Course curriculum

  • 1

    Welcome to the course!

  • 2

    Installation of Python and Data Science Librairies

  • 3

    Introduction to Python for Data Science

    • Basics Python
    • Loops in Python
    • Functions and variables in Python
    • Modules in Python
  • 4

    NumPy Fundamentals

    • Basics of NumPy
    • NumPy functions
    • NumPy Array manipulations
    • NumPy Array reshaping and concatenation
    • More Operations in NumPy Array
  • 5

    Pandas Fundamentals

    • Introduction to Pandas
    • Pandas Series
    • Pandas Data Frame (Part 1)
    • Pandas Data Frame (Part 2)
    • Dealing with missing values using Pandas
  • 6

    Matplotlib Fundamentals

    • Introduction to matplotlib
    • Drawing and saving figures
    • Matplotlib Subplots
    • Parameter tuning for better visualization
    • Scatter plots and Histograms
  • 7

    Introduction to Statistics

    • Basic statistical operations
    • Exploratory analysis
    • Chi square test in Python
    • Analysis of Variances (ANOVA)
    • Sampling
  • 8

    Machine Learning Models Theory and Applications

    • How Linear Regression works
    • Linear Regression in Python
    • How Naive Bayes work
    • Naive Bayes with Python
    • How KNN works
    • KNN in Python
    • How Logistic Regression works
    • Logistic Regression in Python
    • Decision Trees in Python
    • How Decision trees works
    • How Random Forest works
    • Random Forest in Python
    • How K-means clustering works
    • K-means clustering in Python
    • How SVM works
    • SVM in Python
    • How Neural Networks works
    • Neural Networks in Python
  • 9

    Best Practices for Data Scientist

    • 10.3. Feature selection and Label encoding
    • 10.1. Dealing with outliers
    • 10.4. Hyperparameter tuning
    • 10.2. Dealing with missing values