Statistics Scope

There are a variety of M.Sc. Statistics job opportunities available such as Statistician, Investment Analyst, Arithmetician, Cost Estimator, Service Estimator both in government and private sector. LPU offers M.Sc. Statistics that aims at brewing the scientist by provoking the doing by yourself approach using various statistical tools and techniques. The learner has to do project work keeping in mind the research centricity, for which the programme has been chosen. The eligibility for the programme is Pass with 55% aggregate marks in Bachelors degree (any discipline) (with Statistics or Mathematics as a subject) or B.Tech. Or B.E. Or B.Sc. (Engineering) or equivalent, subject to qualifying LPU NEST. The M.Sc. Data Analytics programme at LPU is what it aims at equipping the student with - DISTRIBUTION THEORY, FUNDAMENTALS OF RESEARCH, INTRODUCTION TO DATA MANAGEMENT, LINEAR Algebra REAL ANALYSIS, SURVEY SAMPLING, DATA Science TOOLBOX : R PROGRAMMING, INFERENCE-I, LINEAR MODELS AND REGRESSION ANALYSIS, LINEAR MODELS AND REGRESSION ANALYSIS LAB, PATHWAY ELECTIVE 1, Probability RESEARCH METHODS AND DESIGN, DATA VISUALIZATION, DESIGN AND ANALYSIS OF EXPERIMENTS, DESIGN AND ANALYSIS OF EXPERIMENTS LAB, MULTIVARIATE ANALYSIS etc.

Indian Statistical Institute is the best place for someone who wants to pursue his careers in Statistics and to get into it ISI MStat Entrance should be cleared. The Syllabus consists of two parts:
In Mathematics, you must be quite fluent in the following:
High School Mathematics
Linear Algebra
Real Analysis
Linear Algebra is the way to deal with data - starting from Least Squares to ANOVA. Linear Algebra is the tool to formalize the theory of easy models. The easy models are essentially Linear in Nature. That's why you study linear regression first not quadratic regression. Real Analysis is a way of dealing with functions. It is essentially exaggerated Calculus, to deal with complicated functions and how they behave. The functions are used to describe how the data behaves. This is a very simplistic view, and don't get along with it too much. Statistics consists of the following broadly:
Probability Theory is the mathematics of chance and errors. Being a statistician in the making, you must learn the art of calculating chances. Descriptive Statistics is noobie statistics. Period. It just tells you stories but important, like Simsons Paradox. Regression gives you the first flavor or how to do mathematics with data. You must grasp and fall in love with the way it is developed in terms of modeling the data. It is beautiful if properly understood and taught. All the best. The Theory of Estimation as you will understand is the way of estimating parameters of some distribution, which is used to model the chance or error in the data by the use of Probabilistic Distributions. It is a step jump in the philosophy of attacking the problem of dealing with data. You must understand the philosophy well as well as mathematics. Sample Survey is the mathematics of collecting data. It is very boring if taught in a normal way. You must understand it yourself. The mathematics involved here is very easy if you are comfortable with probability. There are really beautiful ideas to grasp here if properly taught. I tried to give you a brief idea of the syllabus, to know how to prepare, you can visit this: https://www.cheenta.com/isi-mstat-entrance-is-not-just-an-examination-how-to-prepare-for-it/
All the best!
Thank you.