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Course

CS-401: Applied data analysis

Lectures in this course (80)

Data Visualization: Principles and Techniques

Covers principles and techniques of data visualization, emphasizing storytelling, anomaly detection, and effective communication of results.

Evidence of Fraud in Data Analysis

Explores anomalies in data analysis, revealing potential fraud through suspicious patterns and inconsistencies.

Data Science for Innovation

Explores innovation in data science, spin-offs creation, and emotional virtual voices.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Text Data Analysis: Basics and Techniques

Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.

Document Retrieval and Classification

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

Handling Networks: Graph Theory

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

Big Data Challenges: Distributed Computing with Spark

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

Data Science Projects Overview

Provides an overview of the course organization, lab sessions, quizzes, projects, homework, and communication guidelines.

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Data Modeling: Concepts and Applications

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

Data Visualization: Principles and Practices

Emphasizes the importance of data visualization techniques and practices for effective data analysis and communication.

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Linear Regression: Basics and Interpretation

Introduces the basics of linear regression, interpreting coefficients, assumptions, transformations, and 'Difference in Differences' for causal analysis.

Observational Studies: Pitfalls and Solutions

Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Headline Analysis: Language Impact & Success

Explores the influence of language on headline success through real-world data analysis and statistical testing.

Birth Month Impact on Athlete Success

Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.

Applied Machine Learning: Features and Models

Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.