Advanced Bioinformatics Data Analysis: Python, R, and Linux

Focused View

Abdul Rehman Ikram

9:16:26

98 View

1 - Python Section

1 - Introduction to Bioinformatics and Why Python.mp4

09:17

2 - BioPython Introduction.mp4

09:58

3 - GitHub Repository for Python.html

4 - Setting up Coding Environment.mp4

14:02

5 - Explaining the libraries for the course.mp4

15:25

6 - Advance File Formats of Bioinformatics with BioPython.mp4

19:23

7 - Sequence Analysis Using Biopython.mp4

16:17

8 - Database RetrievalAccessing Using Biopython.mp4

11:33

9 - Working With Genomes Using Biopython.mp4

19:08

10 - Phylogenetic Tree Construction using Biopython.mp4

14:09

11 - Proteomics Analysis Using Biopython.mp4

19:56

12 - Machine Learning in Bioinformatics.mp4

11:45

2 - R Section

13 - Introduction to Bioinformatics and R Exploring the Intersection of Biology.mp4

08:02

14 - Getting Started with R Installation and Variables Understanding.mp4

10:15

15 - Working with R Packages Installing Loading and Exploring Bioinformatics.mp4

09:34

16 - Differential Gene Expression Analysis with Deseq2 Preparing Data.mp4

05:27

17 - Deseq2 Code Understanding.mp4

17:43

18 - Converting Ensembl Gene IDs to Gene Symbols Using R Techniques and Packages.mp4

11:30

19 - Visualizing Gene Expression Data Creating Stunning Plots with ggplot2.mp4

10:14

20 - Introduction to SingleCell RNA Sequencing scRNAseq Data Analysis.mp4

07:15

21 - Exploring scRNAseq Code Cell Trajectories and Gene Expression Dynamics.mp4

21:55

22 - GitHub Source Code for R.html

3 - Microarray Data Analysis Using R

23 - Introduction of Microarray.mp4

18:49

24 - Microarray Databases.mp4

12:20

25 - Microarray Analysis Using GEO2R.mp4

18:33

26 - Microarray Analysis on R.mp4

33:45

27 - Source Code for Microarray Section.html

4 - LinuxWSL for Bioinformatics

28 - Introduction and Why CLI in Bioinformatics.mp4

06:02

29 - CLI and GUI Explanation.mp4

03:24

30 - if we already have Graphical user interface system why we should use CLI.mp4

03:44

31 - Short Practical with Programming Language.mp4

03:15

32 - Why Would You Use CLI over GUI.mp4

07:07

33 - Foundation behind CLI Shell explanation.mp4

07:40

34 - Drawbacks of CLI and GUI.mp4

02:53

35 - Linux Introduction and Usage Over years.mp4

05:29

36 - Linux Distros.mp4

05:03

37 - Why Ubuntu Operating System.mp4

03:20

38 - WSL Explanation.mp4

02:05

39 - Linux Vs Unix.mp4

04:21

40 - Practical Making A Subsystem For Linux In Windows OS.mp4

05:06

41 - Linux File Handling Commands.mp4

11:05

42 - Accessing And Creating Files In Windows Os.mp4

03:28

43 - Basic Process Management Commands for Linux OS.mp4

07:07

44 - Eutilities on the Linux Command Line.mp4

03:44

45 - Installing NCBI through CLi.mp4

01:19

46 - Code Used in Lectures.html

47 - Entrez Direct Functions.mp4

02:17

48 - Mrna And Protein Seq Retrieval.mp4

03:08

49 - Batch Retrieval of Protein Using Taxon Id.mp4

02:09

50 - Retrieving CDS From Reference Genome.mp4

01:16

51 - Explaining Different Commands.mp4

01:13

5 - Bioinformatics Pipeline

52 - Pipeline Explanation.mp4

07:55

6 - NGS data Analysis on Command Line

53 - Getting the SRA Reads.mp4

08:18

54 - Checking the Quality of Data.mp4

07:39

55 - Quality Trimming of data.mp4

04:34

56 - Aligners and Aligning Reads to genome.mp4

12:48

57 - SAM and Bam File Indexing and Sorting.mp4

07:15

58 - Feature Extraction.mp4

08:43

59 - Pipeline Code.html

7 - Variant Calling Pipeline Using Linux

60 - Variants and Types.mp4

07:20

61 - Understanding the Metadata and Softwares.mp4

05:22

62 - Getting Data From SRA Using SRA Toolkit.mp4

07:18

63 - Quality Control and Trimming.mp4

09:46

64 - Sam and Bcf Tools and Fixing NS and Calling Variants.mp4

07:49

65 - Alignment to Reference Genome.mp4

10:15

66 - Separation of SNPs and Indels Variants.mp4

05:17

67 - Visualizing Variants Using IGV and UCSC Browser.mp4

03:37

68 - Pipeline Code.html

Description

Master Advanced Bioinformatics Data Analysis with Python, R, and Linux: Uncover Insights from Biological Data

What You'll Learn?

Perform Data Preprocessing and Cleaning: Students will learn how to clean and preprocess biological data using Python and R. This includes techniques for handli
Apply Common Bioinformatics Algorithms: Learners will gain proficiency in implementing and applying common bioinformatics algorithms and tools. This includes se
Visualize Biological Data: Students will develop skills in creating meaningful visualizations for biological data. They will learn to use libraries like Matplot
Analyze Genomic and Proteomic Data: This course will enable students to work with genomic and proteomic data, including DNA sequences, gene expression data, and
Interpret Results and Draw Biological Insights: After completing the course, learners will be able to interpret the results of bioinformatics analyses and draw

Who is this for?

Biologists and Life Scientists: Biologists, biochemists, geneticists, and other life science professionals who want to enhance their data analysis skills and leverage computational tools to extract meaningful insights from biological data.

Data Scientists and Analysts: Data professionals with a background in programming who wish to specialize in bioinformatics and apply their data analysis expertise to biological datasets.

Students and Researchers: Undergraduate and graduate students pursuing degrees in biology, bioinformatics, genetics, or related fields, as well as researchers in academia and industry seeking to expand their knowledge in bioinformatics.

Healthcare and Medical Professionals: Healthcare practitioners, clinicians, and medical researchers interested in genomics and personalized medicine who want to understand how to analyze genomic and proteomic data.

Computer Scientists: Computer science students and professionals interested in applying their programming skills to solve real-world biological problems.

Career Changers: Individuals looking to transition into a career in bioinformatics who may not have prior experience but possess a strong interest in biology and data analysis.

Enthusiastic Beginners: Anyone with a genuine interest in bioinformatics and a willingness to learn, regardless of their background or experience level. The course is designed to be accessible to beginners and gradually progress to more advanced topics.

What You Need to Know?

Basic Programming Knowledge: While not required, having a basic understanding of programming concepts will be helpful. Familiarity with Python and R is a plus, but the course can also accommodate those new to programming.

Biology Fundamentals: A basic understanding of biology concepts, such as DNA, RNA, proteins, and biological processes, will enhance your comprehension of the course content. However, the course will provide explanations and background information as needed.

Access to a Computer: Learners should have access to a computer with internet connectivity to access course materials, code examples, and bioinformatics tools.

Software and Tools: It's beneficial to have Python and R installed on your computer. Instructions for installing and setting up these programming environments may be provided as part of the course.

Curiosity and Eagerness to Learn: A strong desire to learn and explore the field of bioinformatics is essential. Bioinformatics can be a complex field, but a curious and motivated mindset will help you succeed.

More details

Description
Advanced Bioinformatics Data Analysis: Python, R, and Linux
Welcome to "Advanced Bioinformatics Data Analysis: Python, R, and Linux," a comprehensive course designed to equip you with the advanced skills and knowledge needed to excel in the dynamic field of bioinformatics. Whether you're a biologist seeking to harness the power of computational tools, a data scientist looking to specialize in genomics, or a curious learner eager to explore the fascinating intersection of biology and data analysis, this course is your gateway to mastering bioinformatics.
Course Overview
Unlock the Potential of Biological Data
The era of big data has ushered in a new age of discovery in the life sciences. Biological data, encompassing genomics, proteomics, and beyond, holds the key to solving complex biological questions, from understanding the genetic basis of diseases to unraveling the mysteries of evolution. This course empowers you to unlock the potential of biological data by providing hands-on experience in advanced data analysis techniques.
Master Python, R, and Linux
The course's foundation is built on three pillars: Python, R, and Linux. Python and R are indispensable programming languages in bioinformatics, known for their versatility, rich libraries, and data analysis capabilities. Linux, the preferred operating system in scientific computing, offers unparalleled control and efficiency. Throughout the course, you'll become proficient in these essential tools, gaining the technical prowess required to tackle complex bioinformatics challenges.
What You'll Learn
This comprehensive course covers a wide array of topics, ensuring you have a holistic understanding of advanced bioinformatics data analysis. Here's a glimpse of what you'll master:
1. Data Preprocessing and Quality Control
Before embarking on any analysis, you'll learn how to clean and preprocess biological data, ensuring its accuracy and reliability. Techniques for handling missing data, outlier detection, and data normalization will become second nature to you.
2. Genomic Data Analysis
Genomic data, including DNA sequences and genetic variation, is at the heart of many bioinformatics studies. You'll delve into sequence alignment, variant calling, and genome-wide association studies (GWAS), unraveling the secrets encoded within genomes.
3. Proteomic Data Analysis
Proteins are the workhorses of biology, and their analysis is crucial for understanding cellular processes. You'll explore protein structure prediction, functional annotation, and differential expression analysis, gaining insights into the world of proteomics.
4. Biological Data Visualization
A picture is worth a thousand words, and this holds true in bioinformatics. You'll become skilled in creating meaningful visualizations using libraries like Matplotlib, Seaborn, and ggplot2, allowing you to communicate your findings effectively.
5. Linux for Bioinformatics
Mastering Linux is essential for bioinformatics work. You'll learn the command-line interface, shell scripting, and how to harness the power of the Linux environment for data analysis, making you a proficient Linux user.
6. Advanced Topics in Bioinformatics
As you progress, you'll tackle advanced topics such as motif discovery, phylogenetic tree construction, and metagenomics analysis. These topics will challenge and expand your bioinformatics skill set.
7. Real-World Projects
Theory alone won't make you an expert. Throughout the course, you'll engage in hands-on projects that simulate real-world bioinformatics challenges. These projects will give you the confidence and experience needed to excel in practical applications.
8. Biological Insights and Research
Ultimately, the goal of bioinformatics is to derive meaningful biological insights. You'll learn how to interpret your analysis results in a biologically relevant context, making your work valuable to the scientific community.
Who Should Take This Course?
This course is designed for a diverse audience:
Biologists and Life Scientists: Enhance your research by incorporating computational techniques and data analysis into your work.
Data Scientists and Analysts: Specialize in bioinformatics and apply your data analysis expertise to biological data.
Students and Researchers: Whether you're a student pursuing a degree in biology or an experienced researcher, this course will expand your bioinformatics toolkit.
Healthcare and Medical Professionals: Genomic data is becoming increasingly important in healthcare. Learn how to analyze this data to inform medical decisions.
Computer Scientists: Leverage your programming skills to address biological questions and contribute to cutting-edge research.
Career Changers: Transition into a rewarding career in bioinformatics with a solid foundation in data analysis and programming.
Enthusiastic Beginners: If you have a strong interest in bioinformatics and a willingness to learn, this course provides a gentle yet comprehensive introduction.
Prerequisites
This course is designed to be accessible to a wide range of learners, and there are no strict prerequisites. However, the following recommendations will help you get the most out of the course:
Basic Programming Knowledge: Familiarity with programming concepts is helpful but not required. The course caters to learners with varying levels of programming experience.
Biology Fundamentals: Understanding fundamental biology concepts, such as DNA, RNA, and proteins, will enhance your comprehension of the course content. However, the course provides background information as needed.
Access to a Computer: You'll need a computer with internet connectivity to access course materials, code examples, and bioinformatics tools.
Software and Tools: It's beneficial to have Python and R installed on your computer. Instructions for installation and setup may be provided as part of the course.
Curiosity and Eagerness to Learn: A strong desire to explore bioinformatics and a curious mindset are essential for success in this course.
Course Format
The course is structured to accommodate learners of all backgrounds and skill levels. It includes:
Video Lectures: Engage with comprehensive video lectures that explain concepts and guide you through practical examples.
Hands-on Exercises: Apply what you've learned through hands-on exercises and projects that reinforce your skills.
Quizzes and Assessments: Test your knowledge and track your progress with quizzes and assessments throughout the course.
Discussion Forums: Connect with fellow learners, ask questions, and collaborate on bioinformatics challenges in dedicated discussion forums.
Real-World Projects: Work on practical projects that simulate real bioinformatics scenarios, helping you build a portfolio of valuable work.
Instructor Support: Access to instructor support and guidance for clarifying doubts and addressing questions.
Your Journey in Bioinformatics Starts Here
As the field of bioinformatics continues to evolve, the demand for skilled professionals who can unlock the secrets hidden in biological data is on the rise. This course empowers you to embark on a fulfilling journey into bioinformatics, where you'll gain the expertise to make meaningful contributions to the life sciences.
Are you ready to dive into advanced bioinformatics data analysis with Python, R, and Linux? Enroll today and embark on a transformative learning experience that will open doors to exciting career opportunities and groundbreaking research.
Who this course is for:
Biologists and Life Scientists: Biologists, biochemists, geneticists, and other life science professionals who want to enhance their data analysis skills and leverage computational tools to extract meaningful insights from biological data.
Data Scientists and Analysts: Data professionals with a background in programming who wish to specialize in bioinformatics and apply their data analysis expertise to biological datasets.
Students and Researchers: Undergraduate and graduate students pursuing degrees in biology, bioinformatics, genetics, or related fields, as well as researchers in academia and industry seeking to expand their knowledge in bioinformatics.
Healthcare and Medical Professionals: Healthcare practitioners, clinicians, and medical researchers interested in genomics and personalized medicine who want to understand how to analyze genomic and proteomic data.
Computer Scientists: Computer science students and professionals interested in applying their programming skills to solve real-world biological problems.
Career Changers: Individuals looking to transition into a career in bioinformatics who may not have prior experience but possess a strong interest in biology and data analysis.
Enthusiastic Beginners: Anyone with a genuine interest in bioinformatics and a willingness to learn, regardless of their background or experience level. The course is designed to be accessible to beginners and gradually progress to more advanced topics.

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Data Science

Data Visualization

Linux

Bioinformatics

R Programming

Abdul Rehman Ikram

Instructor's Courses

My teaching skills might not be as good as experienced and professional instructors on Udemy who made handsome profits from their courses, but I am sure that I have a sincere passion to teach, to broaden my professional networks, and teach that I love to do in my bioinformatics work.My goal is to help you to understand highly complex biological data and improve your computational skills in the analysis of Genomics, Proteomics, and Transcriptomics DATA using various Bioinformatics Approaches.if you have any queries about any of my work please let me know in my Udemy inbox section I'll be happy to answer all of your questions.That was All Folks!!

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.