পরিসংখ্যানে কি কি পড়ানো হয়?

একাডেমিক লাইফ আসলেই অনেক কষ্টকর। প্রথমত থিওরিটিক্যাল ব্যাপার নিয়ে আলোচনা করা যাক যা আমি ৪ বছরে পড়েছি।

প্রথম বর্ষঃ

• Basic Statistics (Introduction to Statistics, Statistical terms and basic concepts, Data collection, Presentation of Data, Measures of Location, Measures of Dispersion)
• Elementary Probability(Introduction of Set theory, Algebra of sets, Combinatorial methods, Approaches of defining probability)
• Numerical Mathematics(Different Operators, Interpolation and Extrapolation, Numerical Differentiation, and Integration)
• Algebra, Geometry, Geometry of Three Dimensions, and Trigonometry
• Principle of Economics(Demand and utility, Supply, and Firm, Production, Theory of Distribution)
• Fundamentals of Computer
• Principles of Statistics (Relationship Between Variables, Analysis of Attributes, Probability Theory, Random Variables, Mathematical Expectation, Jointly Distributed Random Variables, Moments and Moments Generating Functions)
• Matrix and Vector Analysis( Vectors, Matrices, Solution of Linear Equation, Quadratic Forms, Eigenvalue, and Eigen-vectors)
• Differential Calculus and Integral Calculus
• Mathematical Economics

দ্বিতীয় বর্ষঃ

• Probability Distributions (Univariate Discrete Distributions)
• Basic Demography(Population Characteristics, Analysis of Demographic Events, Life Table Analysis, Population Projection, and Estimation)
• Statistical Methods(Test of Hypothesis)
• Real Analysis and Complex Variables (Classifications of Numbers, Riemann-Stieltjes Integrals, Improper Integral, Complex Number, Complex Functions, Complex Integration)
• Economic Statistics and Educational Statistics(Economic Statistics, Law of Income and Wealth Distribution, Educational Statistics)
• Computer Programming(Programming, Programming in C, Sequential Structure, Selective Structure, Data Analysis with R)
• Probability Distributions (Univariate Continuous Distributions, Bivariate Distribution, Multinomial Distribution)
• Mathematical Demography(Fertility, Life table, Population Projection and Estimation)
• Differential Equations and Fourier series(First-Order Differential Equations, Higher-Order Differential Equations, Laplace and Inverse Laplace Transforms)
• Regression and Modeling(Review of Simple Linear Regression, Checking the Straight Line Fit, Multiple Regression, Selecting the Best Regression Equation, Polynomial, and Non-linear Regression)
• Analysis of Variance(ANOVA, ANCOVA)
• Sampling Distribution(Transformation of Variables)

তৃতীয় বর্ষঃ

• Statistical Inference — I(Estimation, Methods of Point Estimation, Methods of Interval Estimation)
• Time Series Analysis(Stationary and Non-stationary Processes, Index Numbers, Lagged Variable Models)
• Stochastic Process(Markov Chains, Homogeneous Markov Processes)
• Sample Survey(Simple Random Sampling, Nonprobability Sampling)
• Basic Design of Experiments( Design of Experiments, Detailed Study of Basic Designs, Factorial Experiment, Incomplete Block Design, Confounding, Linear Estimation)
• Epidemiology(Types of Epidemiologic Studies, Measures of Disease Frequency, Epidemiology, and Prevention, Screening, Properties of a Screening Test)
• Econometrics(Classical Linear Regression Model, Violations of Assumptions, Categorical Variable Techniques, Panel Data, Simultaneous Equation Models, Time Series Econometrics)
• Biostatistics(Biostatistics and Scope, Non-parametric methods, Inference Procedures for Exponential Distributions, Inference procedures)
• Order Statistics and Non-Parametric Test
• Sampling Techniques(Cluster Sampling, Systematic Sampling, Stratified Random Sampling)
• Simulation and Modeling(Probability Simulation and Monte Carlo Method, Verification and Validation of Simulation Models, Analysis of Simulation Data, Simulation Case Studies)

চতুর্থ বর্ষঃ

• Multivariate Analysis(Multivariate Methods, Principal Components Analysis, Factor Analysis, Independent Component Analysis, Canonical Correlation Analysis, Classification, Clustering)
• Statistical Inference — II(Point Estimation, Interval Estimation)
• Robust Regression and Diagnostics(Quantile Regression)
• Bioinformatics(Introduction, Bioinformatics Databases, Genetic Linkage Analysis, Marker Analysis of Phenotypes, Gene Expression Analysis)
• Advanced Sampling(Sampling with Varying Probabilities, Two Stage and Multistage Cluster Sampling, Double Sampling, and Multiphase Sampling, Non-Sampling Errors )
• Operations Research and Quality Control(Introduction, Inventory Model)
• Linear Programming(Methods of Linear Programming Problems, Transportation and Assignment Problems, Game theory)
• Research Methodology(Measurement and Scaling, Research Design, Questionnaire Design, Report Preparation and Presentation, Research Proposal)
• Actuarial Statistics( Fundamentals of Theory of Interest (In discrete time, Construction of Actuarial Tables)
• Statistical Data Mining(Exploratory Data Analysis, Association Rules, K-Nearest Neighbor Algorithm, Decision Trees, Neural Networks, Kohonen Networks, Model Evaluation Techniques)
• Environmental Statistics(Understanding Environmental Pollution, Stochastic Process in the Environment, Environmental Sampling, Detectability of Sampling, Diffusion, and Dispersion of Pollutants, Dilution of Pollutants)

এবার আসি প্রাক্টিক্যাল জিনিস প্ত্র নিয়েঃ প্রাক্টিক্যালি আমাদের কে যে টুল গুলো নিয়ে কাজ করা লাগছে;

R programming***

Python*

SPSS***

Excel**

STATA*

EVIEWS*

Google Form*

এবার আসি রির্সাস এন্ড পাব্লিকেশন নিয়ে;

ভার্সিটি তে আসলে থিওরিটিক্যাল পড়াশোনার পর খুব কম ছাত্রছাত্রী ই গবেষেনা করার সুযোগ পান। তবে ব্যাক্তিগত চেষ্টায় অনেকে করে থাকেন। আমি এমন ও অনেকেই দেখেছি যারা অর্নাস লাইফেই ১০+ পাব্লিকেশন করেছেন সেটাও আবার টপ জার্নালে। সুতরাং অসম্ভব বলতে কিছুই নেই।

এছাড়া ও ফিল্ড ওয়ার্ক, সার্ভে, ডাটা কালেকশন এসব তো রয়েছই। ধন্যবাদ এত বড় পোস্ট টি পড়ার জন্য।