Python for Data Science, AI & Development

Description

Here’s a comprehensive curriculum for a course titled:

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🐍 Python for Data Science, AI & Development

🎯 Course Objective:

To equip learners with the foundational and practical knowledge of Python for use in data science, machine learning, AI, and application development.

🕒 Duration:

8 to 12 weeks (adjustable for university courses, bootcamps, or corporate training)

🎓 Target Audience:

• Beginners in programming
• Aspiring data scientists and AI developers
• Engineers or analysts transitioning to data/AI roles

🔑 Prerequisites:

• Basic computer literacy
• No prior coding experience required (ideal for beginners)

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🗂️ Course Modules Overview

Module 1: Introduction to Python

• Why Python for Data Science and AI?
• Installing Python & using Jupyter Notebooks
• Python syntax, variables, and data types
• Control structures: if, loops
• Functions and basic error handling

Lab: Write your first Python scripts in Jupyter

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Module 2: Working with Data in Python

• Lists, tuples, dictionaries, and sets
• File I/O (read/write CSV, TXT)
• Introduction to libraries: NumPy, Pandas
• DataFrame operations: filtering, sorting, grouping

Lab: Load and clean a real-world dataset (e.g. Titanic or Iris dataset)

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Module 3: Data Visualization

• Why visualization matters
• Plotting with Matplotlib and Seaborn
• Line plots, histograms, bar charts, box plots, heatmaps
• Styling and customizing plots

Lab: Visualize insights from a cleaned dataset

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Module 4: Python for Statistical Analysis

• Descriptive statistics (mean, median, std deviation)
• Probability basics
• Distributions (normal, binomial)
• Hypothesis testing (t-test, chi-square)

Lab: Analyze data using Python’s SciPy and Statsmodels

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Module 5: Introduction to Machine Learning with Python

• Overview of machine learning
• Supervised vs unsupervised learning
• Using scikit-learn
  • Linear regression
  • Classification (k-NN, Decision Trees)
• Model evaluation (accuracy, confusion matrix)

Lab: Build a simple model to predict housing prices or loan approval

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Module 6: Introduction to AI Concepts

• What is AI? Relationship with ML and DL
• Rule-based AI vs learning-based AI
• Overview of neural networks
• Intro to TensorFlow or PyTorch (high-level)

Lab: Build a simple neural network to classify images (e.g. MNIST)

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Module 7: Python for Application & Web Development (Optional)

• Using Python in full-stack apps
• Basics of Flask for web APIs
• Calling AI/ML models in Flask apps

Lab: Deploy a simple ML model as a web service

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Module 8: Working with APIs & Automation

• What are APIs?
• Consuming REST APIs with Python (requests)
• Automating data tasks with scripts
• Basics of data pipelines

Lab: Pull data from an external API (e.g., weather or Twitter API) and analyze it

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Module 9: Project Week

• Final capstone project ideas:
  • Data dashboard (with Plotly/Dash)
  • Predictive model (housing, health, finance)
  • AI-powered chatbot
  • Automation tool using Python scripts

Deliverables: Presentation + source code + Jupyter Notebook

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📚 Tools & Platforms Used

• Python (latest version)
• Jupyter Notebooks
• Anaconda or Google Colab
• NumPy, Pandas, Matplotlib, Seaborn
• scikit-learn, TensorFlow/PyTorch (intro level)
• Flask (optional for dev)
• Git/GitHub for version control

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🧪 Assessment & Evaluation

• Weekly quizzes
• Hands-on labs and assignments
• Mid-course practical test
• Final capstone project (graded)

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📖 Suggested Readings & Resources

• Python for Data Analysis – Wes McKinney
• Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow – Aurélien Géron
• Kaggle Notebooks & Competitions
• IBM’s “Python for Data Science” (Coursera or edX)

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