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Course

CS-401: Applied data analysis

Lectures in this course (80)

Covers loading compressed files, filtering dataframes, and analyzing data distribution for effective analysis.

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Explores observational studies, pitfalls, solutions, and the importance of valid conclusions in research projects.

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Covers the fundamentals of handling networks and centrality measures in graph theory.

Explores the challenges of big data processing and introduces Spark as a solution.

Covers replicating data analysis from scientific papers and proposing creative extensions to enhance meta data science skills.

Explores the coverage of armed conflicts in online news, analyzing media attention and conflict severity across languages.

Introduces HTTP basics, HTML, and tools for web scraping.

Focuses on the final milestone project, guiding students on creating a data story and video pitch for their creative extension analysis.

By Isabela Constantin reflects on the importance of personal reflection in making career decisions.

Introduces the Applied Data Analysis course, focusing on data science tools and project work.

Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.