Abstract: Time series forecasting is a crucial aspect of business decision-making, widely used in finance, supply chain management, and production planning. Despite its importance, it remains an under-appreciated technique in data science education, often overshadowed by more popular machine learning methods. This talk aims to address this gap by providing an overview of time series forecasting techniques and their applications in various business contexts.
The talk will cover the Forecaster's Toolbox, including benchmark/simple forecasting methods, techniques for simplifying the forecasting task, and methods for residual analysis to check if the forecasting method has utilized all available information. Participants will learn to use a range of forecasting methods, including ETS, ARIMA, SARIMA, VAR models, and machine learning models such as XGBoost, Random Forest, and SVR.
The talk is intended for graduate students, professionals, and MBA students seeking an introduction to forecasting methods without diving too deep into theoretical details. Participants will develop skills, mindsets, and behaviors sought after in the industry today.
For the talk we require zero familiarity but for a hands on session basic knowledge of python and statistics is required.
Bio: Tanvir Ahmed Shaikh is a highly entrepreneurial and visionary data strategist with a passion for driving business growth through innovative data-driven solutions. With a track record of success in data science and digital transformation, Tanvir has been instrumental in developing and implementing strategies that improve efficiency, quality, and compliance. He possesses strong collaboration skills and effectively communicates technical concepts to non-technical stakeholders.
Currently serving as a Data Strategist (Director) at Genentech Inc., Tanvir leads the digital roadmap for the Global Pharma Manufacturing Quality organization. His expertise in prioritizing digital initiatives, building consensus, and driving change management has resulted in significant positive impacts on the organization.
Tanvir's leadership abilities are exemplified through his role as the Founder and Digital Strategy Lead of the Roche Intrapreneur Network, a global network of over 350+ Roche technologists focused on executive capabilities and experiential learning. Through this network, he fosters a culture of entrepreneurship, product management, and storytelling, encouraging innovation and empowering individuals to think like CEOs of their products.
In his previous role as a Principal Data Scientist, Tanvir spearheaded cross-functional projects, driving operational excellence in forecasting, automation, and AI education. His contributions have led to substantial cost savings and increased efficiency within the organization. Tanvir's passion for education and continuous learning is evident in his role as an Adjunct Professor at Carnegie Mellon University. He teaches courses on Time Series Forecasting in
Python, AI Product Management, and Storytelling with Data, inspiring students to think holistically and take an end-to-end view of problem-solving. He actively promotes a culture of continuous learning, inclusive community building, and inspirational storytelling. Beyond his professional pursuits, Tanvir embraces a diverse range of interests. He finds joy in the culinary arts, experimenting with new recipes and creating culinary delights. Music also holds a special place in his heart, and he enjoys singing and playing the ukulele in his free time. Tanvir's curiosity extends to the financial world, where he actively researches stocks and shares his knowledge, promoting personal finance education. Additionally, he stays active through the sport of tennis, both in competitive settings and for leisure. Tanvir's dedication to data-driven strategies, love for storytelling, and commitment to personal growth and education make him a versatile and accomplished professional. He embodies the values of continuous learning, community building, and innovative thinking, making a significant impact in the field of data science and beyond.