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.

Here you can see Important Links:-

Cv And Cover Letter Template

Resume Creative For Job

Resume Cv Templates In Demand

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.

Here you can see Important Links:-

Sample Resume Format

Resume For Job

Best Resume Format

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

---

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

---

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

---

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

---

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

---

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.

Here you can see Important Links:-

Resume Creative For Job

Cv Format For Intern Job

Sample Resume Format

Live Online Deep Learning 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 Deep Learning (6 hrs)

Objective: Build foundational understanding of neural networks and their role in modern data science.
Topics Covered:

1. What is Deep Learning and how it differs from Machine Learning
2. Key Concepts: Neurons, Layers, Activation Functions
3. Biological vs Artificial Neural Networks
4. Deep Learning in Data Science Applications (vision, NLP, recommender systems)
5. Setting up the Environment – TensorFlow, Keras, and PyTorch basics
6. Hands-on: Build your first Neural Network using Keras

---

 Week 2: Artificial Neural Networks (ANN) (6 hrs)

Objective: Develop a strong understanding of feedforward and backpropagation algorithms.
Topics Covered:

1. Architecture of ANN: Input, Hidden, Output Layers
2. Forward Propagation and Backpropagation
3. Gradient Descent and Optimization Techniques (SGD, Adam, RMSProp)
4. Loss Functions and Evaluation Metrics
5. Overfitting & Underfitting, Regularization (Dropout, Batch Normalization)
6. Hands-on: Predicting customer churn using ANN

---

 Week 3: Convolutional Neural Networks (CNN) (6 hrs)

Objective: Learn how to process and analyze image data using CNNs.
Topics Covered:

1. Concept of Convolution, Filters, Pooling, and Feature Maps
2. CNN Architectures – LeNet, AlexNet, VGG, ResNet
3. Data Augmentation and Transfer Learning
4. Hyperparameter Tuning in CNNs
5. Real-world Applications – Image Classification, Object Detection
6. Hands-on: Build an image classifier using CNN in TensorFlow

---

 Week 4: Recurrent Neural Networks (RNN) & LSTM (6 hrs)

Objective: Master deep learning for sequential and time-series data.
Topics Covered:

1. Introduction to Sequential Data
2. RNN Architecture and Vanishing Gradient Problem
3. Long Short-Term Memory (LSTM) and GRU Networks
4. Applications – Stock Prediction, Text Generation, Sentiment Analysis
5. Sequence-to-Sequence Models
6. Hands-on: Sentiment analysis using LSTM on IMDB dataset

---

 Week 5: Advanced Architectures & NLP (6 hrs)

Objective: Explore transformers, attention mechanisms, and advanced NLP techniques.
Topics Covered:

1. Understanding Attention Mechanism
2. Transformer Architecture – Encoder & Decoder
3. Introduction to BERT, GPT Models
4. Word Embeddings: Word2Vec, GloVe, FastText
5. NLP Applications: Text Classification, Named Entity Recognition
6. Hands-on: Build a text classifier using BERT

---

 Week 6: Generative Models & Capstone Project (6 hrs)

Objective: Implement generative and hybrid models and complete an end-to-end project.
Topics Covered:

1. Autoencoders & Variational Autoencoders (VAE)
2. Generative Adversarial Networks (GANs) and their Applications
3. Deep Reinforcement Learning Overview
4. Model Deployment (Flask/Streamlit/TensorFlow Serving)
5. Capstone Project: Choose one –
   • Image Caption Generator
   • Fake News Detector
   • GAN-based Image Generator
6. Presentation & Review

---

Course Outcomes

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

• Build, train, and optimize deep learning models using TensorFlow and PyTorch
• Apply CNNs and RNNs for image, text, and sequence data
• Understand and implement transformer-based models like BERT and GPT
• Deploy deep learning models into production environments
• Complete a full deep learning project for real-world data science applications

---

Tools & Technologies Used

• Programming: Python
• Frameworks: TensorFlow, Keras, PyTorch
• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, OpenCV
• Deployment: Flask / Streamlit
• Datasets: CIFAR-10, MNIST, IMDB, Custom Dataset

Here you can see Important Links:-

Resume Creative For Job

Resume Format For Freshers

Favorite Resume For Jobs

Live Online Apache Airflow Course for Data Engineering

 

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 Airflow & Core Concepts

Duration: 8 hours (4 sessions × 2 hrs)

Topics:

1. Introduction to Workflow Orchestration (2 hrs)

• What is orchestration?
• Role of Airflow in Data Engineering
• Airflow vs Luigi vs Prefect comparison
• Airflow architecture: Scheduler, Executor, Worker, Web Server

2. Airflow Installation & Environment Setup (2 hrs)

• Installing Airflow using pip and Docker
• Understanding Airflow components
• Navigating Airflow UI (DAGs, Logs, Tasks, Graphs)

3. Understanding DAGs & Tasks (2 hrs)

• Creating a simple DAG in Python
• Operators: PythonOperator, BashOperator, DummyOperator
• Dependencies: set_upstream(), set_downstream(), >> and <<

4. Mini Project + Q&A (2 hrs)

• Build a simple ETL DAG to extract and transform CSV data
• Schedule and run through the Airflow UI

Week 2: Building & Managing Complex DAGs

Duration: 10 hours (5 sessions × 2 hrs)

Topics:

1. Advanced DAG Design (2 hrs)

• DAG parameters, default_args, retries, SLAs
• Dynamic task generation
• Branching and SubDAGs

2. Using Airflow Operators (2 hrs)

• FileSensor, EmailOperator, SimpleHttpOperator, PostgresOperator
• Working with external APIs and SQL databases

3. XComs and Data Sharing (2 hrs)

• Passing data between tasks
• Using XComs effectively in data pipelines

4. Error Handling & Task Monitoring (2 hrs)

• Handling task failures and retries
• Alerting & notifications (Slack/Email integration)

5. Mini Project + Q&A (2 hrs)

• Build a multi-stage DAG integrating API extraction + data transformation + DB loading

Week 3: Airflow with Big Data & Cloud Integration

Duration: 10 hours (5 sessions × 2 hrs)

Topics:

1. Airflow with Apache Spark (2 hrs)

• Submitting Spark jobs using Airflow
• Using SparkSubmitOperator for batch data pipelines

2. Airflow with Hadoop & HDFS (2 hrs)

• Managing data in HDFS
• Using Airflow for daily ingestion & transformation jobs

3. Airflow with AWS / GCP / Azure (2 hrs)

• AWS S3, Redshift, BigQuery, and Azure Blob Storage integrations
• Using Airflow Hooks and Connections

4. Airflow with Kafka & Streaming Data (2 hrs)

• Triggering workflows from Kafka topics
• Real-time batch pipeline simulation

5. Mini Project + Q&A (2 hrs)

• Build a batch pipeline integrating Airflow + Spark + S3

Week 4: Airflow in Production, Scaling & Capstone Project

Duration: 12 hours (6 sessions × 2 hrs)

Topics:

1. Scheduling, Triggers, and Backfills (2 hrs)

• Airflow scheduling and cron expressions
• Manual triggers and backfilling DAG runs

2. Airflow in Production Environments (2 hrs)

• Airflow Executors: Sequential, Local, Celery, Kubernetes
• Configuring Airflow for scalability and high availability

3. CI/CD and Version Control (2 hrs)

• DAG versioning using Git
• Deploying Airflow pipelines through CI/CD tools (GitHub Actions, Jenkins)

4. Monitoring, Logging & Security (2 hrs)

• Airflow Metrics, Logging, Prometheus, Grafana integration
• Authentication & Role-Based Access Control (RBAC)

5. Capstone Project Development (2 hrs)

• Design and build an end-to-end data pipeline using Airflow and Cloud Storage

6. Capstone Presentation & Feedback (2 hrs)

• Present final DAG and pipeline workflow
• Instructor feedback and best practices discussion

Capstone Project Example

Project Title: Automated Data Pipeline for E-Commerce Analytics
Goal:
Extract transactional data from APIs → Load into AWS S3 → Transform using Spark → Load into Redshift → Orchestrate with Airflow
Tech Stack: Airflow, Python, Spark, AWS S3, Redshift

Here you can see Important Links:-

Resume Creative For Job

Sample Resume For Job

`Favorite Resume For Jobs

Live Online Excel Course for Data Analytics

 

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 Excel and Data Fundamentals (6 Hours)

1. Overview of Excel and its role in data analytics
2. Navigating Excel interface — ribbons, tabs, and shortcuts
3. Understanding data types, cell references, and formatting
4. Working with formulas and basic functions (SUM, AVERAGE, COUNT, etc.)
5. Sorting, filtering, and conditional formatting for data organization
6. Introduction to data entry, data validation, and basic charts

Week 2: Data Cleaning and Preparation (6 Hours)

1. Techniques for cleaning and structuring raw data
2. Handling duplicates, blanks, and inconsistent entries
3. Text functions (LEFT, RIGHT, MID, TRIM, CONCATENATE, TEXTJOIN)
4. Date and time functions for data processing
5. Using logical functions (IF, AND, OR, IFERROR)
6. Practical exercises on preparing real-world datasets

Week 3: Data Analysis with Formulas and Functions (6 Hours)

1. Lookup and reference functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH)
2. Statistical functions (AVERAGEIF, COUNTIF, SUMIFS)
3. Mathematical and financial functions (ROUND, RANK, PMT, NPV, IRR)
4. Data summarization with dynamic formulas
5. Using named ranges and structured references
6. Automating analysis with nested formulas

Week 4: Data Visualization and Reporting (6 Hours)

1. Creating effective charts (Column, Line, Pie, Bar, Scatter, Combo)
2. Advanced chart customization (axes, labels, dynamic ranges)
3. Conditional visualizations with data bars and color scales
4. Building interactive dashboards using form controls
5. Using Sparklines and Conditional Formatting for insights
6. Best practices for designing clean analytical dashboards

Week 5: Advanced Analytics and Pivot Tools (6 Hours)

1. Introduction to PivotTables and PivotCharts
2. Grouping, filtering, and summarizing data in PivotTables
3. Using Slicers and Timelines for interactivity
4. Advanced calculations with DAX and Power Pivot
5. Introduction to Power Query for ETL (Extract, Transform, Load)
6. Automating repetitive tasks using Macros and basic VBA

Week 6: Business Intelligence and Capstone Project (6 Hours)

1. Using Power Query to merge and clean multiple datasets
2. Integrating Excel with Power BI and external data sources
3. Performing scenario and sensitivity analysis (Goal Seek, Solver, Data Tables)
4. Real-world case study: End-to-end data analytics project in Excel
5. Building an interactive dashboard with live insights
6. Final review, project presentation, and assessment

Mini Project Ideas (Week 4 Hands-on)

Learners will complete an end-to-end analytics project such as:

1. Project 1: Sales Performance Dashboard with KPIs and Trends
2. Project 2: Financial Budget vs. Actual Analysis using Power Query
3. Project 3: Customer Retention Analysis Report using PivotTables

Teaching Methodology

• Live Demonstrations of Excel features and functions
• Hands-on Exercises for every concept
• Assignments & Weekly Quizzes
• Interactive Q&A Sessions
• Final Mini Project Presentation

Final Deliverables

• Certificate of Completion
• End-to-End Excel Analytics Dashboard
• Strong proficiency in Excel for real-world data analytics tasks

Course Outcomes:

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

• Understand Excel’s data analytics capabilities.
• Perform data cleaning, transformation, and analysis using formulas and functions.
• Create charts, dashboards, and reports for decision-making.
• Apply advanced Excel features such as PivotTables, Power Query, and Power Pivot.
• Automate repetitive analytics tasks using macros.

Here you can see Important Links:-

Resume Creative For Job

Sample Resume For Job

Resume Format For Job Application

Sample Resume Format

Live Online AWS Cloud Course for Data Science

 

Duration: 3 Weeks | Total Time: 30 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 — AWS Foundations & Data Handling (10 Hrs)

Topics Covered:

• Introduction to AWS & Cloud Basics
• IAM (Identity & Access Management)
• Data Storage with Amazon S3
• ETL & Data Preparation with AWS Glue
• Serverless SQL Queries with Amazon Athena

Outcome:

• Understand AWS cloud environment & security basics
• Store, manage, and secure datasets in S3
• Build simple ETL workflows with Glue
• Query structured/unstructured data using Athena

Week 2 — Compute, Machine Learning & Visualization (10 Hrs)

Topics Covered:

• AWS EC2 setup for data science environment
• Amazon SageMaker (Notebooks, Training, Deployment)
• Model training & hyperparameter tuning
• Real-time and batch inference deployment
• Visualization & BI with Amazon QuickSight

Outcome:

• Build and manage compute environments (EC2, SageMaker)
• Train ML models using SageMaker
• Deploy models for real-time predictions
• Create dashboards and data visualizations with QuickSight

Week 3 — Advanced Tools, MLOps & Project (10 Hrs)

Topics Covered:

• Big Data Analytics with EMR (Hadoop/Spark)
• Real-time data ingestion with AWS Kinesis
• MLOps using SageMaker Pipelines
• End-to-End Data Science Project (S3 → Glue → Athena → SageMaker → QuickSight)
• Cost Optimization, Security, and Best Practices

Outcome:

• Run large-scale data processing with EMR & Spar
• Stream real-time data using Kinesis
• Automate ML workflows with MLOps pipelines
• Complete a hands-on end-to-end AWS data science project
• Apply cost-saving and security strategies in AWS

Final Outcomes of the Course

By the end of 30 hours (3 weeks), learners will be able to:

Set up AWS environments for data science securely

Store, clean, and process large datasets (batch & real-time)

Train and deploy ML models using SageMaker

Visualize insights with AWS QuickSight dashboards

Design end-to-end AWS-based data science workflows

Here you can see Important Links:-

Resume For Freshers

Resume Cv Templates In Demand

Important Cv For Job