Admissions Open for JANUARY Batch
Learn to set up, manage, and optimize cloud data warehouses and ETL workflows for scalable analytics and business insights.
Days : Tue & Thu
Duration : 12 Hours
Timings: 10 AM - 12 PM IST
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12 Hours
8 - 9 PM IST
Tue & Thu
Cloud Basics & Data Warehousing
Learn cloud computing fundamentals, use Python for AWS S3/GCS, set up data warehouses, model data, and build cost-efficient cloud ETL pipelines.
Online Live Instructor-Led Learning
12 Hours
10 AM - 12 PM
Sat & Sun
By end of this course
Get stronger in
Gain a foundational understanding of cloud computing and interact with cloud storage (AWS S3/GCS) using Python.
Learn to set up, connect to, query, and optimize cloud data warehouses (e.g., Redshift, BigQuery).
Understand data modeling basics (fact/dimension tables, star schema) and manage cloud costs effectively.
Get familier with
Become familiar with basic cloud storage operations like uploading/downloading files and organizing data.
Learn about managed cloud databases (AWS RDS, Cloud SQL) and their appropriate use cases.
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners eager to deepen their skills in cloud data platforms and data warehousing as part of their data engineering journey.
Prerequisites
Proficiency in Python and SQL, and a solid grasp of ETL concepts from Modules 1 and 2.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Proficiency in Python and SQL, and a solid grasp of ETL concepts from Modules 1 and 2.
Who Should Enroll?
This course is for learners eager to deepen their skills in cloud data platforms and data warehousing as part of their data engineering journey.
By the end of this course
Get Stronger in
Gain a foundational understanding of cloud computing and interact with cloud storage (AWS S3/GCS) using Python.
Learn to set up, connect to, query, and optimize cloud data warehouses (e.g., Redshift, BigQuery).
- Understand data modeling basics (fact/dimension tables, star schema) and manage cloud costs effectively.
Get Familiar in
Become familiar with basic cloud storage operations like uploading/downloading files and organizing data.
Learn about managed cloud databases (AWS RDS, Cloud SQL) and their appropriate use cases.
Course Contents
Content
Understand what cloud computing is and why companies use it. Overview of AWS, GCP, and Azure. Learn basic cloud concepts: compute, storage, databases. Create a free AWS/GCP account.
Content
Upload and download files to cloud storage (AWS S3 or Google Cloud Storage). Organize data using folders and prefixes. Understand storage classes and cost.
Content
Use boto3 (AWS) or google cloud-storage to interact with cloud storage. Upload CSV files to S3/GCS from Python. Download files and load into Pandas. List and delete objects programmatically.
Content
Understand what a data warehouse is and how it differs from databases. Learn about OLTP vs OLAP systems. Explore use cases for data warehousing in companies.
Content
Set up a cloud data warehouse (Amazon Redshift, Google BigQuery, or Snowflake free trial). Connect to the warehouse from your laptop. Load your first dataset.
Content
Write SQL queries in the cloud warehouse. Understand columnar storage benefits. Learn about query costs and optimization basics. Export query results to files.
Content
Learn about fact and dimension tables. Understand star schema design with simple examples. Design a basic data warehouse schema for a business scenario.
Content
Load data from S3/GCS into your data warehouse. Use COPY commands (Redshift) or load jobs (BigQuery). Implement basic incremental loading using dates.
Content
Build an ETL pipeline using cloud services: Extract from API → Store in S3/GCS → Load to data warehouse → Query and analyze.
Content
Learn about managed databases (AWS RDS, Cloud SQL). Understand when to use databases vs data warehouses. Connect to cloud databases from Python
Content
Understand how cloud billing works. Learn to monitor your usage and costs. Implement best practices to avoid unexpected charges. Use free tier wisely
Content
Build an end -to -end cloud data pipeline: Extract data from multiple sources, store raw data in cloud storage, transform and load into cloud data warehouse, create analysis queries, document the architecture.
Content
Understand what cloud computing is and why companies use it. Overview of AWS, GCP, and Azure. Learn basic cloud concepts: compute, storage, databases. Create a free AWS/GCP account.
Content
Upload and download files to cloud storage (AWS S3 or Google Cloud Storage). Organize data using folders and prefixes. Understand storage classes and cost.
Content
Use boto3 (AWS) or google cloud-storage to interact with cloud storage. Upload CSV files to S3/GCS from Python. Download files and load into Pandas. List and delete objects programmatically.
Content
Understand what a data warehouse is and how it differs from databases. Learn about OLTP vs OLAP systems. Explore use cases for data warehousing in companies.
Content
Set up a cloud data warehouse (Amazon Redshift, Google BigQuery, or Snowflake free trial). Connect to the warehouse from your laptop. Load your first dataset.
Content
Write SQL queries in the cloud warehouse. Understand columnar storage benefits. Learn about query costs and optimization basics. Export query results to files.
Content
Learn about fact and dimension tables. Understand star schema design with simple examples. Design a basic data warehouse schema for a business scenario.
Content
Load data from S3/GCS into your data warehouse. Use COPY commands (Redshift) or load jobs (BigQuery). Implement basic incremental loading using dates.
Content
Content
Build an ETL pipeline using cloud services: Extract from API → Store in S3/GCS → Load to data warehouse → Query and analyze.
Content
Learn about managed databases (AWS RDS, Cloud SQL). Understand when to use databases vs data warehouses. Connect to cloud databases from Python
Content
Understand how cloud billing works. Learn to monitor your usage and costs. Implement best practices to avoid unexpected charges. Use free tier wisely
Content
Build an end -to -end cloud data pipeline: Extract data from multiple sources, store raw data in cloud storage, transform and load into cloud data warehouse, create analysis queries, document the architecture.
What is covered: Vectors, matrices, operations like addition and multiplication.
Application: Data representation, image processing, neural networks.
Example:
1. Image as Matrix: A grayscale image is a matrix of pixel values. Neural networks process these matrices to recognize objects.
2. Matrix Multiplication: Used to combine weights and inputs in every layer of a neural network.
What is covered: Studying change (derivatives), finding minimum/maximum values.
Application: Training models by minimizing error (loss), adjusting weights.
Example:
1. Gradient Descent: The process of finding the best model parameters by moving in the direction that reduces error, like rolling a ball downhill.
2. Backpropagation: Calculating how much each weight in a neural network should change to improve predictions.
What is covered: Measuring uncertainty, analyzing data, making predictions.
Application: Predicting outcomes, evaluating models, handling randomness.
Example:
1. Spam Detection: Using probability to decide if an email is spam based on words it contains.
2. Model Evaluation: Calculating accuracy, precision, and recall to see how well a model performs.
What is covered: Logic, graphs, counting, combinations.
Application: Social networks, recommendation systems, logical reasoning.
Example:
1.Friend Recommendations: Using graph theory to suggest new friends on social media.
2.Counting Possibilities: Calculating how many ways a password can be formed.
This section includes a comprehensive evaluation covering all course topics, designed to measure understanding and mastery of key mathematical concepts presented throughout the course
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities