Rizki Sasri Dwitama
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Rizki Sasri Dwitama
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Data Concepts
Data Analysis
Data Analysis: Syllabus
Data Engineering
Data Engineering: Syllabus
Data Sources
Cassandra
Cassandra: Syllabus
ChromaDB
ChromaDB: Syllabus
Milvus
Milvus: Syllabus
PostgreSQL
PostgreSQL: Syllabus
PostgeSQL: Introduction
PostgreSQL: Data Types and Table Management
PostgreSQL: Querying and Data Manipulation
PostgreSQL: Performance Optimization and Indexing
PostgreSQL: Transactions and Concurrency Control
PostgreSQL: Stored Procedures, Functions, and Triggers
PostgreSQL: Security and User Management
PostgreSQL: Backup, Recovery, and Replication in PostgreSQL
PostgreSQL: Hands-On Projects
PostgreSQL Programming
PL/pgSQL: Syllabus
PostgreSQL Client
PSQL: Syllabus
MongoDB
MongoDB: Syllabus
Data Processing
Airflow
Airflow: Syllabus
Spark
Spark: Syllabus
Kafka
Kafka - Syllabus
DBT
DBT: Syllabus
Greate Expectation
GX: Syllabus
Pandas
Pandas: Syllabus
Pandas: Introduction
Pandas: Working with Data Structures
Pandas: Data Cleaning and Preprocessing
Pandas: Data Transformation and Manipulation
Pandas: Time Series Analysis
Pandas: Data Visualization
Pandas: Performance Optimization
Pandas: Hands-on Projects
Data Warehouse
Delta Lake
Delta Lake: Syllabus
Iceberg
Iceberg: Syllabus
Minio
Minio: Syllabus
Trino
Trino: Syllabus
Nessie
Nessie: Syllabus
Data Governance
Atlas
Atlas: Syllabus
Keycloak
Keycloak: Syllabus
Data Monitoring
Grafana
Grafana: Syllabus
Open Telemetry
OpenTelemetry: Syllabus
Promotheus
Promotheus: Syllabus
Artificial Intelligence
Machine Learning
ML: Syllabus
ML: Introduction to Machine Learning
ML: Data Preprocessing and Feature Engineering
ML: Supervised Learning - Regression Models
ML: Supervised Learning - Classification Models
ML: Unsupervised Learning
ML: Neural Networks and Deep Learning
ML: Convolutional Neural Networks (CNNs) for Image Processing
ML: Natural Language Processing (NLP) and Transformers
ML: Reinforcement Learning
ML: Model Optimization and Hyperparameter Tuning
ML: Model Deployment and MLOps
Module 12: Time Series Forecasting
ML: Ethical AI and Responsible Machine Learning
ML: Hands-On Projects
ML Projects: Customer Churn Prediction
ML Projects: Image Classification with CNNs
ML Preojects: Sentiment Analysis with NLP
Sentiment Analysis with NLP
Large Language Models (LLMs)
LLMs: Syllabus
Deep Learning
Deep Learning: Syllabus
Infrastructure
Podman
Podman: Introduction
Podman: Fundamentals
Podman: Advanced Concepts
Podman: Building & Managing Container Images
Podman: Security Best Practices
Podman: Kubernetes Integration
Podman: Hands-On Projects
Kubernetes
K3S: Introduction to Kubernetes
K3S: Core Kubernetes Concepts
K3S: Deploying Applications
K3S: Storage Management
K3S: Networking and Service Discovery
K3S: Implement Security Best Practices
K3S: Monitoring & Logging
K3S: High Availability & Scaling
K3S: GitOps & CI/CD
K3S: Serverless & Edge Computing
K3S: Hands-On Projects
Jenkins
Jenkins: Introduction
Jenkins: Fundamentals
Jenkins: Pipelines and Jobs
Jenkins: Advanced Techniques
Jenkins: Administration and Security
Jenkins: Monitoring, Logging, and Maintenance
Jenkins: Advanced Concepts
Jenkins: Practical Implementation
Nginx
Nginx: Introduction
Nginx: Core Concepts and Configuration
Nginx: Advanced Configuration
Nginx: Reverse Proxy and Load Balancing
Nginx: Advanced Techniques
Nginx: Kubernetes Environments
Nginx: Troubleshooting and Maintenance
Nginx: Hands-On Projects
ArgoCD
ArgoCD: Introduction to GitOps
ArgoCD: Fundamentals
ArgoCD: Application Management
ArgoCD: Advanced Usage
ArgoCD: Security and Authentication
ArgoCD: Monitoring, Logging, and Troubleshooting
ArgoCD: Hands-On Projects
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Datascience
Data Concepts
Data Analysis
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Sentiment Analysis with NLP