Google Cloud Platform (GCP) Specialisation Career Path
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About This Course
What You'll Learn
Core GCP skills You Will Learn
- Understand GCP Cloud Computing principles, services, and architecture to build robust and scalable cloud solutions.
- Gain hands-on experience with essential GCP services such as Compute Engine, Cloud Storage, and Cloud SQL, and learn to set up and manage GCP accounts securely.
- Learn to design and implement scalable, resilient cloud infrastructure and networking solutions using Virtual Private Clouds (VPCs), subnets, and load balancers.
- Master GCP's storage and database services like Cloud Storage, Cloud Spanner, and Bigtable to efficiently manage and store data in the cloud.
- Deploy and scale applications using GCP's compute services and develop event-driven, serverless applications with Cloud Functions.
- Implement robust security measures, ensure regulatory compliance, and optimize performance and costs through proactive monitoring and resource management.
Essential tools and platforms:
- Google Kubernetes Engine (GKE): Managed Kubernetes service for deploying, managing, and scaling containerized applications using Kubernetes.
- BigQuery: Fully managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google's infrastructure..
- Cloud Pub/Sub: Messaging service that allows you to send and receive messages between independent applications, providing real-time event data ingestion and delivery.
- Cloud Functions: Serverless compute service that allows you to run code in response to events without managing any server infrastructure.
- TensorFlow on GCP: Comprehensive tools and managed services to build, train, and deploy machine learning models at scale using TensorFlow on GCP. This includes AI Platform, which provides tools to take ML projects from ideation to production.
Essential communication and business skills relevant to the cloud computing domain:
- Stakeholder Communication: Clearly explain cloud computing solutions, benefits, and potential challenges to stakeholders, ensuring alignment with business goals and IT strategies.
- Technical Documentation: Create comprehensive and accessible documentation for cloud architectures, services, and implementation processes to facilitate understanding and collaboration among team members.
- Project Management: Plan, coordinate, and oversee cloud computing projects efficiently, ensuring timely delivery, budget adherence, and alignment with organizational objectives.
- Cost-Benefit Analysis: Present detailed analyses of cloud service costs and benefits to non-technical stakeholders, aiding in informed decision-making and budget planning.
- Cross-Functional Collaboration: Work effectively with IT, security, and business teams to integrate cloud solutions seamlessly into the company's operations and infrastructure.
Problem Solving and Design Thinking Skills You Will Learn
- Create resilient and secure cloud solutions using integrated GCP services.
- Develop strategies for auto-scaling, load balancing, and disaster recovery.
- Configure virtual networks and VPCs for optimal traffic flow and enhanced security.
- Select and implement appropriate GCP storage and database solutions for specific data needs.
- Analyze and manage resource usage to forecast costs and identify savings.
- Enforce access controls, encryption, and data protection measures.
- Streamline development and deployment with CI/CD pipelines and Infrastructure as Code.
- Utilize AI/ML, IoT, and edge computing to drive innovation.
Curriculum Designed For Career Success
Module 1: Introduction to GCP Cloud Computing
3 Lectures
Discover the power of Python for analyzing data and SQL for managing databases. Get ready to start your own data analysis journey with Python's versatile tools and SQL's database querying capabilities.Gain a foundational understanding of GCP Cloud Computing and its applications across various industries. Explore how businesses like Spotify and Snapchat leverage GCP for scalable, reliable, and innovative cloud solutions.
- 1. 1 Overview of GCP services and architecture:
- Understand the comprehensive suite of GCP services and their integration for building robust cloud architectures. For instance, a media streaming platform can leverage GCP services like Compute Engine for hosting, BigQuery for analytics, and Cloud Storage for storing media files.
- 1.2 Understanding the cloud computing paradigm:
- Learn about cloud computing principles such as elasticity, on-demand resource allocation, and pay-as-you-go pricing. A startup can rapidly scale its infrastructure on GCP to accommodate sudden spikes in user traffic without upfront investment in hardware.
- 1.3
Benefits and use cases of GCP in various industries:- Explore how GCP is utilized across different sectors such as healthcare, finance, and gaming. An e-commerce retailer can use GCP's AI/ML capabilities to analyze customer behavior and provide personalized recommendations.
Module 2: GCP Fundamentals and Core Services
3 Lectures
Learn the core GCP services essential for setting up a basic cloud environment. Understand how businesses use services like Compute Engine for virtual machines, Cloud Storage for object storage, and Cloud SQL for managed databases to support their operations.
- 1.1 Introduction to core GCP services (Compute Engine, Cloud Storage, Cloud SQL, etc.):
- Get familiar with essential GCP services and their functionalities. A software development team can use Compute Engine instances to run its web applications, Cloud Storage for storing product images, and Cloud SQL for managing customer data.
- 1.2 Setting up GCP accounts and managing security:
- Learn to create GCP accounts and implement security best practices using Identity and Access Management (IAM). An enterprise can use IAM roles and permissions to enforce data access restrictions and comply with data sovereignty regulations.
- 1.3 Hands-on experience with basic GCP functionalities:
- Gain practical skills in provisioning GCP resources and configuring services through guided exercises and labs. An online marketplace can use GCP's intuitive console and command-line interface to deploy and manage their applications on the cloud.
Module 3: Cloud Infrastructure and Networking
3 Lectures
Design scalable and resilient cloud infrastructure on GCP to support modern applications and workloads. Learn about networking concepts such as Virtual Private Clouds (VPCs), subnets, and load balancing to ensure secure and efficient communication between services.
- 1.1 Designing and implementing cloud infrastructure:
- Plan and deploy cloud environments on GCP using best practices for scalability and reliability. A global e-commerce platform can use GCP's global load balancers and multi-region deployments to deliver low-latency shopping experiences to customers worldwide.
- 1.2 Networking concepts in GCP (VPC, Subnets, Load Balancers):
- Understand how networking works in GCP and configure virtual networks to isolate workloads and control traffic flow. A financial services company can use VPC peering to securely connect on-premises data centers with GCP resources for hybrid cloud deployments.
- 1.3 Optimizing network performance and security:
- Implement network security policies and monitoring solutions to protect against cyber threats and ensure compliance with industry regulations. An SaaS provider can use GCP's firewall rules and network monitoring tools to detect and mitigate DDoS attacks targeting their cloud infrastructure
Module 4: Storage and Database Services
3 Lectures
Efficiently manage and store data in the cloud with GCP's storage and database solutions. Learn how to leverage Cloud Storage for object storage, Cloud Spanner for globally distributed databases, and Bigtable for real-time analytics at scale.
- 1.1 Understanding GCP storage options (Cloud Storage, Cloud Spanner, Bigtable, etc.):
- Explore GCP's storage offerings and choose the right solution for your data storage and processing needs. A gaming company can use Cloud Spanner to build a globally consistent leaderboard system that scales seamlessly as the player base grows.
- 1.2 Introduction to GCP database services (Cloud SQL, Firestore, BigQuery):
- Learn about GCP's managed database services and their use cases for transactional, document-oriented, and analytical workloads. A media streaming platform can use BigQuery for analyzing viewer engagement metrics and optimizing content recommendations in real time.
- 1.3 Best practices for data storage and management in GCP
- Implement data lifecycle management policies and backup strategies to ensure data availability and durability. An e-commerce retailer can use Cloud Storage's object versioning and lifecycle policies to store and manage product images, videos, and user-generated content with high availability and reliability.
Module 5: Compute Services and Serverless Computing
3 Lectures
Deploy and scale applications on GCP without managing underlying infrastructure. Learn about GCP's compute services such as Compute Engine for virtual machines, App Engine for platform as a service (PaaS), and Cloud Functions for serverless computing.
- 1.1 Exploring GCP compute services (Compute Engine, App Engine, Cloud Functions):
- Understand GCP's compute offerings and choose the right compute option based on your application requirements. A software development team can use App Engine's managed environment to deploy and scale applications without worrying about infrastructure management.
- 1.2 Serverless computing with GCP Cloud Functions:
- Develop event-driven, serverless applications that automatically scale with demand and only incur costs when executed. For example, a fintech startup can use Cloud Functions to process financial transactions in real time, ensuring high availability and low latency for their users.
- 1.3 Building and deploying serverless applications:
- Learn best practices for designing, developing, and deploying serverless applications on GCP. An IoT company can use Cloud Functions to process sensor data from connected devices, triggering alerts and notifications based on predefined business rules.
Module 6: Scalability and High Availability
3 Lectures
Ensure applications can scale and remain available under heavy load or in the event of failures. Learn about GCP's auto-scaling and load balancing capabilities to dynamically adjust resources based on traffic patterns and distribute workloads across multiple instances.
- 1.1 Scaling applications on GCP using auto-scaling and load balancing:
- Configure auto-scaling policies and load balancers to handle traffic spikes and distribute requests evenly across backend instances. For instance, a ride-sharing platform can use auto-scaling to provision additional compute resources during peak hours and load balancers to distribute ride requests to nearby drivers efficiently.
- 1.2 Achieving high availability with GCP services (Cloud Load Balancing, Cloud CDN):
- Design fault-tolerant architectures that minimize downtime and ensure seamless user experiences. A healthcare provider can use Cloud Load Balancing to distribute incoming patient inquiries to redundant backend servers, ensuring continuous availability of their online appointment scheduling system.
- 1.3 Disaster recovery and backup strategies in GCP
- Implement data replication and backup solutions to protect against data loss and minimize recovery times in the event of disasters. A financial institution can use GCP's cross-region replication and automated backups to maintain data integrity and compliance with regulatory requirements.
- Implement data replication and backup solutions to protect against data loss and minimize recovery times in the event of disasters. A financial institution can use GCP's cross-region replication and automated backups to maintain data integrity and compliance with regulatory requirements.
Module 7: Security and Compliance
3 Lectures
Implement robust security measures to protect sensitive data and ensure compliance with regulatory standards. Learn about GCP's identity and access management (IAM) policies, encryption mechanisms, and auditing tools to maintain a secure cloud environment.
- 1.1 Securing GCP resources with IAM, Security Groups, and VPC Service Controls:
- Define granular access controls and network policies to restrict resource access and prevent unauthorized actions. A government agency can use IAM roles and permissions to enforce data access restrictions and comply with data sovereignty regulations.
- 1.2 Compliance and governance in GCP:
- Implement security and compliance controls to meet industry-specific requirements and regulatory standards. A financial services firm can use GCP's compliance certifications and audit logs to demonstrate adherence to industry regulations such as PCI DSS and GDPR.
- 1.3 Implementing encryption and data protection measures:
- Encrypt data at rest and in transit to protect against unauthorized access and data breaches. An e-commerce retailer can use GCP's encryption features to safeguard customer payment information and ensure PCI compliance during online transactions.
Module 8: Monitoring and Optimization
3 Lectures
Maintain performance and optimize costs through proactive monitoring and resource management. Learn about GCP's monitoring tools, performance optimization techniques, and cost management strategies to maximize the efficiency of cloud deployments.
- 1.1 Monitoring GCP resources with Stackdriver Monitoring and Logging:
- Monitor application performance and track resource usage metrics in real time to identify performance bottlenecks and troubleshoot issues proactively. An online marketplace can use Stackdriver Monitoring to monitor website latency and user traffic patterns, ensuring optimal user experiences during peak shopping seasons.
- 1.2 Performance optimization techniques:
- Optimize application performance by fine-tuning resource configurations, optimizing code efficiency, and implementing caching strategies. A content delivery network (CDN) provider can use caching mechanisms and edge caching with Cloud CDN to reduce latency and improve content delivery speeds for end users.
- 1.3 Cost management and billing in GCP:
- Analyze resource usage patterns, forecast future costs, and implement cost-saving measures to optimize cloud spending. A software development team can use GCP's cost management tools to identify idle resources, right-size instances, and leverage sustained use discounts to reduce overall infrastructure costs
Case Studies
Industry Case Studies You Will Work On
Risk Assessment with Deep Learning
Predict the likelihood of credit default for loan applicants, helping risk analysts and credit officers improve loan approval processes and reduce default rates.
Skills learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Customer Behavior Analysis
Analyze customer behavior patterns to predict future actions, such as account closure or product churn, enabling targeted strategies for customer retention.
Skills learned:
Sequential Data Processing
Time Series Analysis
Predictive Modeling
Fraud Detection Using Deep Learning
Detect fraudulent transactions in real time by analyzing transaction data patterns, improving fraud detection capabilities and reducing financial losses.
Skills learned:
Anomaly Detection Techniques
Feature Extraction
Model Evaluation
Customer Sentiment Analysis
Analyze customer sentiments from feedback data to gauge satisfaction levels, helping teams identify areas for improvement in banking services.
Skills learned:
Natural Language Processing (NLP)
Sentiment Analysis Techniques
Chatbot Development for Customer Service
Develop AI-powered chatbots to handle customer inquiries and provide instant assistance, improving customer experience and service efficiency.
Skills learned:
Natural Language Processing (NLP)
Conversational AI
Model Deployment
Investment Portfolio Optimization
Optimize investment portfolios by predicting asset performance and rebalancing strategies, helping wealth managers enhance returns and manage risks effectively.
Skills learned:
Time Series Analysis
Predictive Modeling
Portfolio Optimization Techniques
Loan Approval Automation
Develop an automated system to assess loan applications, reducing manual intervention and accelerating loan processing for improved efficiency.pre
Skills learned:
Predictive Modeling
Risk Assessment Techniques
Model Deployment
Predictive Pricing Models
Develop predictive models to forecast pricing trends and optimize strategies, enabling pricing teams to align product pricing with market dynamics.
Skills learned:
Time Series Analysis
Predictive Modeling
Pricing Analytics
Predictive Lending Models
Develop personalized lending models to offer tailored loan products, helping credit departments improve customer satisfaction and portfolio performance.
Skills learned:
Predictive Modeling
Customer Segmentation
Personalized Recommendation Systems
Network Intrusion Detection
Detect and prevent cyberattacks by analyzing network traffic patterns, strengthening cybersecurity measures and protecting sensitive banking data.
Skills learned:
Intrusion Detection Techniques
Anomaly Detection
Cybersecurity Fundamentals
Advanced Fraud Detection Techniques in Banking
Learn the skills and knowledge to detect fraudulent activities in credit card transactions, ensuring financial security and reducing losses from fraud.
Skills learned:
Data Preprocessing
Feature Engineering
Model Evaluation
Anomaly Detection Techniques
Customer Segmentation Strategies
Learn how to segment customers based on their behavior and demographics to optimize marketing campaigns for targeted engagement and retention.
Skills learned:
Data Visualization
Clustering Algorithms
Customer Profiling techniques
Operational Efficiency Optimization
Develop tools and techniques to forecast call volumes in customer service centers, improving resource allocation and enhancing service efficiency.
Skills learned:
Time Series Analysis
Model Evaluation
Operational Optimization Techniques
Sales Prediction and Optimization
Learn how to predict sales performance and optimize strategies for products and services to drive revenue growth and customer acquisition.
Skills learned:
Predictive Modeling
Sales forecasting
Model Interpretation Techniques
Real-Time Fraud Detection
Equip yourself with the skills to detect fraudulent activities in real-time transactions, safeguarding organizations from financial losses.
Skills learned:
Data Preprocessing
Pattern Recognition
Anomaly Detection Techniques
Predictive Modeling for Risk Assessment
Predict default probabilities to assess risk and make informed decisions, helping organizations manage portfolios more effectively.
Skills learned:
Feature Engineering
Risk Assessment
Model Evaluation Techniques
Predictive Analytics for Customer Lifetime Value Estimation
Learn how to estimate the lifetime value of customers, enabling personalized marketing strategies and enhancing customer experiences.
Skills learned:
Customer Segmentation
Predictive Modeling
Customer Lifetime value calculation techniques
Personalized Cross-Selling Recommendations
Gain the skills to build recommendation systems for cross-selling products and services based on customer behavior and preferences.
Skills learned:
Collaborative Filtering
Recommendation Algorithms
Customer Profiling techniques
Machine Learning for Risk Assessment
Learn the tools to assess risks in decision-making processes, enabling better strategies and management for large-scale operations.
Skills learned:
Feature Selection
Risk Modeling
Credit Scoring Techniques
Automated CI/CD Pipeline for ML Models
Develop an automated CI/CD pipeline to streamline the deployment of machine learning models, ensuring quick and reliable integration into production environments.
Skills learned:
Pipeline Design
Automated Testing
Continuous Deployment
Real-Time Model Monitoring and Drift Detection
Set up real-time monitoring systems to track ML model performance and detect model drift, ensuring accuracy and reliability in changing environments.
Skills learned:
Performance Monitoring
Drift Detection
Metric Logging
Scalable Model Deployment with Docker and Kubernetes
Learn to containerize ML models using Docker and deploy them at scale with Kubernetes for reliable, efficient, and reproducible production environments.
Skills learned:
Containerization
Orchestration
Scalable Deployment
Data Versioning and Governance Framework
Design a robust data versioning and governance framework to ensure reproducibility, data integrity, and compliance with industry regulations.
Skills learned:
Data Version Control
Compliance
Governance Frameworks
Hyperparameter Tuning for Model Optimization
Implement hyperparameter tuning techniques to improve the performance of ML models, ensuring they meet production requirements effectively.
Skills learned:
Hyperparameter Tuning
Experiment Tracking
Model Optimization
End-to-End Workflow with TensorFlow Extended (TFX)
Build an end-to-end ML pipeline using TensorFlow Extended (TFX), including data ingestion, preprocessing, model training, and deployment.
Skills learned:
Pipeline Construction
Data Preprocessing
Model Deployment
Implementing Monitoring for Ethical AI Compliance
Design monitoring systems to ensure ML models align with ethical AI practices, addressing issues such as bias detection and fairness.
Skills learned:
Bias Detection
Fairness Metrics
Ethical AI Monitoring
Multi-Model Deployment and Load Balancing
Deploy multiple machine learning models in production and implement load balancing strategies to ensure efficient resource utilization and response times.
Skills learned:
Multi-Model Serving
Load Balancing
Resource Optimization
Image Enhancement and Filtering
Learn to enhance image quality using basic image processing techniques like blurring, sharpening, and edge detection. This foundational skill is crucial for preparing images for further analysis.
Skills learned:
Image filtering
edge detection
blurring
sharpening
Basic Object Detection in Images
Understand how to detect objects within images using bounding boxes and simple detection algorithms. Lay the groundwork for developing systems that can identify objects in various scenarios.
Skills learned:
Object detection
bounding box creation
using detection algorithms
Image Classification of Everyday Objects
Train a simple CNN to classify images into categories like animals, vehicles, or everyday items. This project introduces you to deep learning concepts and how they’re applied in image classification.
Skills learned:
Image classification
CNN basics
training deep learning models
Face Detection in Photos
Learn to detect faces in photos using algorithms like Haar cascades. This hands-on project provides a foundation in object detection and recognition systems.
Skills learned:
Face detection
Feature recognition
Algorithm implementation
Panorama Stitching
Combine multiple images to create seamless panoramic views using keypoint detection, feature matching, and image alignment techniques.
Skills learned:
Keypoint detection
Feature matching
Image stitching
Tracking Moving Objects in Videos
Track objects like moving balls or people in a video sequence using basic tracking algorithms. This project introduces the essentials of object tracking and algorithm implementation.
Skills learned:
Object tracking
Video analysis
Tracking algorithm implementation
Color-Based Image Segmentation
Segment images into meaningful regions by identifying specific colors and grouping similar pixels. Learn how segmentation techniques are used in organizing image content.
Skills learned:
Image segmentation
Color detection
Pixel classification
Edge Detection for Shape Identification
Detect edges in images to identify shapes and structures. This project focuses on preprocessing techniques that lay the foundation for more advanced computer vision tasks.
Skills learned:
edge detection
shape identification
Preprocessing techniques
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Growth in demand for Data Scientists in the next 5 years
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BIGGEST GROWING INDUSTRY
$349.6 Billion
Amount industry is set to grow by 2030
HIGH ENTRY- LEVEL SALARY
₹8-14 LPA+
Current average CTC for entry-level Data Scientists in India.
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Access to curated companies & jobs
Frequently Asked Questions
What technologies will I learn in the GCP Specialization Bootcamp?
You’ll get hands-on experience with essential Google Cloud Platform (GCP) services, including Compute Engine for cloud computing, App Engine for application deployment, Cloud Storage for data management, and BigQuery for data analysis. These services form the core of cloud-based solutions.
Do I need prior experience to join the GCP Specialization Bootcamp?
No, this bootcamp is designed for beginners. You don’t need any prior cloud computing experience, as the course starts from the basics and gradually progresses to more advanced topics, making it accessible to everyone.
How long is the bootcamp, and can I learn at my own pace?
The bootcamp spans 4 months, with flexible learning options. You can learn at your own pace through self-paced modules or attend live classes with industry experts. The course requires about 15–20 hours per week, making it suitable for working professionals.
What will I learn during the bootcamp?
You’ll learn how to design, deploy, and manage cloud solutions using GCP. Key topics include cloud infrastructure, networking, storage, compute services, security, and scalability. By the end of the bootcamp, you’ll be prepared to take GCP certification exams and apply your skills to real-world cloud projects.
Will I have access to the course materials after completing the bootcamp?
Yes, you’ll have lifetime access to all course materials, including video tutorials, recorded live sessions, and project files. This ensures you can revisit and practise the content anytime you need, even after the course ends.
What kind of career support will I get during and after the bootcamp
You’ll receive personalised career support, including 1:1 mentorship, mock interviews with cloud computing professionals, and resume-building workshops. We’ll also connect you with our network of partner companies, helping you find job opportunities in the cloud computing field.
What makes this GCP bootcamp unique?
This bootcamp offers a combination of real-world projects, industry-tested problem-solving frameworks, and personalised 1:1 mentorship. You’ll also benefit from flexible learning options, live expert support, and a 7-day money-back guarantee, giving you the confidence to explore the program risk-free.
What is the cost of the bootcamp, and are there payment options?
The bootcamp costs ₹75,000, and we offer easy EMI payment options to make it more accessible. Plus, with our 7-day no-questions-asked money-back guarantee, you can try the course without any risk.
What types of projects will I work on?
You’ll work on practical, real-world projects such as setting up cloud infrastructure, managing cloud resources, and optimising performance. These projects will help you build a strong portfolio and apply your knowledge in actual business scenarios.
How will this bootcamp help me start a career in cloud computing?
By the end of the bootcamp, you’ll have a strong portfolio showcasing your GCP skills and hands-on experience. With our career support, including resume-building, mock interviews, and access to job opportunities, you’ll be well-prepared to launch a successful career in cloud computing.
I don’t have a tech background; can I still take this course?
Yes! We start with the basics of GCP, making it accessible to learners without prior cloud or tech experience.
What type of job support do you provide after completing the course?
We offer comprehensive placement assistance, including resume building, mock interviews, and leveraging our network of industry partners.
How do you prepare students for the job market?
We equip students with industry-relevant skills, help craft winning resumes, and provide mock interview practice with experienced mentors.
Do you guarantee a job after completing the course?
While we do not guarantee a job, we provide extensive support to help you become highly competitive in the job market.
Can you help me find a job in any specific industry?
Our job assistance is generalized; we prepare you for a variety of roles in the data science field rather than focusing on specific industries.
What is the cost of the Data Science bootcamp?
The cost varies depending on the program. Our full bootcamp is priced between ₹50,000 and ₹75,000, depending on whether you choose self-paced or live instruction.
Are scholarships available for the courses?
Yes, we offer scholarships that can cover up to 70% of the tuition fees, making our courses more accessible to a wider range of students.
What is included in the course fee?
The fee includes access to all course materials, live coding sessions, AI-based learning platform, case studies, capstone projects, and mentorship from industry experts.
What payment options are available for the course fees?
We offer flexible payment options, including easy EMIs, and you can cancel anytime in the first 7 days for a full refund.
Is financial aid or other support available aside from scholarships?
While our primary financial support is through scholarships, our enrollment advisors can also assist you with payment plans and financing options to help manage the cost of your education.
Ready to become a Data Scientist that industry loves to hire? Apply Now.
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