Applied Data Science with Python
Course Completed
Applied Machine Learning in Python Applied Text Mining in Python Applied Social Network Analysis in Python Applied Plotting, Charting & Data Representation in Python Introduction to Data Science in Python
Description
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 -Understand techniques such as lambdas and manipulating csv files -Describe common Python functionality and features used for data science -Query DataFrame structures for cleaning and processing -Explain distributions, sampling, and t-tests -Describe what makes a good or bad visualization -Understand best practices for creating basic charts -Identify the functions that are best for particular problems -Create a visualization using matplotlb -Describe how machine learning is different than descriptive statistics -Create and evaluate data clusters -Explain different approaches for creating predictive models -Build features that meet analysis needs -Understand how text is handled in Python -Apply basic natural language processing methods -Write code that groups documents by topic -Describe the nltk framework for manipulating text -Represent and manipulate networked data using the NetworkX library -Analyze the connectivity of a network -Measure the importance or centrality of a node in a network -Predict the evolution of networks over time