Course Material: Python for Data Science, AI & Development
Course Material: Python for Data Science, AI & Development
Course Outline
| Module | Topic | Subtopics |
|---|---|---|
| Module 1 | Introduction to Python | – Installing Python and Setup – Python Syntax – Variables and Data Types – Operators and Expressions |
| Module 2 | Python for Data Science | – Python Libraries for Data Science (Pandas, NumPy, Matplotlib) – Data Structures in Python – Data Cleaning |
| Module 3 | Data Analysis with Pandas and NumPy | – Introduction to Pandas – Series and DataFrames – Data Manipulation and Transformation – NumPy Arrays |
| Module 4 | Data Visualization in Python | – Data Visualization with Matplotlib – Seaborn for Statistical Visualization – Plotly for Interactive Visualizations |
| Module 5 | Machine Learning with Python | – Supervised vs Unsupervised Learning – Regression and Classification Models – K-Nearest Neighbors and SVM |
| Module 6 | AI and Neural Networks | – Introduction to Artificial Intelligence – Neural Network Basics – TensorFlow and Keras |
| Module 7 | Deep Learning with Python | – Convolutional Neural Networks (CNN) – Recurrent Neural Networks (RNN) – Building Deep Learning Models with Keras |
| Module 8 | Natural Language Processing (NLP) with Python | – Text Preprocessing and Tokenization – Sentiment Analysis – Named Entity Recognition (NER) |
| Module 9 | Python for Web Development | – Introduction to Web Development with Python – Flask Framework – Building Simple Web Applications with Flask |
| Module 10 | Python for Software Development | – Object-Oriented Programming (OOP) in Python – Software Design Patterns – Git for Version Control |
| Module 11 | Python for Big Data | – Working with PySpark – Introduction to Hadoop and Hive – Managing Large Datasets with Pandas |
| Module 12 | Model Evaluation and Validation | – Cross-Validation Techniques – Performance Metrics (Accuracy, Precision, Recall, F1-Score) – Hyperparameter Tuning |
| Module 13 | Python for Cloud Computing | – Introduction to Cloud Platforms – Using Python with AWS and Google Cloud – Automating Cloud Tasks with Boto3 |
| Module 14 | Automation and Scripting with Python | – Automating Repetitive Tasks with Python – Writing Python Scripts for Data Processing – Scheduling Python Tasks |
| Module 15 | Capstone Project and Final Assessment | – Designing a Data Science or AI Project – Project Review and Presentation – Final Exam |
Module Descriptions
Module 1: Introduction to Python
This module serves as the introduction to Python programming, where students will learn the basics of Python syntax, variables, and data types. These concepts lay the foundation for more advanced data science, AI, and development topics.
Key Topics:
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Installing Python and Setup: Learn how to install Python and set up a working environment using IDEs like PyCharm or Jupyter Notebook.
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Python Syntax: Understand how Python uses indentation and the structure of a basic Python script.
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Variables and Data Types: Learn about the different data types in Python like integers, floats, strings, and booleans, and how to work with variables.
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