Course objectives

After completing this course, students will be able to:

• • Set up a Python development environment
  • Write efficient Python code for data analysis tasks
  • Import and clean data from various sources
  • Manipulate and transform data using Pandas
  • Perform statistical analysis and data visualization
  • Build predictive models using machine learning techniques

Course outlines

• Module 1: Python Fundamentals
• • Introduction to Python
  • Basic syntax and data types
  • Control flow statements (conditional statements, loops)
  • Functions and modules
• Module 2: Data Analysis with NumPy
• • Introduction to NumPy
  • Creating and manipulating arrays
  • Array operations and mathematical functions
  • Array indexing and slicing
• Module 3: Data Manipulation with Pandas
• • Introduction to Pandas
  • Series and Data Frames
  • Data cleaning and preprocessing
  • Data exploration and analysis
  • Data aggregation and grouping
• Module 4: Data Visualization with Matplotlib and Seaborn
• • Introduction to Matplotlib
  • Creating basic plots (line plots, scatter plots, bar plots)
  • Customizing plots (colors, labels, titles)
  • Introduction to Seaborn
  • Creating more sophisticated visualizations (histograms, box plots, heatmaps)
• Module 5: Statistical Analysis with Python
• • Descriptive statistics
  • Hypothesis testing
  • Correlation and regression analysis
• Module 6: Machine Learning with Scikit-learn
• • Introduction to machine learning
  • Model selection and evaluation
  • Supervised learning (linear regression, logistic regression, decision trees, random forests)
  • Unsupervised learning (clustering, dimensionality reduction)