipeline stages
- Performance: Indexing strategies, query execution plans, partitioning for large datasets
Project
Multi-table transformation pipeline with CTEs and window functions
- MongoDB Fundamentals: Documents, collections, CRUD operations, aggregation pipeline
- DynamoDB Basics: Key-value and document models, partition keys, query vs. scan patterns
- Data Extraction Patterns: Connecting to SQL and NoSQL sources, change data capture concepts
- API Data Ingestion: Pulling data from REST APIs, pagination handling, rate limiting
- File Formats: Working with CSV, JSON, Parquet, and Avro in data pipelines
Project
Extract and consolidate data from MongoDB + PostgreSQL into a unified dataset
Phase 2: Data Pipelines & Transformation
- Python Essentials: Variables, functions, loops, error handling, virtual environments
- Pandas & NumPy: DataFrames, Series, vectorized operations, groupby, merge, pivot
- Pipeline Scripting: Reading from databases (SQLAlchemy, pymongo), transforming, and writing to targets
- Scheduling & Automation: cron jobs, task schedulers, and idempotent pipeline design
- Data Validation: Schema checks, data quality assertions, and logging pipeline failures
Project
Automated Python ETL pipeline: PostgreSQL to cleaned Parquet files on S3
- ETL vs. ELT: When to transform before or after loading, modern data stack patterns
- Pipeline Architecture: Source layer, staging layer, transformation layer, serving layer
- Incremental Loading: Timestamp-based, CDC-based, and full-refresh strategies
- Data Warehousing Concepts: Fact tables, dimension tables, Star Schema, Slowly Changing Dimensions
- Orchestration: Scheduling pipeline stages, dependency management, retry and alerting patterns
- Testing Pipelines: Unit testing transformations, integration testing data flows, data contracts
Project
End-to-end ELT pipeline from multiple sources into a Star Schema warehouse
- S3 Data Lake: Organizing raw/cleaned/curated layers, partitioning by date, lifecycle policies
- AWS Glue: Crawlers for schema discovery, Glue ETL jobs for serverless transformations
- Amazon Athena: Querying S3 with SQL, optimizing with partitions and columnar formats
- Amazon Redshift: Loading data, distribution styles, sort keys, and query optimization
- IAM & Security: Least-privilege roles for pipeline services, encryption at rest and in transit
Project
Cloud pipeline: S3 raw layer, Glue transformation, Athena queries for analysis
Phase 3: Visualization, AI Analytics & Career
- Data Modeling: Star Schema implementation in Power BI, relationships, cardinality, cross-filter direction
- DAX Mastery: CALCULATE, FILTER, ALL, Time Intelligence (TOTALYTD, SAMEPERIODLASTYEAR, DATEADD)
- Advanced Visuals: Custom visuals, conditional formatting, drillthrough, bookmarks, tooltips
- Power BI Service: Publishing, workspaces, scheduled refresh, Row Level Security (RLS)
- Connecting Pipelines to Power BI: DirectQuery vs. Import mode, dataflows, incremental refresh
- Dashboard Design: Choosing the right chart, color theory, accessibility, executive vs. operational dashboards
Project
Full business intelligence dashboard connected to your data pipeline output
- AI Tool Fundamentals: How AI tools connect to your data tools
- Building Power BI Dashboards with AI: Using AI tools to generate DAX, layouts, and data models from prompts
- AI SQL Generation: AI agents writing optimized queries against your pipeline data
- Automated Report Generation: AI-written narrative summaries from dashboard data
- AI Data Cleaning: Using AI tools to detect anomalies, propose fixes, and validate data quality
- Prompt Engineering for Analytics: Writing effective prompts for data tasks, iterative refinement
Project
AI-generated Power BI dashboard built entirely through prompts
- Capstone Project: End-to-end pipeline from source DBs (SQL + NoSQL) through transformation to Power BI dashboard
- Data Storytelling: Presenting insights to stakeholders, translating metrics into business recommendations
- Professional Branding: LinkedIn optimization, GitHub portfolio with pipeline projects
- Resume Workshop: Tailoring CVs for "Data Engineer," "BI Developer," "Analytics Engineer" roles
- Interview Prep: SQL whiteboarding, system design for data pipelines, behavioral questions
- Job Search Strategy: Networking, portfolio presentations, salary negotiation
Project
Complete data platform: Multi-source ingestion, cloud pipeline, AI-powered dashboard
Capstone
Capstone Project
End-to-end pipeline from source DBs (SQL + NoSQL) through transformation to Power BI dashboard.
FAQs
You can enroll in any course by visiting the course page and clicking the "Enroll Now" button. Follow the registration process and complete the payment to secure your spot.
All our courses are conducted online through live virtual sessions. This allows students from anywhere in the world to participate and learn from our expert instructors. All live sessions are recorded and shared with trainees.
Yes, we offer flexible payment plans for all our courses. Contact our support team at training@jomacsit.com to discuss the available options and find a plan that works for you.
Start Your Data Engineering Journey
Join our program and become a skilled data engineer and AI analytics professional.