BIGDATA ANALYSIS

Unlock the power of massive datasets and transform raw information into actionable insights with our comprehensive Big Data Analysis program! Master essential data processing frameworks like Hadoop and Spark while exploring advanced analytics techniques and data visualization tools. Dive deep into statistical modeling, predictive analytics, and business intelligence as you learn to handle structured and unstructured data at scale. Work with cutting-edge technologies including cloud computing platforms, NoSQL databases, and real-time streaming analytics. Whether you're aspiring to become a data scientist, business analyst, or big data engineer, this course will empower you to extract meaningful patterns from complex datasets and drive data-driven decision making that transforms businesses and industries!

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Apply for Internship

Contact us and Submit your application and get started by providing the necessary documents.

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Review & Selection

Our team will review your application and shortlist candidates based on skills and eligibility.

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Training Sessions

Attend hands-on training sessions in AI, Machine Learning, and Data Science to gain practical knowledge.

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Live Projects

Work on real-world projects that solve critical industry problems, experiment with the code and sharpen your skills.

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Completion & Certification

Receive your certification, showcasing your proficiency in AI and Data Science to potential employers.

Course Outcomes

Big Data Course Learning Objectives
Understand the fundamentals of Big Data and its importance in modern analytics.
Learn to work with large datasets using tools like Hadoop and Spark.
Perform data processing and transformation using distributed computing systems.
Use SQL and NoSQL databases for managing structured and unstructured data.
Visualize large-scale data using appropriate tools and techniques.
Understand data storage, retrieval, and processing in cloud-based environments.
Apply data analysis techniques to extract meaningful insights from big data.
Build and evaluate real-time data pipelines and analytical applications.
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PREREQUISITE

  • Basic understanding of Python programming.
  • Familiarity with data analysis tools (Pandas, NumPy).
  • Interest in learning Machine Learning and AI concepts.
  • Ability to work with simple data visualization tools (Matplotlib or Seaborn).
  • Willingness to solve real-world problems using AI techniques.

PREREQUISITE

  • Basic understanding of Python programming.
  • Familiarity with data analysis tools (Pandas, NumPy).
  • Interest in learning Machine Learning and AI concepts.
  • Ability to work with simple data visualization tools (Matplotlib or Seaborn).
  • Willingness to solve real-world problems using AI techniques.

Program Modules

"Foundations of Big Data: Concepts and Tools"
01

Introduction to Big Data

Understanding Big Data characteristics, types, and industry relevance

02

Big Data Architecture

HDFS, Distributed File Systems, and Big Data Ecosystem Overview

03

Hadoop Framework

HDFS, MapReduce, YARN, and Hadoop cluster setup

04

Apache Spark

RDD, Spark SQL, DataFrames, and Spark Streaming

"Data Processing and Analytics Techniques"
05

Data Ingestion Tools

Sqoop, Flume, Kafka for importing structured and unstructured data

06

Data Warehousing

Hive, HBase, and NoSQL databases for scalable data storage

07

Big Data Analytics

Using Python, R, and SQL to perform data cleaning and visualization

08

Cloud and Big Data

Big Data processing using AWS, Azure, and Google Cloud platforms

"Project, Ethics and Real-World Integration"
09

Data Security & Governance

Privacy, Ethics, and Secure Handling of Big Data

10

Real-Time Analytics

Processing and analyzing streaming data using Apache Storm and Spark

11

Capstone Project

End-to-end Big Data project: from ingestion to insights

12

Career & Portfolio Building

Industry applications, mock interviews, resume and project showcase

Career Opportunities

Career Opportunities in Big Data Analysis

Open doors to a rewarding career :

  • Big Data Analyst
  • Data Engineer
  • Business Intelligence Analyst
  • Data Architect
  • Cloud Data Engineer
  • ETL Developer
  • Data Scientist (Big Data Focus)
  • Big Data Developer

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