Data Analytics Challenge 1 – The Shady Gambler This is the final part of the first data analytics challenge on YOU CANalytics. You could find the previous parts at these links: Part-1 & Part-2. As mentioned earlier, these challenges require your participation for the investigation to move ahead. You had some really good discussions in the previous parts: Discussion Part 1 &
Please read the disclaimer about the Free PDF Books in this article at the bottom The one thing they love more than a hero is to see a hero fail, fall, die trying. In spite of everything you’ve done for them, eventually, they will hate you [Spider-Man]. – Green Goblin / Norman Osborn Batman v
A couple of weeks ago I got an opportunity to be a part of a panel discussion at ‘The Economic Times Business Analytics Summit‘. The topic of the discussion was ‘overcoming the challenges of bringing data science to the boardroom’. The panel had a well-balanced representation from both industry and academia. It was an interesting and thought-provoking discussion. Later I thought about
Sometimes answers lead to more questions. In data science this sometime is true almost everytime. Analytics Challenge – The Shady Gamble In the previous article, you were approached by Scotland Yard to investigate charges against a gambler with dubious character. The allegation against him was that he had loaded his die so that some numbers
Data! data! data!” he cried impatiently. “I can’t make bricks without clay. – Sherlock Holmes In this article, we are starting a new series on YOU CANalytics that can’t proceed without your participation to solve the data analytics mysteries. In the beginning of each article of this series, you will get a problem that needs you
Over the years in my career in data science and predictive analytics, I have noticed some awful practices that young and sometimes seasoned analysts follow. These bad practices, I believe, throw careers of these data scientists on a collision course similar to the Titanic. I will present the six worst mistakes that I feel are at the
So far we have covered the following topics in this case study example on time series forecasting and ARIMA models: Part 1 : Introduction to time series modeling & forecasting Part 2: Time series decomposition to decipher patterns and trends before forecasting Part 3: Introduction to ARIMA models for forecasting Part 4: ARIMA model case study example
This article is a continuation of our manufacturing case study example to forecast tractor sales through time series and ARIMA models. You can find the previous parts at the following links: Part 1 : Introduction to time series modeling & forecasting Part 2: Time series decomposition to decipher patterns and trends before forecasting Part 3: Introduction to ARIMA
For the last couple of articles, we are working on a manufacturing case study to forecast tractor sales for a company called PowerHorse. You can find the previous articles on the links Part 1 and Part 2. In this part, we will start with ARIMA modeling for forecasting. ARIMA is an abbreviation for Auto-Regressive Integrated Moving Average. However,
In the previous article, we started a new case study on sales forecasting for a tractor and farm equipment manufacturing company called PowerHorse. Our final goal is to forecast tractor sales in the next 36 months. In this article, we will delve deeper into time series decomposition. As discussed earlier, the idea behind time series decomposition is to extract