Courses taken at University of Nebraska- Lincoln:


1. CSCE 311: Data Structures and Algorithms (Python; 3 credits)


This is an introductory course to algorithms and data structures for students with diverse background interested in informatics. Foundational coverage of algorithms includes both problems (such as indexing, searching, sorting, and pattern matching) and methods (such as divide-and-conquer, dynamic programming, greedy, network flow and graph theory). Foundational coverage of data structures includes lists, tables, trees, graphs, and multidimensional arrays. Advanced topics will be studied in the context of informatics applications. Specifically, students will learn the fundamental ideas for how to efficiently analyze biological DNA sequence and images, and how to solve challenging problems in general informatics.


2. BIOS 428/828 Perl Programming for Biological Applications (3 credits)


This course teaches the student the basic skills of computer programming, using the Perl programming language. The Perl language is widely used in the biological sciences, and as such this course will use those domains to exemplify concepts learned during this course. Although Perl is especially prevalent in computational biology, bioinformatics, and genomics the course will demonstrate how Perl can be applied to a wider range of biological sciences (from the instructor’s own experience), including but not limited to agronomy, ecology, ethology, and microbiology. It is understood that real expertise in programming comes with a lot of practice: this course will not make the student a highly skilled programmer but will lay a strong foundation in the general concepts and will demonstrate that it is relatively easy to use Perl for the student’s (future) biological research topics where data processing and analysis is required.


Courses taken at South Dakota State University:


3. STAT 541 Statistical Methods II


Simple and multiple linear regressions

ANOVA for one or multiple factors

Design of experiments

Linear models with categorical data

Models with categorical response variable


4. STAT 600 Statistical Programming (R and SAS)


R programming




Dynamic report generation (Sweave/Knitr)

External data sources

SAS programming

Defining and using macro variables and macros

PROC SQL in Macros

Graphing in SAS


5. STAT 601 Modern Applied Statistics I


Introduction to Statistical Graphics and GGplot

Logistic Regression I

Generalized Linear Models

Density Estimation

Recursive Partitioning

Generalized Additive Models and Spline Models

Survival Analysis

Longitudinal Data Analysis and Mixed Models

Multiple Comparisons

False Discovery Rates

Simultaneous Inference



6. STAT 602 Modern Applied Statistics II


Introduction to Statistical Learning

Introduction to Classification

Resampling Methods

Model Selection

“Moving Beyond Linearity”

Tree‐ Based Methods

Support Vector Machines

ROC curves

Clustering/Unsupervised Learning


Courses taken at Microsoft training institute, Nepal (1 semester training):


6. PhP/MySQL


PHP Basics  

Flow Control  


PHP and HTML Forms  

String Manipulation  

Reusing Code and Writing Functions  

Simple SELECTs  

Advanced SELECTs  

Subqueries, Joins and Unions  

Inserting, Updating and Deleting Records  

Managing Data  


Authentication with PHP and SQL  

Regular Expressions  

Session Control and Cookies  

Sending Email with PHP  

File System Management




Understand the development and deployment cycles of enterprise applications.

Utilize the .NET framework to build distributed enterprise applications.

Develop ASP.NET Web Services, secure web services, and .NET remoting applications.

Understand the protocols behind web services.

Understand the 3-tier software architecture (presentation/client tier, application tier, data tier) and develop multi-tier applications.

Understand and experiment with the deployment of enterprise applications.