Diploma In Data Engineering (Job Guaranteed)

About Course

Diploma In Data Engineering by Learnatic Academy is a comprehensive and industry focused training program designed to transform beginners into skilled professionals in the field of modern data technologies. This premium diploma includes 95 detailed recorded lectures, each designed to deliver in depth practical knowledge and real world insights. Every lecture is approximately four hours long, providing extensive learning sessions that help students clearly understand complex concepts and practical applications in the Big Data ecosystem.

This program is carefully structured to provide learners with professional level training that aligns with industry demands. Students will gain strong technical knowledge, practical exposure, and career ready skills required by companies working with large scale data systems. The entire diploma is available through the Learnatic Academy LMS platform with complete recorded access, allowing students to learn at their own pace. Upon successful completion of the program, learners will receive professional certification and will also be supported with job placement opportunities with a job guarantee program, helping them transition confidently into the data technology industry.

Complete understanding of Big Data fundamentals and modern data ecosystems
Practical knowledge of handling large scale data systems
Industry level workflows used in data driven organizations
Understanding of data processing, data storage, and analytics concepts
Hands on learning through detailed step by step lectures
Skills required to work in professional data technology environments
Real world concepts used in companies working with large datasets
Career focused training designed to prepare students for job opportunities

Course Content

BigData BootCamp 2.0 (Job Guaranteed)

Lecture #01 – Introduction to Big Data and Analytics

04:22:00
Lecture #02 – Understanding Data Science and Its Applications

20:00
Lecture #03 – Overview of Big Data Ecosystem

04:10:00
Lecture #04 – Data Types, Sources, and Collection Methods

04:55:00
Lecture #05 – Introduction to Data Warehousing

04:12:00
Lecture #06 – Data Governance, Quality, and Security

01:02:00
Lecture #07 – Tools and Technologies for Big Data

04:27:00
Lecture #08 – Introduction to Hadoop Ecosystem

04:37:00
Lecture #09 – HDFS Architecture and Implementation

05:35:00
Lecture #10 – MapReduce Fundamentals

05:20:00
Lecture #11 – Hive Basics and Querying

05:10:00
Lecture #12 – Pig Programming and Data Processing

05:30:00
Lecture #13 – HBase for Big Data Storage

04:40:00
Lecture #14 – Data Lakes vs Data Warehouses

04:40:00
Lecture #15 – Python for Data Analysis

04:50:00
Lecture #16 – Python Libraries: NumPy and Pandas

04:52:00
Lecture #17 – Data Cleaning and Transformation Techniques

05:00:00
Lecture #18 – Introduction to R Programming for Data Analysis

04:57:00
Lecture #19 – Data Visualization with Python & R

03:20:00
Lecture #20 – Working with Jupyter Notebooks

05:02:00
Lecture #21 – Introduction to Apache Spark

05:00:00
Lecture #22 – Spark RDDs and DataFrames

01:42:00
Lecture #23 – Spark SQL for Data Processing

04:40:00
Lecture #24 – Spark Streaming and Real-Time Analytics

04:52:00
Lecture #25 – Machine Learning with Spark MLlib

02:35:00
Lecture #26 – Kafka for Real-Time Data Streaming

04:32:00
Lecture #27 – Introduction to NoSQL Databases

04:08:00
Lecture #28 – MongoDB Basics and Operations

02:22:00
Lecture #29 – Cassandra for Big Data

04:18:00
Lecture #30 – Redis and In-Memory Databases

04:45:00
Lecture #31 – Querying and Data Retrieval Techniques

01:52:00
Lecture #32 – Introduction to Data Analytics

03:38:00
Lecture #33 – Descriptive, Predictive, and Prescriptive Analytics

04:17:00
Lecture #34 – Supervised Learning Techniques

01:45:00
Lecture #35 – Unsupervised Learning Techniques

03:55:00
Lecture #36 – Regression and Classification Models

03:40:00
Lecture #37 – Clustering Algorithms

01:35:00
Lecture #38 – Recommendation Systems

03:45:00
Lecture #39 – Real-World Project Setup

03:32:00
Lecture #40 – Data Pipeline Design and Workflow

02:00:00
Lecture #41 – ETL Process Implementation

03:37:00
Lecture #42 – Data Cleaning & Transformation for Projects

03:22:00
Lecture #43 – Data Analysis and Reporting Techniques

25:00
Lecture #44 – Visualization of Project Results

03:32:00
Lecture #45 – Introduction to Cloud Platforms

03:17:00
Lecture #46 – AWS for Big Data Applications

01:10:00
Lecture #47 – Azure Big Data Services Overview

04:05:00
Lecture #48 – GCP for Big Data and Analytics

03:30:00
Lecture #49 – Deployment of Big Data Projects on Cloud

04:50:00
Lecture #50 – AI and Deep Learning Fundamentals

02:55:00
Lecture #51 – Natural Language Processing (NLP)

03:17:00
Lecture #52 – Image and Video Analytics with AI

03:27:00
Lecture #53 – Predictive Analytics for Business

03:42:00
Lecture #54 – Integration of AI with Big Data Pipelines

55:00
Lecture #55 – Advanced Data Processing Techniques in Big Data Systems

03:02:00
Lecture #56 – Working with Large Scale Distributed Data Processing

03:32:00
Lecture #57 – Advanced Apache Spark Transformations and Actions

03:37:00
Lecture #58 – Optimizing Spark Performance for Big Data Workloads

03:35:00
Lecture #59 – Real Time Data Processing with Spark Streaming

02:22:00
Lecture #60 – Introduction to Apache Kafka for Data Streaming

01:55:00
Lecture #61 – Building Real Time Data Pipelines

01:57:00
Lecture #62 – Data Integration Techniques for Enterprise Systems

01:52:00
Lecture #63 – Working with Structured and Unstructured Data

03:18:00
Lecture #64 – Data Cleaning and Data Transformation at Scale

01:55:00
Lecture #65 – Advanced Data Visualization Techniques

02:17:00
Lecture #66 – Business Intelligence Concepts for Data Analysis

02:42:00
Lecture #67 – Building Dashboards for Data Driven Decision Making

03:38:00
Lecture #68 – Data Modeling and Data Architecture Fundamentals

03:52:00
Lecture #69 – Data Engineering Workflow and Best Practices

01:00:00
Lecture #70 – Introduction to Machine Learning for Big Data

03:12:00
Lecture #71 – Preparing Datasets for Machine Learning Models

03:57:00
Lecture #72 – Supervised Learning Techniques for Big Data

01:59:00
Lecture #73 – Unsupervised Learning and Clustering Methods

03:15:00
Lecture #74 – Predictive Analytics and Forecasting Techniques

03:37:00
Lecture #75 – Recommendation Systems and Data Personalization

02:10:00
Lecture #76 – Introduction to Deep Learning Concepts

03:12:00
Lecture #77 – Natural Language Processing for Big Data Applications

03:32:00
Lecture #78 – Data Security and Privacy in Big Data Systems

01:08:00
Lecture #79 – Governance and Compliance in Data Management

04:57:00
Lecture #80 – Introduction to Cloud Computing for Big Data

04:55:00
Lecture #81 – Deploying Big Data Solutions on Cloud Platforms

01:35:00
Lecture #82 – Data Warehousing and Cloud Based Analytics

04:58:00
Lecture #83 – Building Scalable Data Pipelines in the Cloud

04:52:00
Lecture #84 – Monitoring and Managing Big Data Infrastructure

01:52:00
Lecture #85 – Big Data Use Cases in Real World Industries

04:15:00
Lecture #86 – Case Studies of Successful Data Driven Companies

01:55:00
Lecture #87 – End to End Big Data Project Implementation

01:15:00
Lecture #88 – Data Analysis and Reporting for Business Insights

04:05:00
Lecture #89 – Building a Professional Data Portfolio

04:27:00
Lecture #90 – Preparing for Big Data and Data Engineer Job Roles

05:00:00
Lecture #91 – Resume Building and Interview Preparation for Data Careers

04:32:00
Lecture #92 – Capstone Big Data Project and Practical Implementation

01:22:00
Lecture #93 – Final Program Review, and Career Roadmap

03:55:00
Final Exam 1: Big Data Fundamentals & Tools
Final Exam 2: Advanced Big Data Analytics & Projects

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About Course

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Course Content

BigData BootCamp 2.0 (Job Guaranteed)

Lecture #01 – Introduction to Big Data and Analytics

Lecture #02 – Understanding Data Science and Its Applications

Lecture #03 – Overview of Big Data Ecosystem

Lecture #04 – Data Types, Sources, and Collection Methods

Lecture #05 – Introduction to Data Warehousing

Lecture #06 – Data Governance, Quality, and Security

Lecture #07 – Tools and Technologies for Big Data

Lecture #08 – Introduction to Hadoop Ecosystem

Lecture #09 – HDFS Architecture and Implementation

Lecture #10 – MapReduce Fundamentals

Lecture #11 – Hive Basics and Querying

Lecture #12 – Pig Programming and Data Processing

Lecture #13 – HBase for Big Data Storage

Lecture #14 – Data Lakes vs Data Warehouses

Lecture #15 – Python for Data Analysis

Lecture #16 – Python Libraries: NumPy and Pandas

Lecture #17 – Data Cleaning and Transformation Techniques

Lecture #18 – Introduction to R Programming for Data Analysis

Lecture #19 – Data Visualization with Python & R

Lecture #20 – Working with Jupyter Notebooks

Lecture #21 – Introduction to Apache Spark

Lecture #22 – Spark RDDs and DataFrames

Lecture #23 – Spark SQL for Data Processing

Lecture #24 – Spark Streaming and Real-Time Analytics

Lecture #25 – Machine Learning with Spark MLlib

Lecture #26 – Kafka for Real-Time Data Streaming

Lecture #27 – Introduction to NoSQL Databases

Lecture #28 – MongoDB Basics and Operations

Lecture #29 – Cassandra for Big Data

Lecture #30 – Redis and In-Memory Databases

Lecture #31 – Querying and Data Retrieval Techniques

Lecture #32 – Introduction to Data Analytics

Lecture #33 – Descriptive, Predictive, and Prescriptive Analytics

Lecture #34 – Supervised Learning Techniques

Lecture #35 – Unsupervised Learning Techniques

Lecture #36 – Regression and Classification Models

Lecture #37 – Clustering Algorithms

Lecture #38 – Recommendation Systems

Lecture #39 – Real-World Project Setup

Lecture #40 – Data Pipeline Design and Workflow

Lecture #41 – ETL Process Implementation

Lecture #42 – Data Cleaning & Transformation for Projects

Lecture #43 – Data Analysis and Reporting Techniques

Lecture #44 – Visualization of Project Results

Lecture #45 – Introduction to Cloud Platforms

Lecture #46 – AWS for Big Data Applications

Lecture #47 – Azure Big Data Services Overview

Lecture #48 – GCP for Big Data and Analytics

Lecture #49 – Deployment of Big Data Projects on Cloud

Lecture #50 – AI and Deep Learning Fundamentals

Lecture #51 – Natural Language Processing (NLP)

Lecture #52 – Image and Video Analytics with AI

Lecture #53 – Predictive Analytics for Business

Lecture #54 – Integration of AI with Big Data Pipelines

Lecture #55 – Advanced Data Processing Techniques in Big Data Systems

Lecture #56 – Working with Large Scale Distributed Data Processing

Lecture #57 – Advanced Apache Spark Transformations and Actions

Lecture #58 – Optimizing Spark Performance for Big Data Workloads

Lecture #59 – Real Time Data Processing with Spark Streaming

Lecture #60 – Introduction to Apache Kafka for Data Streaming

Lecture #61 – Building Real Time Data Pipelines

Lecture #62 – Data Integration Techniques for Enterprise Systems

Lecture #63 – Working with Structured and Unstructured Data

Lecture #64 – Data Cleaning and Data Transformation at Scale

Lecture #65 – Advanced Data Visualization Techniques

Lecture #66 – Business Intelligence Concepts for Data Analysis

Lecture #67 – Building Dashboards for Data Driven Decision Making

Lecture #68 – Data Modeling and Data Architecture Fundamentals

Lecture #69 – Data Engineering Workflow and Best Practices

Lecture #70 – Introduction to Machine Learning for Big Data

Lecture #71 – Preparing Datasets for Machine Learning Models

Lecture #72 – Supervised Learning Techniques for Big Data

Lecture #73 – Unsupervised Learning and Clustering Methods

Lecture #74 – Predictive Analytics and Forecasting Techniques

Lecture #75 – Recommendation Systems and Data Personalization

Lecture #76 – Introduction to Deep Learning Concepts