Are you preparing for a data science job interview? To help you, in this article I will explore some of the most common techniques used by data scientists to select their future colleagues. Additionally, I will also share many sample questions for data science job interviews and suggest a few strategies to prepare. Moreover, please post your
Welcome back to our retail case study example for campaign and marketing analytics. So far in this case study, we were working on a classification problem to identify customers with a higher likelihood to purchase products from the campaign catalogues. In the last article on model selection, we noticed that artificial neural networks performed better than logistic regression, and decision tree
Model Selection This is a continuation of our retail case study example for campaign and marketing analytics. In the previous two parts, we discussed a couple of decision tree algorithms (CART and C4.5) for classification. Recall a previous case study example on banking and risk management where we discussed logistic regression which is another approach to solving classification
Analytics Lab – R Welcome back to Analytics Lab on YOU CANalytics! In our last article, we learned the procedure to visualize risk across a parameter (age groups) in Excel. In this part we will generate the same visualization in R. This lab exercise is a part of the banking case study we have previously
Analytics Lab Malcolm Gladwell in his book ‘Outliers‘ says that it takes approximately 10,000 hours of training or practice for anybody to master a subject. There is nothing in the world that could beat hands-on practice for learning. Keeping this in mind let me start a new series on YOU CANalytics called ‘Analytics Lab’ where
One of the crucial decisions while doing data analysis is an appropriate choice of statistics software and language. In this article, I am going to analyze and help you choose the right data mining and statistics software for your purpose. I have used the following 12 software / languages (tools) with different levels of experience
This is a continuation of the banking case study for the creation of application risk scorecards that we have discussed in some previous articles. You can find the other parts of the series at the following links (Part 1), (Part 2), (Part 3), (Part 4), and (Part 6). Reject Inference is a topic that separates credit
This is a continuation of our banking case study for scorecards development. In this part, we will discuss information value (IV) and weight of evidence. These concepts are useful for variable selection while developing credit scorecards. We will also learn how to use weight of evidence (WOE) in logistic regression modeling. The following are the
Click on the titles to read the articles – Machine Learning Institution || Optimization || Gradient Descent and Regularization || Cross-Validation
A Case for Wise Models Lehman Brothers, a financial services company, filed for bankruptcy on 15th September 2008 – exactly five years from today. A company founded in 1850 disappeared overnight. The chain of events to follow brought the overall financial system across the world to its knees – hence started the biggest economic crisis