Advanced Python For Data Science Training Course: Master Data Libraries

Introduction

Elevate your data science skills with our Advanced Python for Data Science Training Course. This program is designed to equip you with the essential skills to master Python libraries for data manipulation and analysis, enabling you to tackle complex data challenges with confidence. In today's data-driven world, advanced Python proficiency is crucial for extracting meaningful insights and building robust data solutions. Our advanced Python training course offers hands-on experience and expert guidance, empowering you to leverage powerful libraries like NumPy, Pandas, Scikit-learn, and more.

This master data libraries training delves into the core concepts of advanced Python for data science, covering topics such as data cleaning, feature engineering, and model building. You'll gain expertise in using industry-standard Python libraries to master Python libraries for data manipulation and analysis, meeting the demands of modern data science projects. Whether you're a data analyst, data scientist, or machine learning engineer, this Advanced Python for Data Science course will empower you to build and deploy sophisticated data-driven applications.

Target Audience:

• Data Scientists
• Data Analysts
• Machine Learning Engineers
• Software Developers
• Researchers
• Business Intelligence Developers
• Anyone needing advanced Python data science skills

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to Advanced Python for Data Science

• Fundamentals of advanced Python for data science.
• Overview of essential Python libraries (NumPy, Pandas, Scikit-learn).
• Setting up an advanced Python data science development environment.
• Introduction to best practices and advanced techniques.
• Best practices for advanced Python.

Module 2: Advanced Data Manipulation with Pandas

• Implementing advanced data manipulation with Pandas.
• Utilizing multi-indexing, grouping, and pivoting for complex datasets.
• Designing and building efficient data cleaning and transformation pipelines.
• Optimizing Pandas code for performance.
• Best practices for Pandas.

Module 3: NumPy for Numerical Computations

• Implementing NumPy for efficient numerical computations.
• Utilizing advanced array operations and linear algebra.
• Designing and building high-performance numerical algorithms.
• Optimizing NumPy code for speed and memory efficiency.
• Best practices for NumPy.

Module 4: Advanced Feature Engineering with Scikit-learn

• Implementing advanced feature engineering techniques with Scikit-learn.
• Utilizing transformers and pipelines for feature creation.
• Designing and building feature selection and extraction strategies.
• Optimizing feature engineering for machine learning models.
• Best practices for Scikit-learn.

Module 5: Robust Data Analysis Pipelines

• Designing and building robust data analysis pipelines with Python.
• Utilizing modular and reusable code design.
• Implementing automated data processing and analysis.
• Optimizing pipelines for scalability and maintainability.
• Best practices for pipelines.

Module 6: Python Code Optimization

• Optimizing Python code for performance and scalability.
• Utilizing profiling and benchmarking tools.
• Implementing vectorized operations and parallel processing.
• Designing efficient algorithms and data structures.
• Best practices for code optimization.

Module 7: Troubleshooting Python Data Science Challenges

• Debugging common challenges in Python data science.
• Analyzing code performance and errors.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common data science issues.
• Best practices for troubleshooting.

Module 8: Data Visualization Best Practices

• Implementing data visualization best practices for data exploration.
• Utilizing advanced plotting libraries (Matplotlib, Seaborn, Plotly).
• Designing and building effective data visualizations.
• Optimizing visuals for data insights.
• Best practices for visualization.

Module 9: Integration with Real-World Data Sources

• Integrating Python with real-world data sources and applications.
• Utilizing APIs, databases, and file formats.
• Designing and building data integration pipelines.
• Optimizing integration for data retrieval and processing.
• Best practices for integration.

Module 10: Handling Large Datasets and Memory Management

• Implementing techniques for handling large datasets and memory management.
• Utilizing chunking, streaming, and out-of-core processing.
• Designing and building memory-efficient data processing algorithms.
• Optimizing data handling for large-scale applications.
• Best practices for large datasets.

Module 11: Advanced Python Libraries

• Exploring advanced Python libraries for specialized tasks.
• Utilizing libraries for natural language processing (NLTK, SpaCy).
• Implementing geospatial analysis with GeoPandas.
• Designing and building solutions with specialized libraries.
• Optimizing library usage for specific applications.
• Best practices for advanced libraries.

Module 12: Real-World Use Cases

• Implementing advanced Python for financial data analysis.
• Utilizing Python for social media data analysis and sentiment analysis.
• Implementing Python for bioinformatics and genomics data processing.
• Utilizing Python for recommendation systems and personalization.
• Best practices for real-world applications.

Module 13: Python Ecosystem for Efficient Workflows

• Leveraging Python's ecosystem for efficient data science workflows.
• Utilizing virtual environments and package management.
• Implementing version control with Git.
• Designing and building reproducible data science projects.
• Best practices for efficient workflows.

Module 14: Performance Tuning and Profiling

• Implementing performance tuning and profiling techniques.
• Utilizing cProfile and line_profiler for code optimization.
• Designing and building optimized data science applications.
• Optimizing performance for large datasets and complex computations.
• Best practices for performance tuning.

Module 15: Future Trends in Python Data Science

• Emerging trends in Python data science.
• Utilizing AI for automated data analysis and feature engineering.
• Implementing serverless and cloud-based Python data science.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.