Statistics for Bioinformatics I

Statistics for Bioinformatics I, available at $19.99, has an average rating of 3, with 22 lectures, based on 4 reviews, and has 1004 subscribers.

You will learn about Basics of Statistics Analysis by SPSS Build a predict model of data Understand some of the common stochastic models encountered in Bioinformatics This course can be use in Biostatistics, Applied Machine Learning, Computational Drug Designing This course will be helpful in microarray for linear models, gene expression analysis, clustering, PCA plotting and quality control etc This course is ideal for individuals who are Beginner of statistics or Beginner of Bioinformatics or Beginner of Biostatistics It is particularly useful for Beginner of statistics or Beginner of Bioinformatics or Beginner of Biostatistics.

Enroll now: Statistics for Bioinformatics I

Summary

Title: Statistics for Bioinformatics I

Price: $19.99

Average Rating: 3

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of Statistics
Analysis by SPSS
Build a predict model of data
Understand some of the common stochastic models encountered in Bioinformatics
This course can be use in Biostatistics, Applied Machine Learning, Computational Drug Designing
This course will be helpful in microarray for linear models, gene expression analysis, clustering, PCA plotting and quality control etc

Who Should Attend

Beginner of statistics
Beginner of Bioinformatics
Beginner of Biostatistics

Target Audiences

Beginner of statistics
Beginner of Bioinformatics
Beginner of Biostatistics

Bioinformatics is concerned with the study of the inherent structure of biological information, and statistical methods are the workhorses in many of these studies. Statistical Bioinformatics acknowledges the inherent variation found in data that are generated as part of the bioinformatics investigation and attempts to utilize experimental structure and design to partition variation in biological and technical components.

This course introduces the statistical methods commonly used in bioinformatics and biological research. Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. Students will learn the principles behind statistical methods and how they can be applied to analyse biological sequences and data.

There has been a great explosion of biological data and information in recent years, largely due to the advances in various high-throughput biotechnologies such as mass spectrometry, high throughput sequencing, and many genome-wide SNP profiling, RNA gene expression microarray, protein mass spectrometry, and many other recent high-throughput biotechniques. We can tackle all big data with statistics with different software like R, SPSS, Minitab, SAS and MATLAB. Statistical theory or methods to be introduced in this course include Z-test, t-test, regression, ANOVA, hypothesis testing and multivariate data analysis. Its 2nd course (Statistics for Biinfromatic II) will be available soon for advanced statistics.

Course Curriculum

Chapter 1: Statistics

Lecture 1: Introduction of Statistics

Lecture 2: Introduction of SPSS

Chapter 2: Descriptive Statistics

Lecture 1: Mean, Median, Mode and Variance

Lecture 2: Variance and Standard Deviation with calculation

Lecture 3: What is Skewness and Kurtosis?

Lecture 4: Quartiles

Lecture 5: Descriptive Analysis by SPSS

Lecture 6: Coefficient of Correlation

Lecture 7: Coefficient of Correlation by SPSS

Chapter 3: Inferential Statistics

Lecture 1: Introduction of Inferential Statistics

Lecture 2: Least Square Method in Linear Regression

Lecture 3: Methods of Linear Regression

Lecture 4: Linear Regression by SPSS

Chapter 4: Multiple Linear Regression

Lecture 1: Method of Multiple Linear Regression

Lecture 2: Multiple Linear Regression by SPSS

Chapter 5: Hypothesis Testing

Lecture 1: Introduction of Hypothesis Testing and its tests

Lecture 2: Hypothesis Testing by SPSS I

Lecture 3: Hypothesis Testing by SPSS II

Chapter 6: ANOVA

Lecture 1: Introduction of ANOVA and its types

Lecture 2: One way ANOVA by SPSS

Lecture 3: Two way ANOVA by SPSS

Chapter 7: Predictive Modelling

Lecture 1: Predictive Modeling by SPSS

Instructors

Sana Fatima
Science is here

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 0 votes
5 stars: 2 votes

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