General Introduction to Big Data

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Data Science Essentials: Python, Numpy, Pandas, and Scikit-learn

Covers the essentials of Data Science using Python, Numpy, Pandas, and Scikit-learn, including DNA sequence analysis and classification.

Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Integrating Scalable Data Storage and Map Reduce Processing with Hadoop

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.

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.

Introduction to Data Stream Processing

Covers the fundamentals of data stream processing, including tools like Apache Storm and Kafka, key concepts like event time and window operations, and the challenges of stream processing.

Advanced Spark Optimization Techniques: Managing Big Data

Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.

Introduction to R Programming for Genetics & Genomics

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

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Water Consumption in Geneva

Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.