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Applied Data Science with Python

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, and V. G. Vinod Vydiswaran

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.

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What's inside

Five courses

Introduction to Data Science in Python

This course introduces the basics of the Python programming environment, including fundamental Python programming techniques, data manipulation and cleaning techniques using the popular Python Pandas data science library, and the abstraction of the Series and DataFrame as the central data structures for data analysis. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

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Applied Plotting, Charting & Data Representation in Python

This course introduces information visualization basics, focusing on reporting and charting with matplotlib. It covers design principles, statistical measures, and best practices for creating basic charts. The course also demonstrates various statistical charts and discusses other forms of data visualization.

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Applied Machine Learning in Python

This course introduces applied machine learning, emphasizing techniques over statistics. It covers scikit learn, dimensionality, clustering, supervised predictive models, cross validation, overfitting, and advanced techniques like ensembles. By the end, students will be able to distinguish supervised and unsupervised techniques, apply them to datasets, engineer features, and write Python code for analysis.

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Applied Text Mining in Python

This course introduces text mining and manipulation basics. It covers text handling in Python, text structure, and the nltk framework. It also focuses on common manipulation needs, including regular expressions, text cleaning, and preparation for machine learning. The course applies basic natural language processing methods to text and demonstrates text classification. Finally, it explores advanced methods for detecting topics in documents and grouping them by similarity (topic modelling).

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Applied Social Network Analysis in Python

This course introduces network analysis through tutorials using the NetworkX library. It covers concepts like connectivity, network robustness, node centrality, network evolution, network generation, and link prediction.

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Learning objectives

Conduct an inferential statistical analysis
Discern whether a data visualization is good or bad

Enhance a data analysis with applied machine learning
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
Analyze the connectivity of a social network

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Time

0 hours

Level

Intermediate

Via

Coursera

Institution

University of Michigan

Instructors

Christopher Brooks

Kevyn Collins-Thompson

Daniel Romero

V. G. Vinod Vydiswaran

Level

Intermediate

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