DataScience Hands-on Workshop
The Data Science Workshop was hosted by the IEEE Education Society CBIT chapter. This transformative event was conducted by Pranali Bose, a Machine Learning engineer and mentor, and marked the second edition organized by the IEEE Education Society, showcasing a commitment to advancing knowledge and skills in the field of data science. The workshop was attended by students from 2nd and 3rd year and the minimum requirements was knowledge on the basics of machine learning.
Throughout the Data Science Workshop conducted by Pranali Bose, participants delved into a comprehensive exploration of various key topics crucial to mastering the data science landscape. The workshop commenced with a focus on the foundational aspect of problem definition, understanding the significance of laying a solid groundwork for any data-driven endeavour. Charting the course, participants navigated through the intricacies of data collection and preparation, acknowledging the critical role these stages play in ensuring the quality and relevance of the data under scrutiny. The expedition continued with an emphasis on Exploratory Data Analysis (EDA), unravelling insights and patterns inherent in the datasets. Navigating the terrain of feature engineering, attendees learned to enhance the representational power of their models. The workshop then transitioned to the pivotal decision-making phase of model selection and development, followed by testing the waters through comprehensive model evaluation and validation. Finally, participants explored the concluding phases of reaching the destination, focusing on model deployment and implementation, thus equipping themselves with the skills and knowledge necessary for successfully translating data science concepts into real-world applications.
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DataScience workshop
Pranali Bose is a results-driven IT professional with over 5 years of experience, specializing in backend development, database management, and data science. With a proven track record of collaborating with cross-functional teams, Pranali excels in developing innovative solutions for diverse industries, from retail to finance. She is skilled in data manipulation, exploratory data analysis, feature engineering, and both supervised and unsupervised machine learning. As a lifelong learner, Pranali stays abreast of the latest industry trends and technologies, ensuring her work remains at the forefront of innovation.
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IEEE EdSoc CBIT DataScience Workshop | 549.54 KiB | |
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