90 Hours
A structured curriculum across eight learning modules.
BIG DATA ANALYTICSCURRICULUM PREVIEW
Big Data Analytics
Course fee: INR 22,000 + 18% GST (INR 3,960). Total including GST: INR 25,960.
Confirm exclusions, materials, assessments and payment options with admissions.
Turn Massive Data into Meaningful Business Insights
Python & SQL · Hadoop Fundamentals · Apache Spark & PySpark · Data Pipelines · Cloud Analytics
Explore Python, SQL, distributed processing, data pipelines, cloud concepts, and visualization in a 90-hour learning path.
90 hourscourse duration
Contact admissionsupcoming batch
Confirm available delivery formatlearning mode
Course duration: 90 hours. Contact admissions for batch dates, daily timings and delivery arrangements.
Program Highlights
Hands-on Practice
Guided coding exercises and sample-data workflows.
Data Technologies
Explore Python, SQL, Hadoop, Apache Spark, and cloud platform concepts.
Project Focus
Practise processing, validation, analysis, and communicating results.
Program Overview
Build foundations for working with data at scale
Turn massive data into meaningful business insights.
This 90-hour practical training program introduces the collection, processing, storage, and analysis of large structured and unstructured datasets. Learners explore Python, SQL, Hadoop and Spark concepts, data pipelines, cloud analytics, and visualization through guided exercises and sample projects.
The course is intended for students, graduates, IT professionals, analysts, and aspiring data engineers. Basic computer knowledge is recommended. Familiarity with Python, SQL, or programming fundamentals can be helpful, but the curriculum starts with foundational concepts.
The tools, cloud provider, lab environment, project scope, batch format, fees, and assessments depend on the actual delivery plan and available infrastructure. Confirm those details with ECAREERPLUZ before enrolment.
What you will learn
- Understand volume, velocity, variety, veracity, and value in big data contexts.
- Prepare and analyze datasets with Python, Pandas, NumPy, and SQL fundamentals.
- Explore distributed storage, Hadoop architecture, HDFS, YARN, and MapReduce.
- Process larger datasets using Apache Spark and PySpark concepts.
- Build introductory data ingestion, transformation, and ETL workflows.
- Explore cloud storage, data lakes, and scalable analytics platforms.
- Create reports and communicate findings with suitable visualizations.
- Apply learning through guided sample-data projects and a capstone.
Who should enroll?
College students and recent graduates
Build foundational knowledge in data processing and analytics.
IT and computer science learners
Explore distributed computing, data platforms, and processing frameworks.
Working professionals
Develop introductory skills for data-focused work and career exploration.
Excel, SQL, Python, and analytics learners
Extend existing data skills toward distributed processing concepts.
Aspiring data analysts
Practise preparing datasets, querying information, and communicating findings.
Aspiring data engineers
Explore ingestion, ETL workflows, distributed storage, and processing fundamentals.
Eligibility
- Basic computer knowledge is recommended.
- Familiarity with Python, SQL, or programming fundamentals is helpful; the modules begin with foundations.
- Confirm device specifications, software access, cloud accounts, and any batch-specific prerequisites before enrolment.
Curriculum Preview
Suggested eight-module curriculum · 90 hours total.
Module 01: Big Data Fundamentals · 8 hours
- Big data concepts, business applications, and the 5 Vs: Volume, Velocity, Variety, Veracity, and Value
- Structured, semi-structured, and unstructured data
- Traditional databases compared with distributed data systems
- Common data-at-scale use cases, constraints, and responsible data handling
Module 02: Python and SQL for Data Processing · 12 hours
- Python foundations for working with data
- Load, inspect, filter, and transform tabular datasets
- Use Pandas and NumPy for introductory data manipulation
- Write SQL queries with SELECT, WHERE, JOIN, GROUP BY, and aggregations
- Validate and prepare data for downstream processing
Module 03: Hadoop Ecosystem · 12 hours
- Distributed storage concepts and HDFS architecture
- Hadoop cluster roles and data blocks
- YARN resource-management concepts
- MapReduce processing fundamentals
- Explore how Hadoop components fit into data workflows
Module 04: Apache Spark and PySpark · 18 hours
- Spark architecture and distributed processing concepts
- Create and transform Spark DataFrames
- Use Spark SQL for queries and aggregations
- Apply transformations, actions, joins, and filtering
- Explore PySpark data preparation and analysis workflows
- Discuss partitions, performance basics, and responsible resource use
Module 05: Data Pipelines and ETL · 10 hours
- Understand extraction, transformation, and loading workflows
- Ingest data from sample files or approved sources
- Clean, transform, and validate records
- Store processed data in a structured destination
- Review repeatability, logging, and basic pipeline quality checks
Module 06: Cloud and Distributed Data Platforms · 10 hours
- Cloud storage and data lake concepts
- Scalable compute and managed analytics platform fundamentals
- Explore distributed data platform architecture
- Compare batch processing and introductory streaming concepts
- Discuss access, cost, security, and provider-specific variation
Module 07: Analytics and Visualization · 8 hours
- Explore processed datasets using analytical questions
- Summarize trends and key measures
- Select appropriate charts and visual encodings
- Build reports or dashboards to communicate findings
- Present conclusions with context and data limitations
Module 08: Capstone Project and Assessment · 12 hours
- Select and document a suitable sample-data problem
- Design a data ingestion, preparation, and processing workflow
- Create reusable Python or PySpark scripts and SQL queries
- Validate outputs and prepare a dashboard or project report
- Present the technical approach, findings, and limitations
The modules add up to 90 hours (8 + 12 + 12 + 18 + 10 + 10 + 8 + 12). The cloud provider, tools enabled in the lab, infrastructure, and project dataset depend on the batch setup. Exercises use sample files or approved sources; production-scale systems are not implied.
Tools & Technologies
Technologies for distributed data workflows
Explore the frameworks and concepts named in the curriculum. The exact software versions, cloud provider, lab setup, and hands-on availability may differ by batch.
Distributed storage and processing
Apache Hadoop
Explore distributed storage concepts, HDFS architecture, YARN, and MapReduce fundamentals.
HDFS, YARN, and MapReduce concepts
Apache Spark and PySpark
Practise distributed data processing, DataFrames, Spark SQL, transformations, and aggregations.
Spark DataFrames, PySpark, Spark SQL, and performance basics
Programming and data preparation
Python, Pandas, and NumPy
Use introductory Python workflows to inspect, clean, transform, and prepare datasets.
Data manipulation, validation, and automation foundations
Querying and analytics
SQL and Spark SQL
Retrieve, filter, join, group, and analyze information in suitable data environments.
Relational SQL and distributed-query concepts
Cloud and distributed platforms
Cloud data platforms
Understand cloud storage, data lakes, scalable computing, and managed analytics services.
Provider selection and practical access depend on the batch
Reporting and visualization
Power BI and visualization tools
Present processed data through charts, reports, or dashboards for decision support.
Reporting and visualization concepts
Apache, Hadoop, Spark, Python, Microsoft Power BI, and cloud provider names are trademarks of their respective owners. Hands-on access to a Hadoop cluster, distributed compute environment, or cloud platform depends on lab infrastructure and the selected batch setup. Cloud usage can require accounts, permissions, and billable resources. Confirm whether any cloud access or credits are included before using a provider account. The outline introduces platform and processing concepts; it does not establish vendor certification or advanced production administration training.
Projects & Practical Work
Learn by building sample data workflows
These practice projects use example training scenarios. Data sources, project deliverables, and the scale of processing depend on approved datasets, lab infrastructure, and course delivery.
PROJECT 1
Retail Sales Analytics
Process a sample retail transaction dataset, explore product and regional trends, and prepare a business-focused summary.
Python, SQL, PySpark, data aggregation, and visualization.
PROJECT 2
E-commerce Data Processing
Combine sample customer, order, and product data; check missing or inconsistent values; and explore order and category patterns.
Python, SQL, Spark DataFrames, joins, and data validation.
PROJECT 3
End-to-End ETL Pipeline
Ingest data from sample files or approved sources, transform and validate records, and store processed outputs for reporting.
Python, SQL, PySpark, pipeline design, and cloud storage concepts.
PROJECT 4
Smart City Data Analysis
Explore sample traffic or sensor data, identify time-based patterns, and present findings through a visual report.
Python, Spark, aggregation, and data visualization.
PROJECT 5
Capstone: Documented Data Workflow
Select a sample-data problem and prepare a documented processing workflow with reusable scripts, SQL queries, validated outputs, and a presentation.
Data ingestion, transformation, Python or PySpark, SQL, reporting, and communication.
Capstone project steps
- Start with core data concepts and understand why distributed technologies are used.
- Practise Python, SQL, and data preparation with guided sample datasets.
- Progress from storage concepts to Spark processing and pipeline workflows.
- Apply learning through project exercises and a capstone assessment.
- Review results, technical decisions, data limitations, and presentation skills.
Potential deliverables include a documented workflow, cleaned sample dataset, reusable scripts, SQL queries, analytical outputs, and a project presentation. Use only public, fictional, or otherwise approved data, and clearly explain data limitations.
Learning Goals
Technical foundations for data at scale
By completing this 90-hour course and its guided exercises, learners should be able to:
Explain big data concepts
Describe common use cases, the 5 Vs, and distributed computing foundations.
Prepare and query data
Use Python and SQL fundamentals to clean, transform, and analyze datasets.
Understand Hadoop
Explain distributed storage, HDFS, YARN, and MapReduce concepts.
Process data with Spark
Practise Spark DataFrames, PySpark, and Spark SQL workflows.
Build basic data pipelines
Explore ingestion, transformation, validation, and structured outputs.
Explore cloud platforms
Understand data lakes, scalable compute, and managed analytics concepts.
Communicate findings
Present analytical results through suitable reports and visualizations.
Demonstrate practical learning
Document and present a sample capstone project and its limitations.
This course is an introductory foundation. Advanced data engineering, production operations, and specialist analytics roles may require additional programming experience, cloud-platform practice, domain knowledge, and professional experience.
Career Pathways & Indicative Salary Guide
Explore roles across analytics and data engineering
Organizations across IT services, banking, retail, healthcare, manufacturing, logistics, and e-commerce use data to inform decisions and operations. This course can introduce relevant concepts; actual role requirements vary by employer and experience.
Career pathways
Junior Data Analyst
Prepare datasets, run queries, explore patterns, and support analytical reporting.
Big Data Developer — Trainee
Support introductory distributed data processing and application workflows.
Junior Data Engineer
Assist with data ingestion, transformation, validation, and pipeline maintenance.
ETL Developer — Trainee
Help build and validate extraction, transformation, and loading workflows.
Cloud Data Operations Associate
Support data storage, platform monitoring, and routine data operations where trained.
Business Intelligence Analyst
Translate business data into reports, dashboards, and findings for stakeholders.
Indicative salary progression in India
Compare these planning ranges with current local opportunities. Pay varies by role, qualifications, experience and employer.
Fresher / trainee
₹2.5–4.5 LPAIndicative annual range · India1–3 years
₹4–8 LPAIndicative annual range · India3–5 years
₹7–14 LPAIndicative annual range · India5+ years, specialized roles
₹12–25+ LPAIndicative annual range · IndiaBusiness opportunities
- Data cleaning and reporting services for small businesses.
- Sales and inventory analytics for retail businesses.
- Automated reporting workflows for small and medium-sized enterprises.
- Data migration and basic ETL implementation with appropriate experience.
- Dashboard development and business intelligence reporting.
- Data engineering consulting after gaining relevant professional experience.
Build your future: Combine big data foundations with strong SQL, Python, cloud-platform knowledge, version control, and a practical portfolio to continue developing. Client work requires appropriate competence and permission to access data.
Placement Assistance
Prepare for opportunities in analytics and data processing
Ask about career preparation and placement support for this course. Confirm which services are actually offered by ECAREERPLUZ and available for the relevant batch.
Career support services
Resume & Portfolio Preparation
Present relevant technical skills, learning achievements, and approved project work.
- Highlight Python, SQL, and data processing foundations
- Document project goals, methods, and outcomes
Technical Interview Preparation
Practise questions associated with entry-level analytics and data roles.
- Python, SQL, Spark, and data processing fundamentals
- Explain project design and data decisions
Mock Interviews & Communication Skills
Practise professional introductions and clear technical explanations.
- Discuss technical trade-offs and data quality
- Explain findings with appropriate context
Job Application Guidance
Explore suitable openings and prepare applications, subject to available support.
- Discuss entry-level role pathways
- Review application preparation where available
Project Presentation
Practise presenting project objectives, approach, challenges, and findings.
- Present workflow diagrams and analytical outputs
- Describe data limitations and next steps
Career opportunities after training
- Students and recent graduates exploring entry-level roles.
- Working professionals seeking foundational data-processing skills.
- Analytics learners building practical project evidence.
- Aspiring data analysts, big data developers, and junior data engineers.
Outcomes depend on learner readiness, eligibility, employer requirements, and available opportunities.
Certification information
Discuss the issuing authority, assessment requirements and any separate provider exams with admissions.
Certificate availability and external accreditation are not confirmed. Ask ECAREERPLUZ to confirm course completion recognition, issuing authority, attendance, and assessment requirements.
Confirm whether a course completion certificate is offered.
Verify the issuing authority, attendance, and assessment criteria.
No Hadoop, Spark, cloud-provider, or external certification is implied.
Example learning journeys
Explore data. Build projects. Share what you learn.
Explore example Big Data Analytics projects and learning journeys.
Example project journey01
A First Data Processing Workflow
Sample profile · not a real learner
A sample learner practises preparing a dataset, checking its quality, and explaining the processing steps.
Practice focus: Develop practical skills through guided exercises and project work.
Example project journey02
Exploring Spark with Sample Data
Sample profile · not a real learner
A sample exercise uses Spark concepts to summarize data and compare results across categories.
Practice focus: Develop practical skills through guided exercises and project work.
Example project journey03
Presenting a Capstone Project
Sample profile · not a real learner
A sample capstone documents a data workflow, analytical output, and presentation of findings and limitations.
Practice focus: Develop practical skills through guided exercises and project work.
Data-Driven Learning, Delivered with Quality
Concepts, guided practice, and project-based progress
The learning approach moves from foundational concepts to processing workflows and project presentation. Confirm the actual training format, lab access, and instructor support for your batch.
Our learning approach
Structured Curriculum
Follow a 90-hour learning journey with eight modules and defined topics.
Practical Technology Exposure
Practise with tools and platforms selected for the training batch.
Assessment & Improvement
Use exercises, feedback, and project reviews to reflect on progress.
Career-Oriented Outcomes
Develop technical foundations, project documentation, and interview readiness.
Trainer profiles, batch size, cloud lab availability, assessment standards, placement services, and teaching guarantees are not yet confirmed. Confirm arrangements with admissions before joining.
Meet Your Mentors
Learn from Industry Experts and Experienced Professionals.
Mr. P. PRAKASH RAJ
Software Developer
Mrs. S. SIVANISHA
IT Trainer
Mr. S. MOHAMED RIYAS KHAN
UI & UX Designer
Mr. S. SABARINATHAN
AI AnalystFrequently Asked Questions
Everything You Need to Know Before Enrolling
What is Big Data Analytics?
Big Data Analytics involves processing and analyzing large or complex datasets to identify patterns, trends, and insights that may support business decisions.
What is the duration of this course?
The course duration is 90 hours. Contact admissions for batch dates and the detailed timetable.
Who is eligible to join?
This course is suitable for students, graduates, IT professionals, analysts, and aspiring data engineers. Ask admissions about any batch-specific prerequisites.
Do I need programming knowledge?
Basic computer knowledge is recommended. Python, SQL, or programming familiarity is helpful, but the curriculum begins with foundational topics.
Will I learn Hadoop and Apache Spark?
Yes. The outline includes Hadoop ecosystem fundamentals, including HDFS, YARN, and MapReduce, and Apache Spark and PySpark topics.
Is the course practical or theory-based?
The learning approach combines concepts, guided practice, coding exercises, and project work. Confirm lab access and practical arrangements for your batch.
Will I work on real-world projects?
The outline proposes sample projects in retail, e-commerce, ETL, and smart-city data analysis. Project datasets and infrastructure depend on course delivery; these are not confirmed client projects.
What career opportunities are available after the course?
Possible pathways to explore include junior data analyst, trainee big data developer, junior data engineer, trainee ETL developer, cloud data operations associate, and BI analyst. Employers may require additional experience and skills.
What salary can I expect after completing the course?
Actual compensation depends on role, location, employer, experience, skills, and project evidence.
Is placement assistance available?
Ask about resume, portfolio, interview, application, and project-presentation support. Confirm which services are available.
Will I receive a certificate?
Certificate availability and eligibility are not confirmed in the outline. Ask ECAREERPLUZ to confirm the issuing authority and any assessment or attendance requirements.
Can working professionals join?
The course is intended to be relevant to working professionals seeking to upskill. Confirm batch timings and delivery options with admissions.
Do I need a powerful computer?
Device requirements depend on the software and lab setup. Distributed processing or cloud exercises may use shared infrastructure; confirm specifications and access before enrolment.
How is Big Data Analytics different from Data Analytics?
Data analytics can focus on analyzing datasets generally, while big data analytics introduces distributed storage and processing concepts for larger or more complex data. The exact distinction depends on tools, workloads, and course scope.
How can I enroll?
Use the enquiry form on this page to ask ECAREERPLUZ about fees, schedule, delivery mode, prerequisites, lab setup, and admission steps.
What is the course fee, including GST?
Course fee: INR 22,000 + 18% GST (INR 3,960). Total including GST: INR 25,960. Confirm exclusions, materials, assessments and payment options with admissions.
Build your next chapter in Big Data Analytics.
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