Big Data Analytics

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Big Data Analytics

Introduction to Big Data

Understand the basics of Big Data, its importance, and its impact on businesses. Learn about the 5 V's of Big Data: Volume, Velocity, Variety, Veracity, and Value. Discover how Big Data is used in various industries and the challenges involved in managing and analyzing large datasets.

Big Data Technologies and Tools

Explore the ecosystem of Big Data technologies and tools. This section covers popular frameworks and tools such as Apache Hadoop, Apache Spark, Apache Kafka, and NoSQL databases. Learn about the roles these tools play in data storage, processing, and analysis.

Data Ingestion and Storage

Learn how to ingest and store large volumes of data efficiently. Understand different data storage options, including HDFS, cloud storage, and distributed databases. Explore data ingestion tools like Apache Flume and Apache Nifi and learn how to design data pipelines.

Data Processing and Analysis

Discover techniques for processing and analyzing Big Data. Learn about batch and real-time data processing using Apache Spark, Hadoop MapReduce, and stream processing frameworks. Understand how to use tools like Hive and Pig for data querying and analysis.

Data Visualization and Reporting

Gain insights into data visualization and reporting for Big Data. Learn how to create effective visualizations using tools like Tableau, Power BI, and D3.js. Understand the importance of storytelling with data and how to convey insights to stakeholders.

Machine Learning and Big Data

Explore the integration of machine learning with Big Data. Learn about popular machine learning frameworks like TensorFlow and PyTorch, and how to apply machine learning algorithms to large datasets using tools like Apache Spark MLlib.

Big Data Security and Governance

Understand the importance of data security and governance in Big Data analytics. Learn about best practices for securing data, ensuring compliance with regulations, and managing data privacy. Explore tools and frameworks for implementing security and governance policies.

Cloud-Based Big Data Solutions

Learn about cloud-based Big Data solutions and services. Explore platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) for storing, processing, and analyzing Big Data in the cloud. Understand the benefits and challenges of cloud-based Big Data analytics.

Scalability and Performance Optimization

Discover techniques for optimizing the performance and scalability of Big Data systems. Learn about data partitioning, indexing, caching, and other strategies for improving the efficiency of data processing and analysis.

Case Studies and Industry Applications

Review case studies and real-world applications of Big Data analytics. Learn how different industries, such as healthcare, finance, and retail, leverage Big Data to gain insights and drive decision-making. Understand the impact of Big Data on business transformation and innovation.

Future Trends in Big Data Analytics

Explore emerging trends and technologies in Big Data analytics. Learn about advancements in artificial intelligence, IoT, and edge computing, and how they are shaping the future of Big Data. Understand the potential challenges and opportunities for businesses in the evolving landscape of Big Data.

Career Development and Certifications

Big Data Certifications Overview: Paths and preparation tips for Big Data-related certifications
Building a Career in Big Data Analytics: Skills development and career opportunities
Interview Preparation: Common interview questions and scenarios related to Big Data

Big Data Analytics Syllabus

1. Introduction to Big Data Analytics

  • Sources of Big Data
  • Structured and Unstructured Data
  • Data Storage and Processing Technologies
  • Data Analytics Frameworks and Tools
  • Introduction to Hadoop Ecosystem
  • Hadoop Distributed File System (HDFS)
  • MapReduce Programming Model
  • Apache Spark, Apache Hive, Apache Pig Introduction
  • Data Warehousing Concepts
  • Data Mining Techniques

2. Data Mining Techniques

  • Introduction to Data Mining
  • Data Preprocessing Techniques
  • Data Transformation Techniques
  • Association Rule Mining
  • Clustering Techniques
  • Classification Techniques
  • Anomaly Detection
  • Text Mining
  • Time Series Analysis
  • Evaluation and Validation of Data Mining Models

3. Data Warehousing and ETL

  • Introduction to Data Warehousing
  • Data Warehousing Architecture
  • OLAP and OLTP Concepts
  • ETL (Extract, Transform, Load) Process and Its Importance
  • Data Modeling for Data Warehousing
  • Dimensional Modeling
  • Fact Tables and Dimension Tables
  • ETL Tools and Techniques
  • Data Cleansing and Data Profiling
  • Performance Tuning and Optimization for Data Warehousing

4. Big Data Analytics Tools

  • Hadoop Ecosystem Tools: HDFS, MapReduce, Pig, Hive, Sqoop, Flume, etc.
  • NoSQL Databases: MongoDB, Cassandra, HBase, etc.
  • Tools for Data Visualization: Tableau, QlikView, Power BI, etc.
  • Machine Learning Tools: Python, R, Mahout, etc.
  • Cloud-Based Big Data Analytics Tools: AWS, Azure, Google Cloud Platform, etc.
  • Apache Spark and Related Tools: Spark SQL, Spark Streaming, Spark MLlib, etc.
  • Graph Databases: Neo4j, OrientDB, etc.

5. Data Visualization

  • An Overview of Data Visualization and Its Significance
  • Types of Data Visualization Techniques and Tools
  • Best Practices for Designing Effective Visualizations
  • Principles of Visual Perception and How They Apply to Data Visualization
  • Exploratory Data Analysis Using Visualizations
  • Interactive Visualizations and Dashboards
  • Storytelling with Data and Creating Compelling Narratives
  • Designing for Different Audiences and Purposes
  • Common Data Visualization Pitfalls to Avoid
  • Advanced Data Visualization Techniques and Tools

6. Algorithms for Machine Learning in Big Data Analytics

  • Overview of Different Types of Machine Learning Algorithms
  • Preprocessing and Cleaning of Big Data for Machine Learning
  • Regression Analysis for Big Data Using Linear and Logistic Regression
  • Clustering Algorithms for Unsupervised Learning
  • Dimensionality Reduction Techniques
  • Classification Algorithms
  • Deep Learning Algorithms
  • Evaluating and Selecting Appropriate Machine Learning Models for Big Data Analytics
  • Deploying and Scaling Machine Learning Models in Distributed Computing Environments (e.g., Hadoop, Spark)

7. Real-time Big Data Analytics

  • Understanding the Characteristics of Real-time Data
  • Overview of Real-time Big Data Analytics Architecture
  • Real-time Data Ingestion Techniques and Tools
  • Frameworks Like Apache Kafka, Apache Storm
  • Real-time Data Processing and Analysis Techniques
  • Real-time Machine Learning Techniques
  • Real-time Data Visualization Techniques and Tools
  • Use Cases and Applications of Real-time Big Data Analytics

8. Case Studies and Projects

  • Customer Segmentation
  • Fraud Detection
  • Sentiment Analysis
  • Predictive Maintenance
  • Supply Chain Optimization
  • Healthcare Analytics
  • Financial Analytics
  • IoT Analytics
  • Social Network Analysis
  • Image and Video Analytics

Training

Basic Level Training

Duration : 1 Month

Advanced Level Training

Duration : 1 Month

Project Level Training

Duration : 1 Month

Total Training Period

Duration : 3 Months

Course Mode :

Available Online / Offline

Course Fees :

Please contact the office for details

Placement Benefit Services

Provide 100% job-oriented training
Develop multiple skill sets
Assist in project completion
Build ATS-friendly resumes
Add relevant experience to profiles
Build and enhance online profiles
Supply manpower to consultants
Supply manpower to companies
Prepare candidates for interviews
Add candidates to job groups
Send candidates to interviews
Provide job references
Assign candidates to contract jobs
Select candidates for internal projects

Note

100% Job Assurance Only
Daily online batches for employees
New course batches start every Monday
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