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 – Basic Python Syntax – Data Types and Variables – Operators and Expressions |
| Module 2 | Python for Data Science | – Introduction to Data Science – Python Libraries for Data Science (Pandas, NumPy, Matplotlib) – Data Cleaning |
| Module 3 | Data Analysis with Pandas and NumPy | – Introduction to Pandas – DataFrames and Series – Data Manipulation and Transformation – NumPy Arrays |
| Module 4 | Data Visualization in Python | – Introduction to Data Visualization – Matplotlib Basics – Seaborn for Advanced Visualization – Plotly |
| Module 5 | Machine Learning with Python | – Introduction to Machine Learning – Supervised vs Unsupervised Learning – Regression and Classification Models |
| Module 6 | AI and Neural Networks | – Introduction to Artificial Intelligence – Neural Networks Basics – TensorFlow and Keras Libraries |
| Module 7 | Deep Learning with Python | – Introduction to Deep Learning – Convolutional Neural Networks (CNN) – Recurrent Neural Networks (RNN) |
| Module 8 | Natural Language Processing (NLP) with Python | – Introduction to NLP – Text Preprocessing – Sentiment Analysis – Named Entity Recognition (NER) |
| Module 9 | Python for Web Development | – Introduction to Web Development with Python – Flask Framework – Building Simple Web Applications |
| Module 10 | Python for Software Development | – Introduction to Software Development – Object-Oriented Programming (OOP) in Python – Using Git for Version Control |
| Module 11 | Building Machine Learning Applications | – Model Training and Evaluation – Model Deployment in Production – Using Flask for ML Web Applications |
| Module 12 | Python for Big Data | – Working with Big Data Libraries (PySpark) – Data Storage with HDFS – Performing Analysis on Big Data Sets |
| Module 13 | Python for Cloud and Automation | – Python for Cloud Integration – Automating Tasks with Python Scripts – Cloud Libraries (Boto3, Google Cloud) |
| Module 14 | Python Best Practices and Advanced Topics | – Writing Efficient Python Code – Advanced Python Features (Decorators, Generators) – Code Testing and Debugging |
| Module 15 | Final Project and Assessment | – Implementing Data Science, AI, and Development Projects – Code Review – Final Exam |
Module Descriptions
Module 1: Introduction to Python
Objective: Introduce Python programming language basics.
Key Topics:
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Installing Python and Setup: Learn how to install Python on your system and set up an Integrated Development Environment (IDE) like PyCharm or VSCode.
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Basic Python Syntax: Understand how to write Python code, including indentation, comments, and the structure of a Python program.
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Data Types and Variables: Python supports different data types like integers, floats, strings, and booleans. Learn how to declare and use variables.
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Operators and Expressions: Use arithmetic operators, comparison operators, and logical operators to create expressions.
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