Live Online Apache Flink Course for Data Analytics

 

https://cvmantra.com/product/live-online-apache-flink-course-for-data-analytics/

Duration: 4 Weeks | Total Time: 40 Hours

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertise

Week 1: Introduction to Apache Flink and Core Concepts (6 Hours)

1. Overview of Apache Flink and its role in modern data analytics
2. Understanding distributed stream and batch processing
3. Flink architecture — Job Manager, Task Manager, and DataFlow
4. Setting up Apache Flink (Local/Cluster mode)
5. Writing your first Flink application
6. Hands-on: Data stream basics and running simple jobs

Week 2: DataStream API and Transformations (6 Hours)

1. Working with Flink’s DataStream API
2. Key transformations: map, flatMap, filter, reduce, and aggregate
3. Handling event time and processing time
4. Understanding windows (tumbling, sliding, session windows)
5. State management and checkpointing fundamentals
6. Hands-on: Real-time stream transformations and aggregation exercises

Week 3: Advanced Stream Processing and Integrations (6 Hours)

1. Connecting Flink with Kafka for real-time data ingestion
2. Integrating with external systems (HDFS, Cassandra, JDBC, Elasticsearch)
3. Flink Table API and SQL for declarative analytics
4. Working with stateful streaming and process functions
5. Managing late data and watermarks
6. Hands-on: Building a streaming pipeline with Kafka + Flink + HDFS

Week 4: Flink in Production and Analytics Project (6 Hours)

1. Flink cluster deployment and scaling strategies
2. Monitoring, metrics, and performance optimization
3. Error handling, fault tolerance, and backpressure management
4. Advanced use cases — IoT analytics, real-time dashboards, anomaly detection
5. Capstone Project: End-to-End Real-Time Analytics Pipeline using Flink
6. Final review, assessment, and Q&A

Mini Project Ideas (Week 4 Hands-on)

Learners will design and deploy real-time data analytics applications such as:

1. Project 1: Real-Time Log Monitoring System using Flink + Kafka
2. Project 2: Sensor Data Stream Analytics with Flink SQL
3. Project 3: Fraud Detection Pipeline using Flink CEP and ML Integration

Teaching Methodology

• Live Interactive Sessions with practical demos
• Hands-on Labs after each topic
• Assignments & Quizzes for concept reinforcement
• Mini Project & Peer Review during final week
• Q&A and Debugging Sessions for practical problem-solving

Final Deliverables

• Certificate of Completion
• End-to-End Streaming Analytics Project
• Strong understanding of Flink for real-time and batch data analytics

Course Outcomes:

By the end of this course, learners will be able to:

• Understand Apache Flink’s architecture, APIs, and ecosystem.
• Develop Flink applications for both batch and real-time stream processing.
• Integrate Flink with data sources like Kafka, Hadoop, and databases.
• Implement analytics and transformations using Flink DataStream and Table APIs.
• Apply Flink for use cases in data analytics and predictive processing.

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Live Online Natural Language Processing (NLP) Course for Data Science

 

Duration: 6 Weeks | Total Time: 36 Hours

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertise

Week 1: Introduction to NLP (6 Hours)

1. Overview of NLP and Applications — 1 hr

• Understanding NLP in AI and Data Science
• Real-world applications (Chatbots, Sentiment Analysis, Translation, etc.)

2. Text Preprocessing Basics — 2 hrs

• Tokenization, Stopwords, Lemmatization, Stemming
• Using NLTK and spaCy

3. Text Normalization Techniques — 1 hr

• Lowercasing, punctuation removal, noise filtering

4. Bag of Words and TF-IDF — 2 hrs

• Creating document-term matrices
• Feature extraction using scikit-learn

Week 2: Advanced Text Representation (6 Hours)

1. Word Embeddings Overview — 1 hr

• Limitations of BoW, importance of contextual meaning

2. Word2Vec and GloVe — 2 hrs

• Skip-gram vs CBOW
• Implementing embeddings using Gensim

3. Sentence Embeddings and Document Vectors — 2 hrs

• Sentence Transformers, Doc2Vec

4. Dimensionality Reduction for Text Data — 1 hr

• PCA, t-SNE for word visualization

Week 3: Text Classification Techniques (6 Hours)

1. Machine Learning for Text Classification — 2 hrs

• Logistic Regression, Naive Bayes, SVM

2. Pipeline Building and Evaluation — 2 hrs

• Cross-validation, confusion matrix, precision-recall

3. Project 1: Sentiment Analysis with Scikit-learn — 2 hrs

• Twitter/IMDb review dataset
• End-to-end model building

Week 4: Deep Learning for NLP (6 Hours)

1. Neural Networks for NLP — 1 hr

• Word embeddings + neural layers

2. Recurrent Neural Networks (RNN, LSTM, GRU) — 2 hrs

• Sequential modeling, vanishing gradient issue

3. Text Generation and Sequence Models — 2 hrs

• Character-level models, practical demo

Week 5: Transformer Models & Modern NLP (6 Hours)

1. Introduction to Transformers — 2 hrs

• Encoder-decoder architecture, self-attention mechanism

2. Understanding BERT, GPT, and Other Models — 2 hrs

• Fine-tuning pre-trained models for NLP tasks

3. Hands-on: Text Classification using BERT — 2 hrs

• Using Hugging Face Transformers library

Week 6: NLP Applications & Capstone Project (6 Hours)

1. NLP in Real-World Systems — 1 hr

• Chatbots, Recommendation Engines, Search Systems

2. Named Entity Recognition (NER) & Topic Modeling — 2 hrs

• spaCy NER, Latent Dirichlet Allocation (LDA)

3. Capstone Project: End-to-End NLP Solution — 3 hrs

• Example: “Customer Feedback Analysis System”
• Data cleaning → Feature extraction → Model building → Deployment

Course Outcomes

By the end of this course, learners will be able to:

• Preprocess and clean textual data efficiently.
• Apply both statistical and deep learning models for NLP tasks.
• Implement word embeddings and transformer-based models.
• Build end-to-end NLP projects for data science applications.
• Use popular NLP libraries: NLTK, spaCy, scikit-learn, Gensim, TensorFlow, PyTorch, Hugging Face.

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Live Online Docker Course for Data Engineering

 

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertiseLive Online Docker Course for Data Engineering

Week 1: Introduction to Containers & Docker Basics (Beginner)

Sessions: 2 × 3–4 hours

• Introduction to Containerization
  • Virtualization vs. containerization
  • Benefits for data engineering pipelines
• Docker Architecture Overview
  • Docker Engine, CLI, Daemon
  • Images, Containers, Registries
• Installing Docker
  • Docker Desktop / Docker Engine
  • Basic commands: docker run, docker ps, docker stop, docker rm
• Running Your First Container
  • Explore container lifecycle
  • Hello World, Python container examples
• Hands-on Lab:
  • Run multiple containers, inspect logs, and clean up containers

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Week 2: Docker Images, Dockerfile & Basic Pipelines (Beginner → Intermediate)

Sessions: 2 × 3–4 hours

• Docker Images Basics
  • Pull, tag, inspect, remove images
  • Docker Hub and public/private images
• Dockerfile Fundamentals
  • Commands: FROM, RUN, COPY, CMD, EXPOSE
  • Build reproducible environments
• Building Custom Images
  • Python, Pandas, Spark images for data pipelines
• Image Optimization
  • Layering, caching, reducing image size
• Hands-on Lab:
  • Build a custom Python ETL image
  • Run data ingestion script inside container

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Week 3: Networking, Volumes & Docker Compose (Intermediate)

Sessions: 2 × 3–4 hours

• Docker Networking Basics
  • Bridge, Host, None networks
  • Container-to-container communication
• Persistent Storage
  • Volumes vs. bind mounts
  • Sharing and persisting data across containers
• Docker Compose Fundamentals
  • Multi-container orchestration with docker-compose.yml
  • Environment variables & secrets management
• Data Engineering Pipelines with Compose
  • Example: Kafka → Spark → PostgreSQL
  • Scaling services
• Hands-on Lab:
  • Deploy a mini pipeline using Docker Compose

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Week 4: Logging, Monitoring, Security & Private Registries (Intermediate → Advanced)

Sessions: 2 × 3–4 hours

• Container Logging
  • Log drivers, logging best practices
  • Collecting logs for ETL processes
• Monitoring Containers
  • Introduction to Prometheus and Grafana
  • Monitoring resource usage of containers
• Security Best Practices
  • Secure images, scan vulnerabilities
  • User permissions, secrets, and environment management
• Private Registries
  • Push/pull images to AWS ECR, Azure ACR, Docker Hub private
• Hands-on Lab:
  • Secure and monitor Spark + PostgreSQL container setup

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Week 5: CI/CD, Kubernetes Intro & Capstone Project (Advanced)

Sessions: 2 × 3–4 hours

• Docker in CI/CD Pipelines
  • Integrate Docker with Jenkins, GitHub Actions, Airflow
• Introduction to Kubernetes for Data Engineers
  • Pods, Deployments, Scaling containers
  • When to move from Docker Compose to Kubernetes
• Capstone Project: Containerized ETL Pipeline
  • Airflow + Spark + PostgreSQL + MinIO
  • Multi-stage deployment using Docker images
• Project Review & Presentations
  • Peer review and instructor feedback
  • Best practices recap, Q&A

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Key Learning Outcomes After 5 Weeks

1. Master Docker architecture, containers, images, and Dockerfiles.
2. Build and manage multi-container data pipelines using Docker Compose.
3. Implement persistent storage, networking, logging, and monitoring.
4. Apply container security best practices.
5. Integrate Docker with CI/CD pipelines.
6. Gain a foundational understanding of Kubernetes for scaling data workflows.
7. Deploy a real-world containerized data engineering pipeline as a capstone project.

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