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This December a new batch of Interns have been enrolled under the Redwood Apprentice Programme (RAP) 

The Interns are students of ATI and were selected after a rigorous assessment process.  They will be undergoing a 3 month internship with Redwood Algorithms where they would be working on different projects. The focus would be on not just improving their Analytics skills, but also developing their presentation skills.  

Congratulations to them, and wishing them the very best! 

Dec RAP Interns.png

From L to R: 

Standing Row 1: Kurian Sebastian, Kaushal Rao (ex Intern, now Consultant at Redwood Algorithms who will be guiding the interns)

Standing Row 2: Chaitali Roy, Rashmi Singh, Priya Xavier, Batul Burhanpurwala

Row 3: Amul Patil


november 2017


In November 2017, consultants at Redwood Algorithms, together with the ATI team conducted a hands on session that involved solving a Kaggle Case Study.  The term "Kaggle" tends to create a block in many Analytics aspirants minds.  "Is this something that I can do?" "Do I know enough?" "How do I get started?" "There are so many variables, what do I do with it?" 

In order to help them overcome their reservations, the RA team selected a dataset from Kaggle which they then gave to the students who attended the session. They were then organised into small working groups, and a set of instructions given on what to do with the given dataset.  Students were asked to apply one of the following techniques to the dataset - Segmentation Modeling, specifically Cluster Analysis and Predictive Modeling, specifically build a Logistic Regression model and run it.

Students were asked to make a presentation of their analysis and then schedule a time with the RA team when they could come and present their findings. 

The presentation sessions were held over one week. Feedback was shared with the students on how to improve their analysis and findings. 

Overall, the session was great way to get students initiated to working on Kaggle, and more importantly get them used to working on varied and large datasets.