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CS-401: Applied data analysis

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Lectures in this course (80)

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Rebuttal Process in Academic Conferences

Explores the academic conference rebuttal process, emphasizing data analysis for paper acceptance, machine learning, and statistical tests.

Postmortem Memory Analysis

Explores postmortem memory analysis using news and social media data, investigating biographic correlates and memory patterns.

Handling Data: Intro to Pandas

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Data Projects Overview

Covers data projects milestones, available datasets, project ideas, and examples.

Visualizing Data: Techniques and Applications

Explores techniques and applications of data visualization, emphasizing the importance of effective communication and unconventional examples.

Describing Data: Statistics & Uncertainty

Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Causal Analysis of Observational Data

Covers causal analysis of observational data, pitfalls, tools for valid conclusions, and addressing confounding variables.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Applied Machine Learning

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

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