Foundermental Data Science with Python Language

The University of Michigan offers a specialization in Applied Data Science with Python, consisting of five comprehensive courses. This specialization introduces learners to data science using the Python programming language. It is designed for those with a basic understanding of Python or programming who wish to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques. The specialization utilizes popular Python libraries such as pandas, matplotlib, scikit-learn, nltk, and networkx to help learners derive insights from data.

The courses should be taken in the following sequence for optimal learning outcomes:

1. Introduction to Data Science in Python
   • Duration: 34 hours
   • Rating: 4.5 (27,154 ratings)
   • Objectives: Learn techniques like lambdas and CSV file manipulation, explore Python functionalities for data science, query DataFrame structures, and understand distributions, sampling, and t-tests.
2. Applied Plotting, Charting & Data Representation in Python
   • Duration: 24 hours
   • Rating: 4.5 (6,267 ratings)
   • Objectives: Understand what makes visualizations effective, create basic charts using best practices, and implement functions for specific data visualization tasks using matplotlib.
3. Applied Machine Learning in Python
   • Duration: 31 hours
   • Rating: 4.6 (8,556 ratings)
   • Objectives: Differentiate machine learning from descriptive statistics, create and evaluate data clusters, develop predictive models, and construct features for analysis.

After completing the first three courses, learners can take the following courses in any order:

4. Applied Text Mining in Python
   • Duration: 25 hours
   • Rating: 4.2 (3,814 ratings)
   • Objectives: Handle text data in Python, apply basic NLP methods, utilize the nltk framework, and group documents by topics.
5. Applied Social Network Analysis in Python
   • Duration: 26 hours
   • Rating: 4.6 (2,708 ratings)
   • Objectives: Use the NetworkX library to manipulate network data, analyze network connectivity, measure node centrality, and predict network evolution.