15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- Data Analysts
- Machine Learning Engineers
- Software Developers
- Researchers
- Business Intelligence Developers
- Anyone needing advanced Python data science skills
Session Objectives
- Understand the fundamentals of advanced Python for data science.
- Master advanced data manipulation with Pandas for complex datasets.
- Utilize NumPy for efficient numerical computations and array operations.
- Implement advanced feature engineering techniques with Scikit-learn.
- Design and build robust data analysis pipelines with Python.
- Optimize Python code for performance and scalability in data science.
- Troubleshoot and address common challenges in Python data science.
- Implement data visualization best practices for data exploration.
- Integrate Python with real-world data sources and applications.
- Understand how to handle large datasets and memory management.
- Explore advanced Python libraries for specialized data science tasks.
- Apply real world use cases for advanced Python in data science.
- Leverage Python's ecosystem for efficient data science workflows.
About the Course
Push beyond basic scripts and master the full analytical power of the Python data ecosystem with our Advanced Python for Data Science Training Course. Designed for technical practitioners ready to level up their code, this hands-on program gives you direct experience tackling complex data challenges using industry-standard libraries. Under expert instruction, you will work step-by-step through advanced data cleaning routines, engineer high-signal features from messy raw inputs, and build, evaluate, and tune machine learning models. You will walk away with the practical expertise needed to optimize execution speed, manipulate large-scale datasets efficiently, and deliver high-impact data science solutions across modern enterprise environments.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of advanced Python for data science.
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Subtopic 1.2:
Overview of essential Python libraries (NumPy, Pandas, Scikit-learn).
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Subtopic 1.3:
Setting up an advanced Python data science development environment.
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Subtopic 1.4:
Introduction to best practices and advanced techniques.
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Subtopic 1.5:
Best practices for advanced Python.
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Subtopic 2.1:
Implementing advanced data manipulation with Pandas.
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Subtopic 2.2:
Utilizing multi-indexing, grouping, and pivoting for complex datasets.
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Subtopic 2.3:
Designing and building efficient data cleaning and transformation pipelines.
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Subtopic 2.4:
Optimizing Pandas code for performance.
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Subtopic 2.5:
Best practices for Pandas.
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Subtopic 3.1:
Implementing NumPy for efficient numerical computations.
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Subtopic 3.2:
Utilizing advanced array operations and linear algebra.
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Subtopic 3.3:
Designing and building high-performance numerical algorithms.
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Subtopic 3.4:
Optimizing NumPy code for speed and memory efficiency.
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Subtopic 3.5:
Best practices for NumPy.
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Subtopic 4.1:
Implementing advanced feature engineering techniques with Scikit-learn.
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Subtopic 4.2:
Utilizing transformers and pipelines for feature creation.
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Subtopic 4.3:
Designing and building feature selection and extraction strategies.
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Subtopic 4.4:
Optimizing feature engineering for machine learning models.
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Subtopic 4.5:
Best practices for Scikit-learn.
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Subtopic 5.1:
Designing and building robust data analysis pipelines with Python.
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Subtopic 5.2:
Utilizing modular and reusable code design.
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Subtopic 5.3:
Implementing automated data processing and analysis.
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Subtopic 5.4:
Optimizing pipelines for scalability and maintainability.
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Subtopic 5.5:
Best practices for pipelines.
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Subtopic 6.1:
Optimizing Python code for performance and scalability.
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Subtopic 6.2:
Utilizing profiling and benchmarking tools.
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Subtopic 6.3:
Implementing vectorized operations and parallel processing.
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Subtopic 6.4:
Designing efficient algorithms and data structures.
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Subtopic 6.5:
Best practices for code optimization.
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Subtopic 7.1:
Debugging common challenges in Python data science.
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Subtopic 7.2:
Analyzing code performance and errors.
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Subtopic 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Subtopic 7.4:
Resolving common data science issues.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data visualization best practices for data exploration.
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Subtopic 8.2:
Utilizing advanced plotting libraries (Matplotlib, Seaborn, Plotly).
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Subtopic 8.3:
Designing and building effective data visualizations.
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Subtopic 8.4:
Optimizing visuals for data insights.
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Subtopic 8.5:
Best practices for visualization.
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Subtopic 9.1:
Integrating Python with real-world data sources and applications.
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Subtopic 9.2:
Utilizing APIs, databases, and file formats.
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Subtopic 9.3:
Designing and building data integration pipelines.
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Subtopic 9.4:
Optimizing integration for data retrieval and processing.
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Subtopic 9.5:
Best practices for integration.
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Subtopic 10.1:
Implementing techniques for handling large datasets and memory management.
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Subtopic 10.2:
Utilizing chunking, streaming, and out-of-core processing.
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Subtopic 10.3:
Designing and building memory-efficient data processing algorithms.
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Subtopic 10.4:
Optimizing data handling for large-scale applications.
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Subtopic 10.5:
Best practices for large datasets.
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Subtopic 11.1:
Exploring advanced Python libraries for specialized tasks.
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Subtopic 11.2:
Utilizing libraries for natural language processing (NLTK, SpaCy).
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Subtopic 11.3:
Implementing geospatial analysis with GeoPandas.
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Subtopic 11.4:
Designing and building solutions with specialized libraries.
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Subtopic 11.5:
Optimizing library usage for specific applications.
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Subtopic 11.6:
Best practices for advanced libraries.
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Subtopic 12.1:
Implementing advanced Python for financial data analysis.
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Subtopic 12.2:
Utilizing Python for social media data analysis and sentiment analysis.
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Subtopic 12.3:
Implementing Python for bioinformatics and genomics data processing.
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Subtopic 12.4:
Utilizing Python for recommendation systems and personalization.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Leveraging Python's ecosystem for efficient data science workflows.
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Subtopic 13.2:
Utilizing virtual environments and package management.
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Subtopic 13.3:
Implementing version control with Git.
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Subtopic 13.4:
Designing and building reproducible data science projects.
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Subtopic 13.5:
Best practices for efficient workflows.
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Subtopic 14.1:
Implementing performance tuning and profiling techniques.
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Subtopic 14.2:
Utilizing cProfile and line_profiler for code optimization.
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Subtopic 14.3:
Designing and building optimized data science applications.
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Subtopic 14.4:
Optimizing performance for large datasets and complex computations.
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Subtopic 14.5:
Best practices for performance tuning.
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Subtopic 15.1:
Emerging trends in Python data science.
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Subtopic 15.2:
Utilizing AI for automated data analysis and feature engineering.
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Subtopic 15.3:
Implementing serverless and cloud-based Python data science.
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Subtopic 15.4:
Best practices for future applications.
