Final Session 2 of 2
Guest Speaker: Ekta Aggarwal
Bio/Profile:
Ekta Aggarwal is a Data Scientist who has worked at Ernst & Young, McKinsey & Company, Evalueserve, and a prestigious consulting firm. She has worked in the supply chain, telecom, retail, healthcare, website, and people analytics domains. She has been training students and professionals in statistics and data science using various analytical tools namely, R, SQL, Python, Tableau, Power-BI, MS – Excel, SAS, and Data Science concepts for over 5 years. She has completed a Master's in Data Science from the University of Leeds, and a Master's and Bachelors in Statistics from India.
Reference materials can be found at her website at: https://www.analyticsisnormal.com/
Presentation:
BA Tools and Techniques - a series of hands-on workshops for Business Analysts
Ekta Aggarwal will be presenting two more sessions that will introduce participants to Machine Learning to make it approachable and to understand how and when Machine Learning is used, and as a basis for further learning, if desired.
Learning Objectives:
In this training, we will be covering 2-3 real-life problems from the retail and banking domain which will involve:
1. Retail Clustering Case Study: Using the transactional data set, with data manipulation and K-Means clustering we will segment the customers into various categories.
2. Loan Prediction Case Study: To predict whether a bank should give loans to its customers, we will leverage Random Forests to build an ML (machine learning) model which will involve data manipulation, missing value imputation, and data preparation.
Get ready for this session:
This will be a hands-on, interactive working session, where attendees can code along with the presenter in Python. To get the most out of the session, Ekta encourages attendees to install anaconda; software (https://www.anaconda.com/products/distribution) in advance of the session, which automatically has Python loaded in it.
Moreover to avoid any last-minute surprises check also that you are able to open Python Jupyter Notebook after installation.
Register for the event via Zoom: HERE!
If you missed Session 1 on March 20th, then you are welcome to review March's recording (TBA).