Biological Data Analysis with Python

Extract meaningful insights by analyzing and mining biological data with Python
About This Book
* Learn why Python classes are important for biological data analyses
* Explore how to interact with and manipulate biological databases
* Test your hypotheses using NumPy, SciPy, and scikit-learn
Who This Book Is For
This course is designed to provide data scientists, bioinformaticians, and biologists a deep understanding of methods, tools, and analyses in biological data. Data scientist and developers will find this course useful from the biological perspective. Biologists and bioinformaticians will benefit from the introduction to intermediate level coding. To easily grasp concepts, you must be familiar with Python, descriptive statistics, and hypothesis testing and know the basics of genetics and sequencing technologies.
What You Will Learn
* Explore Python data structures that are commonly used to analyze biological data
* Read text files with complex structures with Python
* Deal with bigger-than-memory files while manipulating their contents
* Use Python libraries to make basic and publication-ready figures
* Display results in an organized manner both in tabular and graphical form
* Build command line programs to create your own pipelines

In Detail
Strategies to analyze biological data abound, but the bridge between ease and performance is murky at best. Python offers a simpler and more modern framework to work efficiently with big data.
Biological Data Analysis with Python bridges the biology and the programming sides of bioinformatics in an interactive, hands-on style. The course begins by exploring common Python data structures and features that are useful to bioinformaticians. You will see how to gain more from data by using Python packages and libraries to mine, manipulate, interact and display it in engaging ways. The course ends by talking about commonly used statistics and hypothesis testing methods. You will learn ways to use the NumPy, SciPy, and scikit learn packages to test your hypotheses and display results in an organized manner both in tabular and graphical form.
Whether it is interacting with command line programs for pipelining or it is exploring the dataset through Principal Component Analysis (PCA), this course teaches all that you need to know to process biological data with Python.