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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

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.

Graduation Ceremony

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
    • Emerging trends: Artificial Intelligence, Blockchain, Internet of Things (IoT)
    • The future of Big Data analytics and its integration with other technologies
    Final Project and Certification Exam
  • Capstone Project
    • Apply learned concepts to a real-world Big Data analytics project
    • Data preprocessing, analysis, and visualization using Big Data tools
    • Presenting findings and insights to stakeholders
  • Certification Exam
    • Final exam covering all course topics
    • Evaluation based on theoretical understanding and practical applications
  • Key Tools and Technologies Covered:
    • Big Data Frameworks: Hadoop, Apache Spark, Apache Hive, Apache Pig
    • Data Storage: HDFS, NoSQL databases (MongoDB, Cassandra, HBase)
    • Data Analytics Tools: Python, R, Apache Flink, Apache Storm
    • Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
    • Cloud Platforms: AWS, Google Cloud, Microsoft Azure
    • Machine Learning Libraries: Spark MLlib, scikit-learn, TensorFlow, Keras

    End-to-End Career Assistance

    Comprehensive Career Support to Help You Shine

    Resume Revamp
    Resume Build

    Upgrade and polish resumes to make them stand out to potential employers.

    LinkedIn Optimization
    Industry Relevant Training

    Learn From digital marketing industry experts with real-world experience.

    GitHub Mastery
    Project Presentation

    Develop presentation skills and refine projects with constructive feedback from peers and instructors.

    Portfolio Building
    Career Guidance

    Dedicated Assistance to help figure out the right step for your career.

    Pitch Perfect
    Personalize Emphasis

    We ensure each student receives focused as we teach them key concepts

    Mock Interviews
    Mock Interview Drill

    Comprehensive Guidance to master interview skills & land your dream job.

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    Total Program Fee:

    ₹30,500 ₹25,500

    • Live instruction from Industry Veterans
    • Vibrant community just like a College Campus
    • Hands-on curriculum with Real-Life Projects
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