The 5 courses in the University of Michigan’s Applied Data Science with Python specialization introduce learners to data science through the python programming language.
This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
What will you learn
- Analyze the connectivity of a social network
- Conduct an inferential statistical analysis
- Discern whether a data visualization is good or bad
- Enhance a data analysis with applied machine learning
- Text Mining
- Python Programming
There are 5 Courses in this Specialization
- Introduction to Data Science in Python: This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.
- Applied Plotting, Charting & Data Representation in Python: This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations.
- Applied Machine Learning in Python: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
- Applied Text Mining in Python: This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text.
- Applied Social Network Analysis in Python: This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks.
To enroll for this course, click the link below.
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