Big Data and Social Sciences: The Need for Big Theory

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Introduction: What do we mean by Data Science?

Introduces the team, provides a crash course on Python, and explores the journey into Data Science and the importance of refining data.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Digital Transformation: Solutions and Data

Explores digital transformation opportunities, big data, analytics, and technology innovations in business and research.

Excel Upgrade: Advanced Functions and Data Analysis

Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.

Air Pollution Data Analysis

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Super-resolution Network Analysis: SuperNet

Introduces SuperNet, a software for super-resolution network analysis and quantification of single molecule clusters, covering motivation, challenges, methodology, features, and proposed methods.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Online Dating Data: Insights into Partner Preferences

Examines how online dating data provides insights into partner preferences and matching mechanisms.