What you'll get
- 17+ Hours
- 6 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- This bundle provides access to all six courses included in the Projects package, eliminating the need to purchase each course separately.
- Learners will benefit from over 17 hours of comprehensive video content covering essential Big Data concepts.
- The course offers a thorough understanding of MapReduce and its practical applications within the Big Data ecosystem.
- Learners will have one-year access to all course materials, allowing them to learn at their own pace and convenience.
- The program is suitable for anyone committed to pursuing a career in Big Data.
- A basic understanding of management principles is recommended to maximize learning.
- Upon completion, learners receive a certificate for each of the six courses and associated projects.
- All certificates include unique verification links that can be shared on resumes or LinkedIn profiles to highlight the skills gained.
- The training is delivered through self-paced video lessons, offering flexibility and convenience for independent learning.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| MapReduce Fundamentals | 3h 01m | ✔ | View Curriculum |
| MapReduce Advanced | 5h 35m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Hadoop Project:10 - HIVE/MapReduce - Customers Complaints Analysis | 53m | ✔ | View Curriculum |
| Hadoop Project:13 - PIG/MapReduce - Youtube Data Analysis | 3h 02m | ✔ | View Curriculum |
| Hadoop Project:11 - HIVE/PIG/MapReduce/Sqoop - Social Media Analysis | 3h 34m | ✔ | View Curriculum |
| Hadoop Project:08 - PIG/MapReduce - Analyze Loan Dataset | 2h 33m | ✔ | View Curriculum |
Description
The MAPREDUCE course provides an in-depth exploration of one of the most important frameworks in the Big Data ecosystem. Learners will understand the fundamentals of MapReduce, including how it processes large-scale datasets efficiently using parallel computing. The course covers both the conceptual framework and practical implementation, helping learners develop the skills to design, execute, and optimize MapReduce jobs for real-world data processing tasks.
Through this self-paced video training with over 17 hours of content, participants will learn how to break down complex problems into mappable tasks, combine results with the reduce phase, and handle big data workflows effectively. The course also delves into performance optimization, fault tolerance, and best practices for writing efficient MapReduce programs.
As part of a comprehensive Projects bundle, this course gives access to six interconnected courses without separate purchases. Participants receive a Course Completion Certificate for each course and project, with verifiable links suitable for resumes and LinkedIn profiles to highlight newly acquired skills.
The program is ideal for anyone serious about pursuing a career in Big Data, from students to working professionals. A basic understanding of management or computing concepts is recommended to maximize learning outcomes. With one-year access to all materials, learners can progress at their own pace, revisiting lessons and projects to fully grasp the practical applications of MapReduce in the Big Data landscape.
Sample Certificate
Requirements
- Learners are expected to have a fundamental grasp of programming, since the course includes writing programs with tools such as HIVE and PIG.
- Strong communication skills are recommended to effectively present and convey processed data to stakeholders or team members.
- A foundational knowledge of data analysis, such as working with tools like MS Excel, is beneficial to understand data processing concepts and workflows.
Target Audience
- Working professionals seeking to advance their careers in Big Data.
- Students aiming for internships or entry-level roles in reputed organizations.
- Learners interested in hands-on projects using HIVE and PIG.
- IT professionals looking to transition into data science roles.
- Anyone motivated to gain practical knowledge of MapReduce.
