We concluded our CyberFest tech-focused bootcamp series with the Big Data and Data Science Bootcamp. Mohammad Tibi, kicked off the day with the “Data is the New Oil” session where he discussed several aspects in the field of data science with the participants. From structured and unstructured, categorical and numerical data types. He also explained the real meaning of data science as it encompasses a plethora of principles, subjects, algorithms, and processes for extracting patterns from large data sets. Then Fahed Khutaba delivered a session “Big Data in Practice” where he started by explaining what big data means and providing real examples of big data from different industries like HP Indigo and KLA. Then, he gave deeper knowledge on Hadoop ecosystem including the file systems, data processing, Spark architecture, and Apache projects. Fahed explained why learning to work within the Hadoop ecosystem is important for data analysts, data engineers, and developers to easily read, write and manage files in the Big Data Space. Mohammad Tibi examined how the data science field can be incorporated in each major business, in industry, and in academic research. he focused on the subject of bioinformatics and computational biology, as he explained with several practical examples the concept of interdisciplinary knowledge. Mona Shaheen presented a workshop on Spark, where she started with an introduction to spark, it’s components and features, then we explained about the spark architecture, spark driver & spark executors, then we did an interactive coding of spark transformations and actions, wide & narrow transformations.


On the second day of the Big Data and Data Science Bootcamp, Firas Shama, the co-founder, and CTO of VRee.AI, delivered two fruitful sessions, one of which was an “Intro to computer vision”. During this session, Firas dived deeper into the concept on computer vision, and then went on to explain deep learning, from semantic segmentation to text to image generation, image enhancement, style transfer and many more topics. Firas also explained the challenges to train & handle the big data (data biases, data labeling, and in his second session, he delivered a hands-on training on Neural Networks. Layla Abu Khalaf, the Data Analyst at Antidote Health presented a workshop using Python and SQL on a real-life project preprocessing. She explained the preprocessed data steps and presented how it affects the model results. Layla helped the participants learn the best practices for organizing, summarizing, and interpreting quantitative data, creating a repeatable process for analyzing your data, bringing out patterns in data that were not apparent at first glance, identify and apply tools for data analysis, and recognize the value of using data as a Strategic Model (DASA) to address business questions and inform decision-making.