Data Engineering Fundamentals (EDA + Databases + BigQuery)

About Course

In this module, you will build a strong foundation in Data Engineering, focusing on essential tools and techniques that power modern data pipelines and analytics systems. The course will guide you through the key processes involved in preparing and managing large datasets, including Exploratory Data Analysis (EDA), working with databases, and gaining hands-on experience with Google BigQuery—a powerful cloud data warehouse platform used by top companies for analyzing big data.

You’ll learn how to efficiently manage, clean, and explore data to derive actionable insights, how to query databases and handle structured/unstructured data, and how to leverage cloud technologies for scalable and optimized data storage and analysis.

Aspiring Data Engineers: Anyone looking to start a career in data engineering or working with big data technologies.
Data Scientists: Individuals who want to gain a better understanding of the data engineering pipeline and tools that complement machine learning workflows.
Database Enthusiasts: Learners looking to deepen their knowledge of database management, querying, and optimization.
Business Analysts: Those who want to leverage big data tools like BigQuery to improve data-driven decision-making.

Course Content

Introduction to Data Engineering

What is Data Engineering? And Why It’s the Backbone of AI & Analytics
The Data Lifecycle: From Raw to Refined
Essential Tools of Modern Data Engineering
How Data Engineers Design & Build Data Pipelines
Career Map and Skills for a Data Engineer

Exploratory Data Analysis (EDA)

Databases and SQL Fundamentals

Google BigQuery Overview

Data Cleaning and Transformation

Best Practices in Data Engineering

Handling Structured and Unstructured Data

Student Ratings & Reviews

No Review Yet

About Course

What Will You Learn?

Course Content

Introduction to Data Engineering

What is Data Engineering? And Why It’s the Backbone of AI & Analytics

The Data Lifecycle: From Raw to Refined

Essential Tools of Modern Data Engineering

How Data Engineers Design & Build Data Pipelines

Career Map and Skills for a Data Engineer

Exploratory Data Analysis (EDA)

Introduction to EDA: Why It’s the Superpower of Data Science

Data Collection, Loading, and Initial Inspection

Handling Missing, Incorrect, and Outlier Data

Data Visualization for Storytelling

Summary Statistics and Business Insights

Databases and SQL Fundamentals

Why Databases Matter in Data Science and AI

Types of Databases: Relational vs Non-Relational

Core SQL Concepts: Tables, Schemas, CRUD Operations

Writing SQL Queries: SELECT, WHERE, JOIN, GROUP BY

Real-World Applications of SQL for Data Science

Google BigQuery Overview

Introduction to Google BigQuery

Key Features and Benefits of BigQuery

Data Storage in BigQuery: Datasets, Tables, and Views

Writing Queries in BigQuery

Loading Data & Integrating with Other Google Cloud Tools

Data Cleaning and Transformation

Understanding Dirty Data and Its Impact

Handling Missing Data the Smart Way

Detecting and Treating Outliers

Data Transformation Techniques for Better Modeling

Building a Reusable Data Preprocessing Pipeline

Best Practices in Data Engineering

Understanding the Data Lifecycle

Data Quality Management (DQM) Essentials

Optimizing Data Storage and Access

Building Reliable and Maintainable Data Pipelines

Security, Compliance, and Governance in Data Engineering

Handling Structured and Unstructured Data

Introduction to Data Types: Structured vs Unstructured Data

Handling Structured Data: Databases and Spreadsheets

Cleaning and Preprocessing Structured Data

Handling Unstructured Data: Text and Media Files

Converting Unstructured to Structured Data for Analysis

Student Ratings & Reviews

Support

Resources

Policies