Biological Data Analysis (CUBS2549)

Teacher

Dr. Polaki Suman

Category

Core Courses

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Course code

Course Name

(T+PR+PP)

CUBS2549

BIOLOGICAL DATA ANALYSIS

(0+1+1)

 

Course Overview

  • Course Description:

      • This course introduces students to the principles and methods of analyzing biological data.
      • It covers statistical techniques and visualization tools and applies these methods to test hypotheses and validate research outcomes.
      • The course emphasizes hands-on practice through projects involving real biological datasets.
 

 

  •   Course Objectives:

                     Upon completion of this course, students will
      • Understand and analyze different biological data sets.
      • Apply the statistical methods to test and validate research hypotheses.
      • Visualize and plot the analyzed biological data.
   
  • Course Outcomes:

   By the end of the course, students will be able to:

      1. Employ computer science methodologies for analyzing various biological data types, demonstrating proficiency in descriptive statistics methods.
      2. Utilize computational tools for constructing and analyzing different plots, emphasizing the interdisciplinary nature of bioscience engineering.
      3. Develop and validate research hypotheses using statistical and computational approaches.
      4. Effectively present the outcomes of statistical analyses using figures, tables, and plots, focusing on integrating computer science techniques.
      5. Critically evaluate biological studies concerning experimental design and the application and interpretation, integrating principles from computer science and bioscience engineering.
 
 CO-PO mapping:

 

PO1: Engineering Knowledge

PO2: Problem Analysis

PO3:

Design/ Development

of Solutions

PO4: Conduct

Investigations

of Complex Problems

PO5:

Modern Tool Usage

PO6: The Engineer and Society

PO7: Environment and Sustainability

PO8: Ethics

PO9: Individual and Team Work

PO10: Communication

PO11: Project Management and Finance

PO12: Life-long Learning

CO1

3

3

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-

3

2

-

-

3

-

-

3

CO2

3

-

3

-

3

-

-

-

3

-

-

3

CO3

3

3

3

-

3

-

-

-

3

-

-

3

CO4

4

3

3

-

3

2

-

-

3

3

-

3

CO5

5

5

5

-

5

5

5

5

5

-

-

5

 

SYLLABUS

 

Practice

Module I

 

Introduction to different types of data in biology; Use of descriptive statistics (mean, median, mode, standard deviation, and standard error) for biological data analysis; Construction of different types of data plots (bar graph, line graph, pie chart, box plot, and scatter plot); Understanding error bars.

 

Module II

 

Hypothesis testing: Student's t-test, Chi-squared test, ANOVA; Pearson correlation; Regression: linear, non-linear; Principal component analysis; Clustering of data: K-means algorithm, hierarchical clustering; Visualization tools: heat map, volcano plot.

 

Projects

      1. Analysis and representation of biological data by employing descriptive statistics and different plots.
      2. Validation of research hypothesis by using t-test/chi-square test/ANOVA.
      3. Analysis of correlation of gene expressions in multiple biological samples.
      4. Analysis, clustering, and plotting of differential gene expression data.

 

 

 

 
  • Suggested books:

 

      1. S. Ross, A First Course in Probability, 9th Edition, Pearson Education India, 2014.
      2. R. C. Elston and W. D. Johnson, Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach, 1st Edition, Wiley, 2008.
      3. M. C. Whitlock, and D. Schluter, The Analysis of Biological Data, 2nd Edition, W. H. Freeman & Company, 2014.

   

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