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Website Development Curriculum


Module 1:Introduction to Python for Data Science:

  • Why Python for Data Science?
  • Installing Python and Setting up the Data Science Environment
  • Writing and Executing Your First Python Data Science Program

Module 2:Python Language Fundamentals for Data Science:

  • Character Set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading Input from Console
  • Parsing Strings to Integers and Floats

Module 3:Python Conditional Statements and Looping for Data Science:

  • If Statements
  • If-Else Statements
  • If-Elif Statements
  • If-Elif-Else Statements
  • Looping with While and For Loops
  • Nested Loops
  • Pass, Break, and Continue Keywords

Module 4:Standard Data Types for Data Science:

  • Numeric Types (int, float, complex, bool, NoneType)
  • Strings (str)
  • Lists (list)
  • Tuples (tuple)
  • Sets (set)
  • Dictionaries (dict)

Module 5:Data Manipulation and Analysis with Python:

  • Introduction to NumPy
  • Introduction to Pandas
  • Data Loading and Exploration
  • Data Cleaning and Preprocessing
  • Data Visualization with Matplotlib and Seaborn

Module 6:Statistical Analysis and Probability:

  • Introduction to Statistics
  • Probability Distributions
  • Hypothesis Testing
  • Correlation and Covariance

Module 7:Machine Learning Basics:

  • Introduction to Machine Learning
  • Supervised Learning (Classification and Regression)
  • Unsupervised Learning (Clustering)
  • Feature Engineering and Selection
  • Model Evaluation and Metrics

Module 8:Machine Learning Algorithms:

  • K-Nearest Neighbors (KNN)
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • Ensemble Learning
  • Model Selection and Hyperparameter Tuning
  • Recommendation Systems
  • Dimensionality Reduction (PCA)

Module 9:Natural Language Processing (NLP):

  • Introduction to NLP
  • Text Preprocessing
  • Feature Extraction for Text Data
  • Sentiment Analysis
  • Text Classification with Naive Bayes

Module 10:Image Processing with OpenCV:

  • Introduction to OpenCV
  • Reading and Manipulating Images
  • Object Detection and Classification
  • Webcam Integration

Module 11:Deep Learning (Optional):

  • Introduction to Deep Learning
  • Neural Networks with TensorFlow or PyTorch
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Transfer Learning

Module 12:Final Data Science Project:

  • Capstone Project - Applying Data Science Techniques to Real-World Data