AI-Powered Python for Data Science

Learning Objective: You will use Copilot to practice working with Python data structures (lists, tuples, sets, dictionaries), functions, and modules, including managing and documenting your modules.

• Mastering Python Data Structures with Copilot:
• • Sequential Types: Use Copilot to generate code examples and explain operations and practice:
  • • Strings: formatting, slicing.
    • Lists: comprehensions, methods.
    • Tuples: unpacking, immutability.
  • Unordered Types: Use Copilot to explore use cases, generate code, and practice:
  • • Sets: mathematical operations, uniqueness.
    • Dictionaries: comprehensions, key-value pairs.
  • Copilot in Action: Use Copilot to generate code for data structure manipulations, explain the efficiency of different operations, create practice problems and solutions, and debug errors.
• Functions Practice with Copilot:
• • Defining and Calling Functions: Use Copilot to practice function definition, parameter passing, and return values.
  • Scope and Lifetime: Use Copilot to understand variable scope and lifetime within functions.
  • Recursion: Explore recursion with Copilot's help.
  • Copilot Chat for Function Concepts: Use Copilot Chat to get explanations of function-related concepts, generate examples, and discover best practices.
• Modules and Packages:
• • Importing Modules: Use Copilot to understand and practice importing standard library modules (e.g., math, random, datetime).
  • Creating, Using, Managing, and Documenting Custom Modules: Learn how to use Copilot to make, use, manage, and document your Python modules, enhancing code organisation and reusability.

Learning Objective: You will utilise Copilot to practice numerical computation with NumPy, data analysis with Pandas, and create data visualisations with Matplotlib and Seaborn.

• NumPy Practice with Copilot:
• • Arrays: Use Copilot to practice array creation, indexing, slicing, reshaping, and broadcasting.
  • Mathematical Operations: Use Copilot to perform mathematical operations on arrays and understand vectorised computation.
  • Copilot in Action: Use Copilot to generate NumPy code snippets, explain array operations, and see the power of NumPy for numerical tasks.
• Data Analysis with Pandas and Copilot:
• • Series and DataFrames: Use Copilot to practice creating, manipulating, and querying Series and DataFrames.
  • Data Cleaning and Transformation: Use Copilot to handle missing data, perform data type conversions, and apply various data wrangling techniques.
  • Data Aggregation and Grouping: Use Copilot to perform aggregations, grouping operations, and generate summary statistics.
  • Copilot Chat for Pandas Assistance: Use Copilot Chat to explain Pandas functions, discover data analysis approaches, and get help with debugging.
• Data Visualization with Matplotlib, Seaborn, and Copilot:
• • Plotting Basics: Use Copilot to generate code for various plot types (scatter, line, bar, histogram, box plots, etc.).
  • Customisation and Styling: Use Copilot to customise plot appearance and add labels, titles, legends, and annotations.
  • Interpreting Visualizations: Use Copilot to understand and explain the insights revealed by plots.
  • Copilot in Action: Use Copilot to generate complete visualisation code from descriptions, suggest appropriate plot types, and help with styling.
  • Automating Repetitive Tasks: Learn how to use Copilot to automate repetitive data analysis and visualisation tasks, enhancing productivity.

Learning Objective: You will use Copilot to master advanced Python concepts like object-oriented programming (OOP), explore techniques for efficient data processing and analysis, and enhance your data science workflow with Copilot in Word, Excel, and PowerPoint.

• Advanced Python - Object-Oriented Programming (OOP) with Copilot:
• • Classes and Objects: Use Copilot to understand and practice defining classes, creating objects, and working with attributes and methods.
  • Inheritance and Polymorphism: Explore how Copilot can help you implement inheritance and polymorphism in your code.
  • Copilot in Action: Use Copilot to generate code for OOP concepts, explain class structures, and demonstrate the benefits of OOP for organising and reusing code.
• Techniques for Efficient Data Processing and Analysis:
• • List Comprehensions and Generators: Use Copilot to write more concise and efficient code with list comprehensions and generators.
  • Lambda Functions: Use Copilot to create and use anonymous functions for simple operations.
  • Working with Large Datasets: Use Copilot to explore techniques like chunking and iterators for processing large datasets that don't fit in memory.
  • Copilot Chat for Optimization: Ask Copilot Chat to suggest ways to optimise your code for performance and memory usage.
• Copilot for Data Science Workflow Enhancement:
• • Using Copilot in Word: Generate report outlines, draft data analysis reports, and incorporate Python code snippets and visualisations.
  • Using Copilot in PowerPoint: Create data-driven presentations, generate slide content from data analysis results, and discover effective layouts.
  • Using Copilot in Excel: Automate data cleaning and transformation tasks, generate formulas for data analysis and get a brief overview of creating interactive dashboards.

Learning Objective: You will leverage Copilot to prepare for a data science career, build a strong portfolio, and practice interview skills.

• Copilot for Career Planning and Job Search:
• • Market Research with Copilot Chat: Use Copilot Chat to research data science roles, required skills, salary ranges, industry trends, and potential employers.
  • Resume/CV and Cover Letter Crafting: Use Copilot in Word to generate tailored resumes and cover letters and get feedback on their content and structure. Learn how to create a strong online presence that showcases your skills.
  • Interview Preparation: Use Copilot Chat to practice data science interview questions (technical and behavioural and get feedback on your answers. Learn strategies for effectively communicating your skills and experience during interviews.
• Building a Data Science Portfolio with Copilot:
• • Project Idea Generation: Use Copilot to brainstorm project ideas that align with your interests and career goals.
  • Project Planning and Outlining: Use Copilot to outline the steps involved in a data science project, including data collection, cleaning, analysis, visualisation, and modelling.
  • Project Documentation: Use Copilot to write clear project descriptions, explain your methodology, and document your code effectively.
  • Showcasing Your Portfolio: Get guidance on platforms for showcasing your projects (e.g., GitHub, personal website) and how to present your work effectively to potential employers.
• Practice and Feedback:
• • Mock Interview Session: Participate in a mock interview using Copilot Chat, focusing on technical skills and behavioural questions.
  • Portfolio Review: Get feedback on your portfolio projects from peers and the instructor, using Copilot to suggest improvements.