Applied Data Science with Python Specialization
Gain new insights into your data . Learn to apply data science methods and techniques, and acquire analysis skills.
About This Specialization
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a 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. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
Follow the suggested order or choose your own.
Designed to help you practice and apply the skills you learn.
Highlight your new skills on your resume or LinkedIn.
- Intermediate Specialization.
- Some related experience required.
COURSE 1Current session: Jan 15
- English, Vietnamese, Chinese (Traditional)
About the CourseThis 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. The course will i
COURSE 2Upcoming session: Feb 5
About the CourseThis 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
COURSE 3Current session: Jan 15
About the CourseThis course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than des
COURSE 4Current session: Jan 15
About the CourseThis 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 frame
COURSE 5Upcoming session: Jan 29
About the CourseThis 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. The second week in
V. G. Vinod Vydiswaran
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