Learn Mastering Data Engineering with Python Online: Free Course with AI Tutor & Certificate
Built for aspiring data professionals, this free python data engineering course teaches you to build scalable data pipelines. Over 13 modules and 83 video lessons taking about 1 hour, you'll learn data ingestion, storage, and processing. You can then apply these skills directly to real-world big data challenges.
Mastering Data Engineering with Python course syllabus
01 Introduction to Data Engineering
- Understanding Data Engineering
- The Role of Python in Data Engineering
- Overview of Data Pipelines
- Key Tools and Technologies
02 Python Basics for Data Engineering
- Python Syntax and Data Types
- Control Structures and Functions
- Working with Libraries
- Error Handling and Debugging
03 Data Collection and Ingestion
- Data Sources and Formats
- Web Scraping with Python
- Working with APIs
- Ingesting Data into Databases
04 Data Storage Solutions
- Relational Databases and SQL
- NoSQL Databases
- Cloud Storage Options
- Data Warehousing Concepts
05 Data Processing and Transformation
- Introduction to ETL Processes
- Data Cleaning Techniques
- Data Transformation with Pandas
- Automating Data Workflows
06 Data Analysis and Visualization
- Exploratory Data Analysis
- Data Visualization with Matplotlib
- Advanced Visualization with Seaborn
- Reporting and Dashboarding
07 Introduction to Big Data
- Understanding Big Data Concepts
- Introduction to Hadoop and Spark
- Working with PySpark
- Big Data Use Cases
08 Data Security and Compliance
- Data Privacy Principles
- Implementing Data Security
- Compliance and Legal Considerations
- Best Practices for Data Protection
09 Advanced Data Engineering Concepts
- Data Lakes vs. Data Warehouses
- Real-Time Data Processing
- Data Governance and Metadata Management
10 Advanced Data Manipulation with Python
- 1 Advanced Pandas Techniques
- 1.1 Multi-Indexing and Pivot Tables|Learning advanced indexing techniques and how to create pivot tables with Pandas for complex data analysis.
- 1.2 Data Merging and Joining|Exploring methods for merging and joining datasets to enrich data analysis.
- 1.3 Performance Optimization|Discussing ways to optimize Pandas code for better performance on large datasets.
- 2 Python for Data Integration
- 2.1 Data Integration Concepts|Understanding the principles of data integration and its importance in data engineering.
- 2.2 Python Tools for Data Integration|Exploring Python tools and libraries that facilitate data integration tasks.
- 2.3 Best Practices for Data Integration|Learning best practices for integrating data from various sources to create a unified view.
- 3 Advanced Visualization Techniques
- 3.1 Interactive Visualizations with Plotly|Introducing Plotly for creating interactive data visualizations in Python.
- 3.2 Geospatial Data Visualization|Exploring libraries like Geopandas for visualizing geospatial data.
- 3.3 Custom Visualization Tools|Discussing how to build custom visualization tools using Python for specialized data engineering needs.
11 Big Data Ecosystem and Tools
- 1 Big Data Processing Frameworks
- 1.1 Overview of Big Data Frameworks|Discussing the variety of frameworks available for big data processing and their use cases.
- 1.2 Choosing the Right Framework|Criteria for selecting the appropriate big data processing framework for a given project.
- 1.3 Integrating Big Data Frameworks|Exploring how to integrate big data frameworks into existing data engineering pipelines.
- 2 Big Data Analytics
- 2.1 Big Data Analytics Techniques|Understanding the techniques used in big data analytics to extract insights.
- 2.2 Tools for Big Data Analytics|Exploring tools like Apache Hive and Apache Impala for big data analytics.
- 2.3 Case Studies in Big Data Analytics|Analyzing case studies to understand the practical applications of big data analytics.
- 3 Big Data and the Cloud
- 3.1 Cloud Services for Big Data|Discussing how cloud services are used to manage and process big data.
- 3.2 Big Data Solutions on AWS, Azure, and GCP|Comparing big data solutions offered by major cloud providers like AWS, Azure, and Google Cloud Platform.
- 3.3 Building Scalable Big Data Applications|Learning how to build scalable big data applications on the cloud.
12 SQL and Data Querying Techniques
- 1 Advanced SQL for Data Engineering
- 1.1 Complex Queries and Optimization|Learning how to write complex SQL queries and optimize them for performance.
- 1.2 SQL Window Functions|Exploring the use of window functions in SQL for advanced data analysis.
- 1.3 SQL for Data Engineering Scenarios|Applying SQL in various data engineering scenarios to solve real-world problems.
- 2 Data Ingestion Strategies
- 2.1 Batch vs. Streaming Data Ingestion|Comparing batch and streaming data ingestion methods and understanding their trade-offs.
- 2.2 Data Ingestion Tools and Frameworks|Exploring tools and frameworks that support efficient data ingestion.
- 2.3 Best Practices for Data Ingestion|Discussing best practices to ensure reliable and efficient data ingestion.
- 3 Data Warehousing and ETL
- 3.1 Modern Data Warehousing Solutions|Understanding modern data warehousing solutions and their impact on data engineering.
- 3.2 ETL Optimization Techniques|Learning techniques for optimizing ETL processes for better performance and reliability.
- 3.3 ETL vs. ELT in Practice|Discussing the practical considerations when choosing between ETL and ELT approaches.
13 Data Quality and Cleaning
- 1 Strategies for Handling Missing Data
- 1.1 Types of Missing Data|Understanding the different types of missing data and their impact on analysis.
- 1.2 Imputation Techniques|Exploring various techniques for imputing missing data in datasets.
- 1.3 Missing Data in Time Series|Discussing specific challenges and solutions for handling missing data in time series.
- 2 Data Validation and Consistency
- 2.1 Data Validation Techniques|Learning about techniques for validating data accuracy and consistency.
- 2.2 Consistency Checks and Constraints|Implementing consistency checks and constraints to maintain data integrity.
- 2.3 Automated Data Validation Tools|Exploring tools that can automate the data validation process.
- 3 Data Cleaning Projects
- 3.1 Planning and Executing Data Cleaning Projects|Understanding how to plan and execute data cleaning projects effectively.
- 3.2 Case Studies of Data Cleaning|Analyzing case studies to learn from real-world data cleaning projects.
- 3.3 Maintaining Data Quality|Discussing strategies for maintaining data quality over time in data engineering pipelines.
Real-world projects you'll work on
Practice the exact kind of work Mastering Data Engineering with Python professionals do on the job.
Building a Scalable Data Pipeline for Real-Time Analytics at Uber
Designing a Comprehensive Data Warehouse Solution for Amazon's E-commerce Platform
Implementing a Secure and Compliant Data Storage System for Google's Cloud Services
Developing an Interactive Data Visualization Dashboard for Netflix's User Engagement Metrics
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Mastering Data Engineering with Python course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Software developers looking to transition into data engineering roles.
- IT professionals who want to understand big data ecosystems.
- Computer science students building practical skills for the tech industry in India.
- Data analysts wanting to learn backend data processing and storage.
Prerequisites
- Basic understanding of programming concepts is recommended.
- Familiarity with Python syntax will help you progress faster.
- No prior data engineering experience is strictly required.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Mastering Data Engineering with Python course: free, structured and job-focused
Designed for tech enthusiasts and developers, this python data engineering course teaches you how to manage large-scale data systems. You'll start with Understanding Data Engineering and quickly move into Python Basics for Data Engineering. Across 13 modules, we cover essential topics like Data Collection and Ingestion, Data Storage Solutions, and Data Processing and Transformation. You will also study Data Analysis and Visualization, Introduction to Big Data, and Data Security and Compliance.
Theory only gets you so far, so you'll build four hands-on projects to solidify your knowledge. You will design a Comprehensive Data Warehouse Solution for Amazon's E-commerce Platform and build a Scalable Data Pipeline for Real-Time Analytics at Uber. The curriculum also includes implementing a Secure and Compliant Data Storage System for Google's Cloud Services and developing an Interactive Data Visualization Dashboard for Netflix's User Engagement Metrics. By the end, you'll understand Advanced Data Engineering Concepts, Advanced Data Manipulation with Python, Big Data Ecosystem and Tools, SQL and Data Querying Techniques, and Data Quality and Cleaning.
LearnTube makes upskilling simple and highly interactive. Our platform pairs curated YouTube video lessons with an AI tutor that tests your understanding. After every lesson, you'll take a quick quiz to make sure the concepts stick. The learning experience is completely free, joining a community of over 60,000 learners. If you want to verify your new skills for employers, you can choose to add an optional paid certificate once you finish the 83 video lessons.
Jobs you can target after learning Mastering Data Engineering with Python
Mastering Data Engineering with Python is a gateway skill for some of the fastest-growing roles in tech.
Designs, builds, and maintains scalable data pipelines and architectures.
Creates software applications that process and analyze massive datasets.
Interprets data and builds visualization dashboards to support business decisions.
Typical entry-level salary ranges in India as of September 2026. Actual compensation varies by city, company and experience.
Earn a Mastering Data Engineering with Python certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Mastering Data Engineering with Python course.
Do I need prior experience for this python data engineering course?
No, you do not need extensive prior experience. While knowing basic Python helps, the course starts with fundamental concepts. You'll learn data engineering basics before moving into advanced data manipulation and big data tools.
What job roles can I get after learning data engineering with Python?
You can pursue roles like Data Engineer, Big Data Developer, or Data Analyst. Entry-level data engineers in India typically earn between ₹6.0 and ₹10.0 LPA. The skills you learn directly apply to these technical positions.
Is this data engineering bootcamp online accessible on mobile devices?
Yes, the entire platform is mobile-friendly. You can watch the 83 video lessons and take the AI-tutor quizzes directly from your phone. This makes it easy to study python programming for data engineering anywhere.
What is the data engineering course duration?
The total video content takes about 1 hour to watch. However, you should plan to spend additional time completing the quizzes and four hands-on projects. You can work through the 13 modules entirely at your own pace.
Does this python for data engineers course include a certificate?
Yes, you can purchase an optional certificate after finishing the free lessons. Adding this credential to your resume helps prove your new skills to the 900+ hiring partners connected with the LearnTube platform.
What projects are included in the data engineering curriculum?
You will complete four practical projects. These include building a scalable pipeline for Uber, designing an Amazon data warehouse, securing Google cloud storage, and creating an interactive visualization dashboard for Netflix user metrics.
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