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📊 Applied Data Science with Python – University of Michigan

UM-logo

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

About

Coursera: Michigan python data analysis assignements

Topics

chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

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GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
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📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
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📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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try {
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var __re = new RegExp('^' + ".*" + '
GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
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📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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try {
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GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
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📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
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GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - Abdoelabassi/Michigan-python-data-science: Coursera: Michigan python data analysis assignements · GitHub
Skip to content
BranchesTags
Repository files navigation
📊 Applied Data Science with Python – University of Michigan
UM-logo
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
About
Coursera: Michigan python data analysis assignements
Topics
chartingdata-analysisdata-representationdata-sciencemachine-learningmatplotlibnetwork-analysisnumpypandasplottingpythonregexregexpseaborntext-miningvisualization
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages