Udemy - Statistics For Bioinformatics I

What you'll 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
Requirements
No knowledge of statistics needed. You will learn from scratch to advance of Statistics.
Description
Bioinformatics is concerned with the study of 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 analyses biological sequences and data.There has been a great explosion of biological data and information in recent years, largely due to the advances of 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's 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.
Overview
Section 1: Statistics
Lecture 1 Introduction of Statistics
Lecture 2 Introduction of SPSS
Section 2: Descriptive Statistics
Lecture 3 Mean, Median, Mode and Variance
Lecture 4 Variance and Standard Deviation with calculation
Lecture 5 What is Skewness and Kurtosis?
Lecture 6 Quartiles
Lecture 7 Descriptive Analysis by SPSS
Lecture 8 Coefficient of Correlation
Lecture 9 Coefficient of Correlation by SPSS
Section 3: Inferential Statistics
Lecture 10 Introduction of Inferential Statistics
Lecture 11 Least Square Method in Linear Regression
Lecture 12 Methods of Linear Regression
Lecture 13 Linear Regression by SPSS
Section 4: Multiple Linear Regression
Lecture 14 Method of Multiple Linear Regression
Lecture 15 Multiple Linear Regression by SPSS
Section 5: Hypothesis Testing
Lecture 16 Introduction of Hypothesis Testing and its tests
Lecture 17 Hypothesis Testing by SPSS I
Lecture 18 Hypothesis Testing by SPSS II
Section 6: ANOVA
Lecture 19 Introduction of ANOVA and its types
Lecture 20 One way ANOVA by SPSS
Lecture 21 Two way ANOVA by SPSS
Beginner of statistics,Beginner of Bioinformatics,Beginner of Biostatistics

Homepage

https://www.udemy.com/course/statistics-for-bioinformatics-i/