Course

CS-401: Applied data analysis

Lectures in this course (78)

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Handling Text Data: Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Handling Network Data

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

Machine Learning: Features and Model Selection

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

Scaling to Massive Data: Spark Fundamentals

Covers the fundamentals of scaling to massive data using Spark, focusing on RDDs, transformations, actions, Spark architecture, and Spark's machine learning toolkit.

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.

Big Data Challenges: Distributed Computing with Spark

Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.

Clustering Methods: K-means and DBSCAN

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

Rebuttal Process in Academic Conferences

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

Text Data Processing: Basics & Techniques

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

Postmortem Memory Analysis

Explores the analysis of postmortem memory of public figures in news and social media, uncovering significant insights into memory formation.

Text Handling: Matrix, Documents, Topics

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

Postmortem Memory Analysis

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

Handling Networks: Graph Theory

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

Scaling up: Spark and Big Data

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

Armed Conflicts in Online News

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