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📊 Applied Data Science with Python – University of Michigan

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Instructor(s) : V. G. Vinod Vydiswaran, Assistant Professor

This repository documents my journey through the Applied Data Science with Python Specialization offered by the University of Michigan on Coursera. The specialization focuses on practical data science skills using Python, covering data manipulation, visualization, machine learning, and text mining.


🎯 Specialization Overview

This specialization provides a hands-on introduction to data science using Python. It emphasizes real-world datasets, practical techniques, and applied problem-solving.

Skills Gained

  • Data manipulation and cleaning
  • Data visualization and storytelling
  • Statistical analysis
  • Machine learning fundamentals
  • Text mining and NLP
  • Social network analysis

📚 Course Breakdown

1. Introduction to Data Science in Python

Description: Covers the basics of Python for data science, including data structures, data manipulation, and introductory analysis.

Key Topics:

  • Python fundamentals (lists, dictionaries, functions)
  • NumPy basics
  • Pandas for data manipulation
  • Data cleaning and preprocessing
  • Handling missing data
  • Basic data analysis

Tools:

  • Python
  • NumPy
  • Pandas

2. Applied Plotting, Charting & Data Representation in Python

Description: Focuses on visualizing data effectively using Python libraries.

Key Topics:

  • Principles of data visualization
  • Matplotlib basics and advanced usage
  • Chart types (line, bar, scatter, histograms)
  • Data storytelling
  • Visual encoding and perception

Tools:

  • Matplotlib
  • Pandas plotting

3. Applied Machine Learning in Python

Description: Introduces machine learning concepts and implementation using Scikit-learn.

Key Topics:

  • Supervised learning (classification & regression)
  • Model evaluation and validation
  • Overfitting and underfitting
  • Feature engineering
  • Model selection

Algorithms Covered:

  • k-Nearest Neighbors
  • Decision Trees
  • Logistic Regression
  • Support Vector Machines

Tools:

  • Scikit-learn
  • NumPy
  • Pandas

4. Applied Text Mining in Python

Description: Explores natural language processing and working with textual data.

Key Topics:

  • Text preprocessing (tokenization, normalization)
  • Regular expressions
  • Bag-of-words and TF-IDF
  • Sentiment analysis
  • Topic modeling basics

Tools:

  • NLTK
  • Scikit-learn

5. Applied Social Network Analysis in Python

Description: Introduces graph theory and analysis of social networks.

Key Topics:

  • Network structure and metrics
  • Centrality measures
  • Community detection
  • Graph visualization
  • Real-world network datasets

Tools:

  • NetworkX
  • Matplotlib

🛠️ Technologies Used

  • Python 3.x
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn
  • NLTK
  • NetworkX

🧠 Learning Outcomes

By completing this specialization, you will be able to:

  • Analyze and manipulate complex datasets
  • Create meaningful visualizations
  • Build and evaluate machine learning models
  • Process and analyze text data
  • Understand and analyze network structures

📌 Notes

  • All assignments are based on real-world datasets.
  • Emphasis is on practical application rather than theory.
  • Ideal for learners with basic Python knowledge.

📜 License

This repository is for educational purposes only. Course content belongs to the University of Michigan and Coursera.


🙌 Acknowledgments

  • University of Michigan
  • Coursera platform
  • Course instructors and contributors

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