 Friends, let me give introduction to the course probability and statistics. In earlier times, the term chance was attributed to our ignorance to describe a certain physical or biological phenomenon. So, for example, if we did not follow something or something happened which is beyond our understanding, then we will attribute it to our ignorance that we are not able to fully analyze the corresponding physical model. For example, unexpected rainfall or some unnatural event such as earthquake etcetera. However, today chance or randomness is considered to be inherent in nature. Therefore, statistical methods are used in all branches of science, engineering, humanities and social sciences. The foundations of statistical methods are based on probability and distribution theory. So, in this particular course, we have designed in such a way that we give a very good introduction to the foundations of probability. We tell about the classical definitions of the probability, the relative frequency definition of the probability and the modern axiomatic definition of probability and the corresponding rules that have been developed for calculation of probabilities of various events. Then we will introduce the concept of random variables, distributions, the concept of moments, generating functions etcetera. And then we will study important discrete and continuous distributions. The important discrete and continuous distributions include say binomial distribution, Poisson distribution, geometric distribution, hypergeometric distribution, exponential distribution, gamma distribution, normal distribution, Pareto distribution or double exponential distribution etcetera. We will study that how this from various natural events are where these distributions originate, then we will study their properties and we will look at the applications of these distributions to various areas of science and engineering. Further, we will introduce the concept of joint distributions, what is the jointly distributed random variables. In particular, we will study bivariate normal distribution and its properties. Further, we will introduce the concept of data or data analysis. So, because most of the real world things happen or happen to deal with the data. So, we will talk about various descriptive statistics that are used to describe the data and in particular the functions of the sample. So, what is the concept of sampling, random sampling and the distributions of the statistics which are called sampling distributions. As a consequence of this, we introduce the problem of statistical inference. Now, the problem of statistical inference is very wide, there are so many aspects of it. In this particular course, we will take up three important parts of statistical inference namely the theory of point estimation, the methods of constructing confidence intervals and various methods of testing of statistical hypothesis. And we will spend sufficient time on various aspects of this. So, this course will enable students of various disciplines in science and engineering to understand the basic nature of probability and statistics. They will be able to effectively apply statistical methods in their areas of work, whether it is a practical work, organizational work or it is a research work. The work could be related to financial mathematics, stochastic finance, business analytics. The people who are working in the manufacturing industry where they do quality control studies, the people in the say bio-logy or biomedical engineering or genetics, in all these areas statistical methods are unavoidable, we have to use them. Even the people who do cognition studies, psychology, they also have to use statistics. So, this course will be very helpful to the students of this. In the end, I must say that in the context of use of statistics or stochastic methods, we have come a long way from the days of Einstein, who is quoted to have famously said, God does not play dice with the universe. From there, we have come to the modern area or modern physics or that is quantum mechanics, which considers randomness is inherent and indispensable part of the normal functioning of molecules. It is interesting to note that Einstein himself accepted the random behavior of molecules as suggested by physicist Satyendra Nath Bose. This resulted in Bose-Einstein particles called Bosons. Historical development of most of the modern statistical methods was initiated by statisticians such as Carl Pearson, R. A. Fisher from England, Georgie Neyman, E. S. Pearson, P. C. Malanobis from India, C. R. Rao, R. C. Bose, W. E. Deming and various other statisticians. In fact, there is a rich legacy in Indian context also, where the foundations of statistical methodology were developed in Indian Statistical Institute. I hope you will enjoy and learn much from this course and will be able to effectively use these tools in your areas of work. Thank you.