What you'll get
  • 17+ Hours
  • 6 Courses
  • Course Completion Certificates

Synopsis

  • Delivers in-depth training to help learners work efficiently with large-scale data in the Hadoop ecosystem
  • Provides one year of unlimited access to all course resources for flexible, self-paced learning
  • Designed for individuals aiming to build or advance a career in Big Data technologies
  • Includes certificates for all six modules, along with hands-on project exposure
  • Certificates come with unique verification links suitable for resumes and LinkedIn profiles
  • Delivered through convenient, self-paced video lessons

Content

Courses No. of Hours Certificates Details
PIG Fundamentals2h 1mView Curriculum
PIG Advanced2h 13mView Curriculum
Courses No. of Hours Certificates Details
Hadoop Project:08 - PIG/MapReduce - Analyze Loan Dataset2h 33mView Curriculum
Hadoop Project:11 - HIVE/PIG/MapReduce/Sqoop - Social Media Analysis3h 34mView Curriculum
Hadoop Project:12 - HIVE/PIG - Sensor Data Analysis5h 26mView Curriculum
Hadoop Project:13 - PIG/MapReduce - Youtube Data Analysis3h 02mView Curriculum

Description

This course equips learners with the practical knowledge required to work with Big Data tools and large datasets. It focuses on transforming raw data into meaningful insights that support informed business decisions.

Participants gain hands-on experience with data processing techniques and scripting concepts used for large-scale data analysis. By the end of the program, learners are capable of efficiently handling complex datasets and applying data-driven approaches in real-world scenarios.

Sample Certificate

Course Certification

Goals

  • Enable learners to efficiently process and analyze large datasets
  • Build confidence in using Big Data tools for real-world applications
  • Develop industry-relevant skills for data-driven decision-making
  • Support career growth in Big Data and analytics roles

Objectives

  • Understand the fundamentals of Big Data data processing
  • Learn scripting techniques for efficient data transformation
  • Work with large datasets in distributed environments
  • Apply concepts through practical exercises and projects
  • Gain job-ready skills aligned with Big Data industry needs

Highlights

  • Comprehensive coverage across six structured modules
  • One year of unrestricted access to learning materials
  • Self-paced video-based learning for maximum flexibility
  • Practical projects for hands-on experience
  • Verifiable certificates with shareable links
  • Career-focused curriculum aligned with industry demand

Requirements

  • Basic knowledge of computer operations
  • Prior exposure to any programming or coding concepts is helpful
  • Familiarity with the Big Data ecosystem and HDFS is recommended
  • Strong interest in learning and applying Big Data technologies

Target Audience

  • Big Data professionals seeking to strengthen their technical skill set
  • IT professionals planning a transition into Big Data roles
  • Students and beginners looking to start a career in data technologies
  • Data analysts and developers aiming to broaden their analytics expertise
  • Individuals interested in learning programming concepts within analytics

FAQ

Q1. Is this course suitable for beginners?

Yes. While prior exposure to Big Data concepts is beneficial, the course is structured to guide learners progressively.

Q2. Is the training self-paced?

Yes. Learners can access the video content anytime during the one-year access period.

Q3. Will I receive a certificate after completion?

Yes. Certificates are provided for all six modules and include verification links.

Q4. Does the course include practical experience?

Yes. Hands-on projects are included to reinforce the real-world application of concepts.

Career Benefits

  • Builds strong foundations for Big Data and analytics roles
  • Enhances resume value with verifiable certifications
  • Develops practical skills for handling large datasets
  • Improves career prospects in data engineering and analytics
  • Prepares learners for real-world Big Data project environments