15 Data Science Books You Must Read

15 Data Science Books You Must Read

Python for Data Analysis | Intro to Numpy

Exploratory Data Analysis (EDA) Using Python (Jupyter Notebook)

In this video you will learn how to perform Exploratory Data Analysis using Python. We will see how to slice data using Pandas, how to perform computing summary statistics using Numpy and how to visualize data using Matplotlib and Seaborn.



Exploratory data analysis is very use full while building Statistical/Machine Learning models. It helps to understand the structure of the data in order to be able to build a good predictive model

Exploratory Data Analysis (EDA) Using Python (Jupyter Notebook)

In this video you will learn how to perform Exploratory Data Analysis using Python. We will see how to slice data using Pandas, how to perform computing summary statistics using Numpy and how to visualize data using Matplotlib and Seaborn.



Exploratory data analysis is very use full while building Statistical/Machine Learning models. It helps to understand the structure of the data in order to be able to build a good predictive model

Credit Risk Analytics Interview Questions and Answers

In this video you will learn about 50 very important credit risk modelling interview questions and their answers.



To learn credit risk modelling (Development of POP, PD, LGD, EAD models, Model validation, Stress testing, Back testing). Contact : analyticsuniversity@gmail.com



Credit Risk Analytics Study packs: http://analyticuniversity.com/credit-risk-analytics-study-pack/



Some of the credit risk modelling questions discussed in the videos (and many more):



1- What were the main objectives of Basel 1

2-What were the main objectives of Basel 2

3-What is Capital Adequacy ratio

4-What are tier 1 & tier 2 capital

5-How does IFRS9 effects credit loss modeling?

6- What is CCAR?

7- What is PPNR?

8-What are the objectives of credit rating model?

9-What are LCR & NSFR

10-What is the difference between Expected loss and Unexpected loss

11- What is the main difference between wholesale & retail banking?

12- How do we test for multicollinearity

13-How do you deal with autocorrelation?

14-How do you deal with Heteroskedasticity??

15-What are the metrics used for model monitoring?

16-What are the aspects of model risk?

17-Guidelines for model development

18-What are the different aspects of Model Validation?

19-What are the aspects of model audit?

20-How do you perform back testing?

Data Science Projects in Insurance Industry

Insurance industry has been using quantitative research for a very long time.  Actuaries have been using statistical modelling to price insurance products and quantify risk for decades. But with advances in the data sciences, insurance industry is getting further disruptive. Here you will learn what are the projects that you can do in the insurance industry using data science



1- Consumer Targeting

2- Risk based pricing

3- Better claim management

4- Customer retention

5- Fraud Detection

6- Automate Under writing

7- Cyber Crime