Ahmed Sherif

Ahmed Sherif

Data Scientist

Biography

Ahmed Sherif has been working in the business intelligence field for over 10 years. He has both an engineering and a business background, which helped him in his first job as a data analyst. Understanding business needs and translating them into technical requirements became second nature. Ahmed started digging into the backend SQL of business intelligence tools such as SAP BusinessObjects, where he started to understand the underlying data model behind the business layout. He used these skills build dashboards and data visualization applications as a consultant for customers who were in need of something more than just spreadsheets. As a business intelligence consultant, Ahmed has had the opportunity to work with customers from all back end data types. He found a common theme across all of their needs. If the model for the data warehouse is poorly architected on the backend, then it doesn’t matter how much technology on the fronted is spent to build a productive business intelligence application. Ahmed has made it his focus to help customers develop useful visualizations from their data. In 2016 he competed a Masters in Predictive Analytics from Northwestern University, where he focused on machine learning and predictive modeling techniques using SAS, R, and Python. As a data scientist, Ahmed strives to fuse predictive capabilities into business intelligence solutions so that organizations can leverage their data to understand the past as well as the future. He is fascinated by anything data visualization related, especially when it involves politics and sports.


Talk: How positive are your tweets? Sentiment analysis with python.

Ever wonder if tweets that are on your time line are trending positively or negatively? Would you like to be able to see in real time if this is happening to you or to a specific topic or hashtag? This talk will show you how you can stream your tweets using a library called ‘tweepy’, store them in a data frame using ‘pandas’, and then extract subjectivity and polarity using textblob. Additionally, this talk will show you how this can be done with python with minimal lines of code compared to this same function being performed with R or SAS. After walking through the steps, we will perform an actual live demo of capturing tweets using audience participation.

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