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שתתחיל
מתחיל 4 June 2026 23:11
נגמר 4 June 2026
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ימים
00
שעות
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דקות
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שניות
43 minutes
שדרוג אופציונלי זמין
Not Specified
התקדמות בקצב שלך
Conference Talk
שדרוג אופציונלי זמין
סקירה כללית
Explore powerful data analysis workflows by combining Python and Docker. Learn to create robust, repeatable systems for small and large-scale data processing using containerization techniques.
סילבוס
- Introduction to Data Analysis with Python & Docker
- Setting Up Your Environment
- Python for Data Analysis
- Introduction to Docker
- Docker for Data Analysis
- Advanced Containerization Techniques
- Integrating Python and Docker
- Best Practices and Optimization
- Project: Building a Dockerized Data Analysis Pipeline
- Conclusion and Further Learning
Overview of data analysis workflows
Introduction to Python for data analysis
Introduction to Docker and containerization
Installing Python and essential libraries (pandas, NumPy, etc.)
Installing Docker and understanding Docker Desktop
Setting up a development environment
Data manipulation with pandas
Data visualization techniques (matplotlib, seaborn)
Applying statistical analysis using Python
Case studies and hands-on exercises
Understanding Docker architecture
Key Docker concepts: images, containers, Dockerfile
Building your first Docker container
Using Docker to create isolated data processing environments
Building Docker images for Python-based data analysis
Containerizing a Python data analysis application
Working with Docker Compose for multi-container applications
Managing data with Docker volumes
Networking and communication between containers
Developing and deploying data processing workflows in Docker
Automating data tasks with Docker
Scaling data analysis with swarm mode and orchestration tools
Optimizing Docker images for performance
Security considerations in containerized environments
Testing and debugging Python applications in Docker
Defining project goals and requirements
Designing the pipeline architecture
Implementing and testing the system
Presenting and discussing project outcomes
Recap of key learnings and skills
Resources for further study in Python and Docker
Discussion on emerging trends in data analysis and containerization
נושאים
Conference Talks