Welcome to a hands on SAS analytics course. In this video we will give you a brief overview of the course curriculum. This course consists of eight modules. The first module is the introductory module where you will get a brief idea about my knowledge and experience who can take the course etc. Coming to the next module is Introduction to Business in analytics, where you will get enough knowledge about what is business analytics that is what business analytics exactly is, and what are the main categories of business in analytics. Next comes the third module of chapter three, which is all about linear regression.

We will cover what linear regression exactly is classical linear regression model their assumptions multicollinearity and autocorrelation chapter four, we will do our first custom project using linear regression based on Shop stop customer satisfaction data. We will also give you the data set and codes used in this capstone project. So basically we'll be doing the practical session on linear regression in chapter four chapter five is all about logistic regression or odds ratio concordant pairs, discordant based type space confusion matrix and all the theoretical concepts will be covered in this chapter, we will be covering all these concepts one at a time, step by step in chapter six, we will do our our second capstone project that is credit risk analytics using logistic regression. In our seventh chapter, we will be discussing about time series forecasting, we will discuss about it this analysis components the smoothing techniques stationarity and non stationarity Arima modeling box Jenkins technology etc.

Chapter Eight which is the last module is the practical session of time series forecasting where we will use time series forecasting on the Jet Airways ln data to forecast the number of passengers that will be traveling through Jet Airways for the coming 12 months. Now let's discuss about the benefit of this aspect modeling program. That is, what are the advantages or what are the benefits that y'all will get with this course. So SAS high performance procedures provides predictive modeling techniques that has been specifically developed to solve big data problems. This course gives an overview of all SAS rated modeling solutions, and specifically introduces the functionality in the SAS high performance statistics for predictive modeling. The course shows examples of applying advanced statistics to huge volumes of data and draw specific interpretations out of it.

Now, there are certain purposes of predictive modeling. The first is creating future values that is when you have when we are forecasting the future values based on the present data. Next, that is the second point to identify statistically statistically significant variables that is to identify which of the variables are significant for our model, that is the to identify those significant variables. That will be useful for us to predict the future values. So that is to identify statistically statistically significant variables. And the third purpose is to take decision at the level of an individual client, customer etc that is based on the predicted values to take a proper decision from the point of view of an client, or customer or any other person or any other profession.

So this course, that is aspected modeling is best suited for candidates having prior knowledge of basis. It isn't suitable for beginners but must for intermediate SAS users, professionals who are interested to do different forecasts from their data can go for this course this course will help them to build up different predictive models based on their data and focus future balance that is people who have got prior knowledge of basis that is, who knows basis should go for this course because this course is application of basis and intimated SAS users that is who has already worked in SAS should go for this course. And people who have And objective to discover insights from the data to apply different predictive modeling techniques and to build different practice models based on their data to do different sorts of forecast using the data should go for this cost that is they should go for this course.

So, goodbye. Thank you see you all for the next video.