Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
The project orientation is very much unique and it helps to understand the real time scenarios most of the industries are dealing with. And there is no limit, one can go through as many projects... Read More
I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More
Time series forecasting has been one of the important area in data science, it is important to predict a variable associated with time elements such as sales, demand, revenue, profit etc. For logistic and supply chain companies, they need to know the exact inventory they need to stock for that they need to predict the demand for future.
Similarly, people in sales and marketing need to know how much order the customers are going to place so that they can manage their staff. Telecom companies should know how much manpower they need to prepare so that they can handle peak hour traffic etc. In various businesses, at least 5-10 areas where the variable of interest is associated with the time element.
Let’s look at few examples where we can apply various time series forecasting techniques.
Forecast the business for the upcoming years by Exploring Hidden Trends, Calculating Machine Productivity , Extrapolation and Assumptions and Summarizing Answers through Visualizations.
In this project, we will use traditional time series forecasting methods as well as modern deep learning methods for time series forecasting.
In this project, we will build a model to predict the purchase amount of customers against various products which will help a retail company to create personalized offer for customers against different products.