Lecture

Tools and analysis of big data

Related lectures (32)

Introduction to Machine Learning: Course Overview and Basics

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

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Data Visualization: Techniques and Applications

Explores data visualization techniques, design impact, and interactive applications for effective information communication.

Data Wrangling with Hadoop: Advanced Techniques

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Digital Urban History: QGIS Practical Session

Covers the practical use of QGIS for spatial data analysis and visualization, including georeferencing historical maps and manipulating vector data.

Data Visualization & Storytelling

Delves into data physicalization, expressiveness, feminist visualization, and the balance between exploration and explanation in data visualization.

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.

Collaborative Data Science: Tools and Techniques

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective 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.

Data Science Fundamentals

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

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.