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BDA Course

Big Data Analytics Certification

Boost up Program for Mastermind & Growth Hack

On Campus Internship Plus Course Duration- 180 Hours

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Course Objectives:

Learn to build dynamic web applications with integrated frontend and backend.

Develop secure and efficient APIs using Laravel.

Optimize app performance and database queries with Eloquent ORM.

Gain skills to create and manage scalable full-stack web solutions.

Industry Readiness Certification Programs

overview
Industry Demand
syllabus
FAQ
Project & Training

Module 1:Introduction to Big Data and Analytics

What is Big Data?

Defining Big Data: Characteristics and key features (Volume, Velocity, Variety, Veracity, Value)
Importance of Big Data in the modern business landscape
Overview of Big Data analytics applications across industries

Big Data Technology Landscape

Introduction to Big Data tools and frameworks
The role of Data Science and Big Data in business intelligence (BI)
Big Data ecosystem overview: Hadoop, Spark, NoSQL databases, etc.

Data Analytics Basics

Data types and structures: Structured, semi-structured, unstructured data
Introduction to data analysis techniques and methodologies
Descriptive, diagnostic, predictive, and prescriptive analytics

Module 2: Big Data Architecture and Tools

Overview of Big Data Architecture

Big Data processing models: Batch Processing vs. Real-time Processing
Understanding the Hadoop ecosystem: HDFS, YARN, MapReduce
Distributed systems and parallel computing

Big Data Storage

Introduction to NoSQL databases (Cassandra, MongoDB, HBase)
Data storage strategies: Data lakes vs. Data warehouses
Cloud-based storage options (AWS S3, Google Cloud Storage, Azure Blob Storage)

Big Data Frameworks and Tools

Hadoop ecosystem: HDFS, MapReduce, Hive, Pig, and HBase
Apache Spark for Big Data analytics: RDDs, DataFrames, and MLlib
Data wrangling and ETL tools (Apache NiFi, Talend)
Introduction to Apache Kafka for real-time data streaming

Module 3:Data Processing and ETL Techniques

Data Collection and Integration

Data acquisition techniques for Big Data: Web scraping, APIs, IoT, and sensors
Data pipelines and workflows
Data integration challenges and strategies

Data Cleaning and Preprocessing

Data wrangling techniques: Handling missing data, outliers, and duplicates
Transforming data for analysis: Normalization, standardization, encoding
Preprocessing unstructured data (text, images, videos)

ETL (Extract, Transform, Load)

Introduction to ETL processes and tools
Building data pipelines with Apache NiFi and Talend
Loading data into Big Data storage systems

Module 4:Data Analytics with Hadoop and Spark

Analyzing Data with Hadoop

Writing and running MapReduce jobs
Using Apache Hive and Pig for SQL-like queries on Hadoop
Query optimization and performance tuning in Hadoop

Data Processing with Apache Spark

Introduction to Spark architecture and components
Working with RDDs and DataFrames in Spark
Spark SQL for querying Big Data
Introduction to Spark Streaming for real-time analytics
Using MLlib for machine learning tasks in Spark

Module 5: Data Visualization and Reporting

Big Data Visualization Techniques

Importance of data visualization in Big Data analytics
Visualization tools: Tableau, Power BI, D3.js, and Python-based libraries (Matplotlib, Seaborn)
Best practices for visualizing large datasets and complex data

Building Dashboards and Reports

Creating interactive dashboards with Tableau and Power BI
Real-time data visualization in Big Data applications
Integrating Big Data tools with visualization platforms

Data Storytelling

Creating compelling narratives from data
Presenting Big Data insights to stakeholders
Communicating complex insights in a clear and actionable manner

Module 6: Machine Learning and Predictive Analytics with Big Data

Introduction to Machine Learning

Understanding machine learning algorithms (supervised, unsupervised, reinforcement learning)
Introduction to model evaluation metrics: Accuracy, Precision, Recall, F1 Score

Big Data Machine Learning with Spark MLlib

Using Spark MLlib for classification, regression, clustering, and recommendation systems
Implementing decision trees, random forests, and support vector machines (SVM)
Working with large datasets for model training and validation

Predictive Analytics

Time series forecasting and trend analysis with Big Data
Using machine learning to predict business outcomes (sales forecasting, demand prediction)
Building predictive models on Big Data platforms

Module 7:Real-Time Data Processing and Streaming

Introduction to Real-Time Data Processing

Key differences between batch and real-time processing
Stream processing models and tools

Apache Kafka for Real-Time Data Streaming

Introduction to Apache Kafka architecture and use cases
Real-time data ingestion and processing with Kafka
Integrating Kafka with Spark Streaming for real-time analytics

Other Real-Time Processing Tools

Overview of Apache Flink and Apache Storm
Building real-time data pipelines using Flink or Storm
Streaming analytics use cases (real-time recommendation engines, fraud detection)

Module 8:Big Data Security and Privacy

Data Security Challenges in Big Data

Security concerns with Big Data storage and processing
Data encryption, access controls, and secure data storage
Security frameworks and tools for Big Data environments

Privacy Concerns and Regulations

Understanding data privacy laws (GDPR, CCPA)
Managing personally identifiable information (PII) in Big Data systems
Privacy-preserving techniques in data analytics (anonymization, differential privacy)

Module 9: Big Data in Cloud Computing

Introduction to Cloud Computing

Overview of cloud services and models (IaaS, PaaS, SaaS)
Cloud providers and their Big Data solutions (AWS, Azure, Google Cloud)

Big Data in the Cloud

Cloud-based Big Data platforms: Amazon EMR, Google BigQuery, Azure HDInsight
Running Hadoop and Spark clusters on cloud platforms
Managing scalability, cost optimization, and resource allocation in the cloud

Module 10: Big Data Analytics in Business and Industry Applications

Big Data in Business Decision-Making

Role of Big Data in business intelligence and decision-making
Case studies of Big Data analytics in marketing, finance, healthcare, and retail

Industry-Specific Applications of Big Data Analytics

Big Data applications in healthcare: Predictive modeling, personalized treatment, clinical trials
Big Data in finance: Fraud detection, risk management, algorithmic trading
Retail and e-commerce: Customer segmentation, demand forecasting, recommendation engines

Future Trends in Big Data Analytics