Associate Professor
2 Learning Experiences
More Applied Data Science with Python
What You'll Learn
- Build foundational analytic and machine learning techniques through data mining concepts, representing real-world data, and extraction patterns
- Analyze network structures using NetworkX, apply network generation models, simulate diffusion processes, and detect community structures
- Explore unstructured data using clustering, dimensionality reduction, and topic modeling to uncover hidden patterns and improve predictive analysis
- Extract meaningful information from text data by applying machine learning techniques for named entity recognition across diverse domains
4-Course Series
14 weeks
35 hours per course (approx.)
Shareable Certificate
Add to your LinkedIn profile
About More Applied Data Science with Python
In our increasingly interconnected world, we’re collecting more raw data than ever. In “More Applied Data Science with Python,” you’ll learn how to extract and analyze complex data sets using Python. Practice using real-world data sets, like health data and comment sections, to develop visual representations and identify key patterns amongst populations. You’ll also learn to manage missing and messy data using advanced manipulation methods. Throughout this course series, you’ll build a foundation for advanced analytics and machine learning with the help of Scikit-Learn and NLP libraries by applying methods for data mining, clustering, topic modeling, network modeling, and information extraction. Upon completing the series, you'll have gained advanced data analysis skills that will help you gain insights into the datasets you're exploring.
Learners should have intermediate Python programming skills before enrolling in the Specialization. It is encouraged that you complete Applied Data Science with Python prior to beginning this Specialization.
Skills You'll Gain
- Data Mining
- Python (Programming Language)
- Data Presentation
- Data Analysis
- Text Processing
- Python For Data Analysis
- Probability Distribution
- Unsupervised Learning
- Data Manipulation
- Text Extraction
- Machine Learning
- Information Extraction
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Associate Professor
2 Learning Experiences
Associate Professor
3 Learning Experiences
Professor
1 Learning Experience
Advanced Level
Intermediate Python skills, knowledge of linear algebra and machine learning in Python, and have completed
Applied Data Science with Python.
Series Video
4 Courses in this series
-
Free U-M Access CourseData Mining in Python
Learn to extract patterns from real-world datasets using data mining principles and Python for business and social insights.
-
Free U-M Access CourseApplied Unsupervised Learning in Python
Discover how to use unsupervised learning techniques to find patterns in data, including clustering, topic modeling, and dimensionality reduction.
-
Free U-M Access CourseNetwork Modeling and Analysis in Python
Use Python and NetworkX to analyze complex systems like epidemics and social media using network theory and diffusion models.
-
Free U-M Access CourseApplied Information Extraction in Python
Use machine learning and NLP to extract meaningful patterns from free-text data, including names, locations, and complex real-world entities.
What Learners Are Saying
Each course in this series will help you build data analytics skills using Python and increase your understanding of the role of data in shaping decisions.
Qiaozhu Mei Professor of Information, Associate Dean for Research and Innovation, School of Information
