Program Outcomes (POs):

Students taking admission to this program of B.Sc. are expected to get equipped with following outcomes:
a) To find mean deviation about mean and median,
b) To find variance and standard deviation,
c) To find coefficient of variation and analyze the data
d) To find sample space,
e) To identify between Exhaustive events and mutually exclusive events,
f) To find probability of an event for simple problems.
g) The problem and mathematical formulation, Applications in daily life
h) To find probability using conditional probability formula,
i) To identify and solve problem by Bayes’ theorem,
j) To find probability distribution of different random variables,
k) To identify and solve problem by using Bernoulli trials

Program Specific Outcomes(PSOs):

a) Application of statistics in various walks of life.
b) Ability to apply various statistical tools to research problem.
c) Understanding how to collect, present, analyze and interpret the data.
> d) Ability to analyze the data by using MS-Excel, Open Source Software
e) Knowing the statistical organization in India and Abroad.
f) Ability to build statistical knowledge.
g) Application of various distributions to real life situation.

Course Outcomes (COs) :

After completion of B.Sc. I Statistics program the student will be able to:
1. To compute various measures of central tendencies, dispersion, moments, skewness, kurtosis and to interpret them. 2. To analyze data pertaining to attributes and to interpret the results.
3. To compute correlation coefficient, interpret its value.
4. To compute regression coefficient, interpret its value and use in regression analysis.
5. To compute various index numbers. <
6. To distinguish between random and non-random experiments.
7. To find the probabilities of various events.
8. To understand concept of conditional probability and independence of event.
9. To apply discrete probability distributions studied in this course in different situations.
10. Distinguish between discrete variables and study of their distributions.
11. Know some standard discrete probability distributions with real life situations
12. Understand concept of bivariate distributions and computation of related probabilities.

After completion of B.Sc. II Statistics program the student will be able to:
1 Understand concept of discrete and continuous probability distributions with real life situations.
2 Distinguish between discrete and continuous distributions.
3 Find the various measures of random variable and probabilities using its probability distribution.
4 Know the relations among the different distributions.
5 Understand the concept of transformation of univariate and bivariate continuous random variable.
6 Know some standard continuous probability distributions with real life situations.
7 Distinguish between various continuous distributions.
8 Find the various measures of continuous random variable and probabilities using its probability distribution.
9 Understand the relations among the different distributions.
10 Understand the Chi-Square, t and F distributions with their applications and inter relations.
11 Understand the concept of Multiple Linear Regression.
12 Understand the concept of Multiple Correlations and Partial Correlation.
13 Know the concept of sampling theory.
14 Understand the need of vital statistics and concept of mortality and fertility
15 Know the concept and use of time series.
16 understand the meaning, purpose and use of Statistical Quality Control, construction and working of control charts for variables and attributes
17 Apply the small sample tests and large sample tests in various situations.

After completion of B.Sc. III Statistics program the student will be able to:
1. Knowledge of important univariate distributions such as Laplace, Cauchy, Lognormal, Weibull, Logistic, Pareto, Power Series Distribution.
2. Knowledge of Multinomial and Bivariate Normal Distribution.
3. Knowledge of Truncated Distributions.
4. Information of various measures of these probability distributions.
5. Acumen to apply standard continuous probability distributions to different situations.
6. knowledge about order statistics and associated distributions
7. concept of convergence and Chebychev’sinequality and its uses
8. concept of law large numbers and central limit theorem and its uses.
9. knowledge of terms involved in reliability theory as well as concepts and measures.
10. Knowledge about important inferential aspect of point estimation.
11. Concept of random sample from a distribution, sampling distribution of a statistic, standard error of important estimates such as mean and proportions. 12. knowledge of various important properties of estimator,
13. Knowledge about inference of parameters of standard discrete and continuous distributions.
14. Concept of Fisher information and CR inequality.
15. Knowledge of different methods of estimation.
16. Knowledge of interval estimation of mean, variance and population proportion.
17. Knowledge of important aspect of test of hypothesis and associated concept.
18. Concept about parametric and non-parametric methods.
19. Knowledge of some important parametric as well as non–parametric tests.
20. Concept of interval estimation.
21. Knowledge of basic terms used in design of experiments.
22. Concept of one-way and two-way analysis of variance.
23. Knowledge of various designs of experiments such as CRD, RBD, LSD and factorial experiments.
24. Knowledge of using an appropriate experimental design to analyze the experimental data.
25. Importance of R- programming
26. Knowledge of identifiers and operators used in R.
27. Knowledge of conditional statements and Loops used in R.
28. Knowledge of quality tools used in Quality management.
29. Knowledge of process and product control used in Quality management.
30. basic knowledge of complete enumeration and sample, sampling frame sampling distribution, sampling and non-sampling errors, principle steps in sample surveys, sample size determination, limitations of sampling etc.
31. concept of various sampling methods such as simple random sampling, stratified random sampling, systematic sampling and cluster sampling.
32. An idea of conducting sample surveys and selecting appropriate sampling techniques.
33. Knowledge of comparing various sampling techniques.
34. Knowledge of ratio and regression estimators.
35. Concept of Linear programming problem
36. Knowledge of solving LPP by using graphical and simplex, big - M method.
37. Knowledge of Transportation problem, Assignment problem and sequencing problems.
38 Concept of queuing theory
39. Knowledge of simulation techniques and Monte Carlo technique and apply these in real life situations.

Photo	Name	Education	Designation	Profile
	Mr.Prakash Sadashiv Chougule	M.Sc	Associate Professor	View
	Dr.Tejaswi Satyajit Kurane	M.Sc,Phd	Assistant Professor	View
	Miss.Gitanjali Amol Gadhari	M.Sc	Assistant Professor	View
	Mr.Shubham Suresh Kamble	M.Sc	Assistant Professor	View

Old Faculties

Photo	Name	Education	Designation	Profile
	Mr.Ananda Vasant Powar	SET,M.Sc	Assistant Professor	View
	Miss.Anjali Sanjay Bhosale	M.Sc	Assistant Professor	View
	Miss.Shaeen Rahimbaksh Mujawar	M.Sc,SET	Assistant Professor	View
	Miss.Sonam Ashok Amate	SET,M.Sc	Assistant Professor	View
	Miss.Puja Arun Pawar	M.Sc,SET	Assistant Professor	View
	Miss.Sharmili Sanjay Patil	M.Sc	Assistant Professor	View
	Miss.Pallavi Shivaji Patil	M.Sc	Assistant Professor	View

Yearwise List