Build Expertise in Python, Big Data, and Machine Learning with Real-World Applications and Scalable Solutions
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- Other IT & Software
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Objectives
- Setup a fully functional environment tailored for success from scratch.
- Learn Python fundamentals that empower you to write dynamic, user-driven programs with ease.
- Handle runtime exceptions gracefully, keeping your programs robust and user-friendly.
- Use print statements and Python’s built-in debugger to identify and resolve issues efficiently.
- Implement a systematic approach to monitor program behavior, ensuring maintainability and transparency.
- Reshape data using Melt and Pivot functions for tidy and wide formats.
- Manage multi-index and hierarchical data for complex datasets.
- Optimize performance with vectorized operations and Pandas’ internal evaluation engine.
- Parse dates and resample data for trend analysis.
- Analyze temporal patterns in fields like finance, climate
- Leveraging Eval and Query functions for faster computations
- Implementing vectorized operations to efficiently process large datasets.
- Array creation with functions like zeros, ones, and random.
- Mastery of slicing, indexing, and Boolean filtering for precise data handling
- Broadcasting for Accelerated Calculations
- Simplify calculations on arrays with differing shapes
- Perform efficient element-wise operations.
- Simplify calculations on arrays with differing shapes.
- Matrix multiplication and eigenvalue computation.
- Practical applications in physics, optimization, and data science.
- Transform NumPy arrays into Pandas DataFrames for structured data analysis.
- Leverage NumPy’s numerical power for machine learning pipelines in libraries like Scikit-learn.
- Line Plots: Showcase trends and relationships in continuous data.
- Customization Techniques: Add titles, labels, gridlines, and legends to make your plots informative and visually appealing.
- Highlighting Key Data Points: Use scatter points and annotations to emphasize critical insights
- Scatter Plots: Visualize relationships between variables with custom hues and markers.
- Pair Plots: Explore pairwise correlations and distributions across multiple dimensions.
- Violin Plots: Compare data distributions across categories with elegance and precision.
- Custom Themes and Styles: Apply Seaborn’s themes, palettes, and annotations to create polished, professional-quality visuals.
- Divide datasets into subsets based on categorical variables.
- Use histograms and kernel density estimates (KDE) to uncover distributions and trends.
- Customize grid layouts for clarity and impact.
- Set up and configure a Spark environment from scratch.
- Work with Resilient Distributed Datasets (RDDs) and DataFrames for efficient data processing.
- Build data pipelines for Extract, Transform, Load (ETL) tasks.
- Process real-time streaming data using Kafka.
- Optimize Spark jobs for memory usage, partitioning, and execution.
- Monitor and troubleshoot Spark performance with its web UI.
- Configure Jupyter Notebook to work with PySpark.
- Create and manipulate Spark DataFrames within notebooks.
- Run transformations, actions, and data queries interactively.
- Handle errors and troubleshoot efficiently in a Pythonic environment.
- Select, filter, and sort data using Spark DataFrames.
- Add computed columns and perform aggregations.
- Group and summarize data with ease.
- Import and export data to and from CSV files seamlessly.
- Set up Airflow on a Windows Subsystem for Linux (WSL).
- Build and manage production-grade workflows using Docker containers.
- Integrate Airflow with Jupyter Notebooks for exploratory-to-production transitions
- Design scalable, automated data pipelines with industry best practices
- Prototype and visualize data workflows in Jupyter.
- Automate pipelines for machine learning, ETL, and real-time processing.
- Leverage cross-platform development skills to excel in diverse technical environments.
- Bridging Exploratory Programming and Production-Grade Automation
- Combining Python Tools for Real-World Financial Challenges
- Containerizing Applications for Workflow Orchestration
- Benefits of Using Docker for Reproducibility and Scalability
- Organizing Files and Directories for Clean Workflow Design
- Key Folders: Dags, Logs, Plugins, and Notebooks
- Isolating Project Dependencies with venv
- Activating and Managing Virtual Environments
- Avoiding Conflicts with Project-Specific Dependencies
- Ensuring Required Packages: Airflow, Pandas, Papermill, and More
- Defining Multi-Service Environments in a Single File
- Overview of Core Components and Their Configuration
- The Role of the Airflow Web Server and Scheduler
- Managing Metadata with PostgreSQL
- Jupyter Notebook as an Interactive Development Playground
- Verifying Docker and Docker Compose Installations
- Troubleshooting Installation Issues
- Specifying Python Libraries in requirements.txt
- Managing Dependencies for Consistency Across Environments
- Starting Airflow for the First Time
- Setting Up Airflow's Database and Initial Configuration
- Designing ETL Pipelines for Stock Market Analysis
- Leveraging Airflow to Automate Data Processing
- The Anatomy of a Directed Acyclic Graph (DAG)
- Structuring Workflows with Airflow Operators
- Reusing Task-Level Settings for Simplified DAG Configuration
- Defining Retries, Email Alerts, and Dependencies
- Creating Workflows for Extracting, Transforming, and Loading Data
- Adding Customizable Parameters for Flexibility
- Encapsulating Logic in Python Task Functions
- Reusability and Maintainability with Modular Design
- Linking Tasks with Upstream and Downstream Dependencies
- Enforcing Workflow Order and Preventing Errors
- Using Papermill to Parameterize and Automate Notebooks
- Building Modular, Reusable Notebook Workflows
- Exploring the Dashboard and Monitoring Task Progress
- Enabling, Triggering, and Managing DAGs
- Viewing Logs and Identifying Bottlenecks
- Debugging Failed or Skipped Tasks
- Understanding Log Outputs for Each Task
- Troubleshooting Notebook Execution Errors
- Manually Starting Workflows from the Airflow Web UI
- Automating DAG Runs with Schedules
- Automating the Stock Market Analysis Workflow
- Converting Raw Data into Actionable Insights
- Using airflow dags list import_errors for Diagnostics
- Addressing Common Issues with DAG Parsing
- Designing Scalable Data Pipelines for Market Analysis
- Enhancing Decision-Making with Automated Workflows
- Merging Data Outputs into Professional PDF Reports
- Visualizing Key Financial Metrics for Stakeholders
- Streamlining Daily Updates with Workflow Automation
- Customizing Insights for Different Investment Profiles
- Leveraging Airflow's Python Operator for Task Generation
- Automating Workflows Based on Dynamic Input Files
- Running Multiple Tasks Concurrently to Save Time
- Configuring Parallelism to Optimize Resource Utilization
- Generating Tasks Dynamically for Scalable Workflows
- Processing Financial Data with LSTM Models
- Exploiting Airflow's Parallelism Capabilities
- Best Practices for Dynamic Workflow Design
- Migrating from Sequential to Parallel Task Execution
- Reducing Execution Time with Dynamic DAG Patterns
- Designing a DAG That Dynamically Adapts to Input Data
- Scaling Your Pipeline to Handle Real-World Data Volumes
- Ensuring Logical Flow with Upstream and Downstream Tasks
- Debugging Tips for Dynamic Workflows
- Applying Airflow Skills to Professional Use Cases
- Building Scalable and Robust Automation Pipelines
- Explore how Long Short-Term Memory (LSTM) models handle sequential data for accurate time series forecasting.
- Understand the role of gates (input, forget, and output) in managing long-term dependencies.
- Learn how to normalize time-series data for model stability and improved performance.
- Discover sequence generation techniques to structure data for LSTM training and prediction.
- Construct LSTM layers to process sequential patterns and distill insights.
- Integrate dropout layers and dense output layers for robust predictions.
- Train the LSTM model with epoch-based optimization and batch processing.
- Classify predictions into actionable signals (Buy, Sell, Hold) using dynamic thresholds.
- Reserve validation data to ensure the model generalizes effectively.
- Quantify model confidence with normalized scoring for decision-making clarity.
- Translate normalized predictions back to real-world scales for practical application.
- Create data-driven strategies for stock market analysis and beyond.
- Dynamically generate time series analysis tasks for multiple tickers or datasets.
- Orchestrate LSTM-based predictions within Airflow's DAGs for automated time-series analysis.
- Scale workflows efficiently with Airflow's parallel task execution.
- Manage dependencies to ensure seamless execution from data preparation to reporting.
- Automate forecasting pipelines for hundreds of time series datasets using LSTMs.
- Leverage Airflow to orchestrate scalable, distributed predictions across multiple resources.
- Fuse advanced machine learning techniques with efficient pipeline design for real-world applications.
- Prepare pipelines for production environments, delivering insights at scale.
Pre Requisites
- No programming experience needed, you will learn everything you need to know
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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