About This Program
The Professional Diploma Program in Data Engineering with Data Analytics and AI Fundamentals is a comprehensive 6-month industry-focused curriculum designed to equip learners with essential skills in data engineering, data analytics, AI, and cloud technologies. The program covers core concepts of Azure data fundamentals, advanced data engineering techniques, Databricks and Apache Spark, foundational AI and machine learning principles, and practical data analytics skills. Structured into two phases—6 months of intensive training followed by 4 months of hands-on internship—this course prepares learners to design and implement scalable data pipelines, perform advanced data analytics, work with Lakehouse architectures, integrate AI models, and manage cloud-based data solutions. The course includes preparation for Microsoft DP-900 and Databricks Certified Data Engineer Associate certifications to validate your expertise and boost career prospects.
Eligibility
Bachelor’s degree in Computer Science, IT, Engineering, Mathematics, or a related field with a minimum of 60% marks.
Selection Criteria
Selection is based on academic background, aptitude test performance, and personal interview.
Program Highlights
- Practical, project-based learning across Azure, Databricks, Spark, Data Analytics, and AI fundamentals
- 4-month guaranteed internship with real-world industry exposure
- Certification preparation for Databricks Data Engineer Associate & Microsoft DP-900
- Suitable for freshers and working professionals looking to upskill
- 24/7 access to labs and learning resources
- Guidance from industry-experienced trainers
- Coverage of batch and streaming pipelines, data analytics, governance, automation, AI model deployment, and more
Course Curriculum
Months 1-2: Azure Data Fundamentals, Core Data Engineering & Data Analytics
Hours: 70
- Introduction to Data & Analytics (DP-900)
- Relational Data Concepts (Tables, Keys, Relationships)
- Non-Relational Data Concepts (NoSQL)
- Core Azure Data Services Overview
- Basics of SQL: Queries, Joins, Indexes
- Introduction to Data Warehousing & ETL Concepts
- Data ingestion techniques & pipelines
- Data governance and compliance basics
- Advanced SQL & PL/SQL (Stored Procedures, Views, Triggers)
- NoSQL Databases Deep Dive (Cosmos DB, MongoDB)
- Azure Data Lake Storage & Blob Storage Fundamentals
- Data transformation & cleansing techniques
- Introduction to Azure Synapse Analytics
- Hands-on labs: Data ingestion, transformation, and basic data analytics pipelines
Month 3: Python for Data Engineering and Analytics
Hours: 25
- Python basics & data structures
- Advanced Python programming (OOP, error handling)
- Data manipulation with Pandas, NumPy
- Working with APIs & file formats (JSON, CSV, XML)
- Writing ETL scripts in Python
- Integration of Python with Azure services
- Practical exercises & mini-projects focusing on data analytics
Month 4: Big Data, Databricks & Data Analytics Fundamentals
Hours: 40
- Big Data concepts and ecosystem overview
- Introduction to Apache Spark & PySpark programming
- Spark SQL & DataFrames
- Working with Databricks workspace and UI
- Creating & managing Databricks clusters
- Collaborative coding with Databricks Notebooks
- Running PySpark jobs on Databricks
- Introduction to Delta Lake & data versioning
- Data analytics workflows on Databricks
Month 5: Cloud Data Engineering, Analytics & Databricks Integration
Hours: 30
- Azure Data Factory: pipelines, activities, triggers
- Orchestrating Databricks notebooks within ADF pipelines
- Deep dive into Azure Synapse Analytics
- Working with Azure Cosmos DB & other Azure data services
- Optimizing data workflows using Databricks Delta Lake
- Security, monitoring, and cost optimization on Azure
- Hands-on project: End-to-end data pipeline with integrated analytics
Month 6: AI Fundamentals, Model Deployment & Career Preparation
Hours: 45
- Introduction to AI & Machine Learning fundamentals
- Overview of ML algorithms: classification, regression
- Azure Machine Learning Studio: setup and experiments
- Feature engineering & data preparation for ML models
- Training and deploying models on Azure ML and Databricks
- Model monitoring, retraining, and lifecycle management (MLflow)
- Responsible AI principles: ethics, bias, explainability
- Capstone project: Integrate data pipeline with a simple ML model and analytics
- Resume building, interview preparation, and mock interviews
Post-Course 4-Month Internship
- Real-world projects leveraging Azure, Databricks, Spark, Python, Data Analytics, and AI pipelines
- Continuous mentorship & performance feedback
- Exposure to industry-standard workflows and collaboration tools
- 100% job guaranty at Credenca.
Certifications Covered
- Databricks Certified Data Engineer Associate (Optional)
- Microsoft DP-900 – Azure Data Fundamentals
Hands-On Projects
- Python + SQL Data Pipeline
- Streaming + Batch Pipeline with Delta Lake and Azure Data Factory
- Real-time Capstone Project: End-to-end data engineering, data analytics, and AI integration
Exam Preparation Support
- Access to Databricks Academy resources: Data ingestion, Workload deployment, Delta Live Tables, Unity Catalog
- DP-900 exam readiness materials and practice
Key Skills You Will Master
- Data Engineering on Azure & Databricks
- Apache Spark (PySpark) programming
- Batch and Streaming ETL pipelines
- Delta Lake architecture & data versioning
- Data Governance using Unity Catalog
- Workflow automation & monitoring
- AI/ML fundamentals and model deployment
- Power BI for reporting and data visualization
- Data Analytics with SQL, Python (Pandas), and Azure Synapse
Ideal For
- Freshers and professionals seeking career transition into data engineering, data analytics, and AI roles
What’s Included
- Industry-aligned curriculum with Data Engineering, Data Analytics, and AI components
- 24/7 access to cloud tools including Azure, Databricks, Power BI
- Real-world datasets and hands-on learning
- Capstone project for portfolio showcase
This Course Include
- Industry-aligned curriculum with Data Engineering, Data Analytics, and AI components
- 24/7 access to cloud tools including Azure, Databricks, Power BI
- Real-world datasets and hands-on learning
- Capstone project for portfolio showcase
Enquire Now
Industry Collaboration & Placements
+91-77580 15726
akshata.swami@nextgenedu.in