Data Visualization & Machine Learning

Get ready to make your data shine! In Week 4, we’re moving into the exciting world of data visualization and machine learning. You’ll learn how to create beautiful, impactful charts and graphs that tell a story, and we’ll introduce you to the basics of machine learning—where your data begins to predict, classify, and impress. It’s time to unlock the magic of smart data!"

Session 7: Data Visualization with Matplotlib and Seaborn

Date and Time: 19.12.2024, 14:00-16:45
Duration: 2 hours 30 minutes, 15 minutes break

Content:

• Matplotlib Basics:
  • Plotting basic graphs: line, scatter, bar charts.
  • Customizing plots (titles, labels, legends).

• Advanced Plotting with Seaborn:
  • Creating statistical plots (histograms, box plots, heatmaps).
  • Styling and customizing Seaborn plots.

•  Integrating with Pandas:
  • Plotting directly from DataFrames.
  • Visualizing data analysis results.

•  Practical Exercises:
  • Creating visualizations for sample datasets.
  • Interpreting and presenting data through plots.
    
    

Session 8: Elements of Machine Learning

Date and Time: 20.12.2024, 14:00-16:15
Duration: 2 hours, 15 minutes break

Content:

• Machine Learning Concepts:
  • What is machine learning?
    • Types of machine learning: supervised vs. unsupervised.scikit-learn user guide

• Supervised Learning Algorithms:
  • Linear Regression:
    • Understanding linear relationships.
    • Implementing linear regression in Python.
  • Logistic Regression:
    • Binary classification problems.
    • Implementing logistic regression.
  • Support Vector Machines (SVM):
    • Concept of hyperplanes and margins.
    • Using SVM for classification tasks.
  • Decision Trees (DT):
    • Understanding tree-based models.
    • Building decision trees for classification and regression.

• Practical Implementation:
  • Introduction to scikit-learn library.
    • Building and evaluating simple predictive models: evaluation metrics and confusion matrices. ”Ethical” Considerations:
    • Bias and fairness in machine learning.
    • Importance of data quality.