Gain foundational knowledge of Data Science including:

• What is Data Science and its business value
• Types of data analysis and real-world use cases
• Overview of popular Data Science tools
• How Data Science fits into digital transformation

Learn the basics of Data Science and start your first project:

• Installing and setting up Python / Jupyter Notebook
• Introduction to the Data Science environment
• Creating your first data analysis project
• Using libraries like Pandas and NumPy for simple tasks

Dive into data preparation techniques for accurate analysis:

• Understanding and handling missing values
• Using placeholders, filters, and transformations
• Exploring data with Pandas profiling and visualization tools
• Best practices in data preprocessing for reliable models

Learn how to process and manipulate data in Data Science:

• Understanding variables, data types, and data structures
• Working with strings, lists, and dictionaries
• Performing operations with Pandas DataFrames and NumPy arrays
• Cleaning, filtering, and transforming data efficiently

This lesson covers all key aspects of data gathering for analytics, including:

• Advanced techniques for scraping dynamic web content
• Extracting structured data from tables and complex web pages
• Handling authentication, cookies, and session management
• Approaches to deal with CAPTCHAs and restricted access
• Optimizing scraping for large-scale datasets

Master professional techniques to make your data workflows reliable and efficient, including:

• Implementing robust error handling in data pipelines
• Creating custom exceptions for data quality issues
• Using logging frameworks for reproducibility and audit trails
• Debugging complex models and workflows with checkpoints
• Profiling performance and optimizing computation

Explore enterprise-level orchestration techniques for managing large-scale data projects:

• Setting up and configuring task/job queues for data processing
• Managing credentials, APIs, and data assets securely
• Implementing automated scheduling and prioritization of pipelines
• Monitoring workflows with alerts and logging
• Best practices for deploying data solutions across multiple environments

Develop a complete end-to-end data science solution by covering:

• Gathering business requirements and understanding the problem statement
• Designing the data pipeline and solution architecture
• Applying all learned techniques: data preprocessing, modeling, and evaluation
• Documenting methodologies and ensuring knowledge transfer
• Deploying the model into production and planning maintenance strategies