17 Bioinformatics courses

In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes * Develop a foundational understanding of R's role in data science and proficiency in RStudio. * Gain fluency in R programming basics, enabling the handling of complex data tasks. * Acquire skills in managing various R data structures for efficient data analysis. * Master relational and logical operations for advanced data manipulation in R. * Learn to create functions and utilize R packages for expanded analytical capabilities. WHY CHOOSE THIS R PROGRAMMING FOR DATA SCIENCE COURSE? 1. Unlimited access to the course for a lifetime. 2. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. 3. Structured lesson planning in line with industry standards. 4. Immerse yourself in innovative and captivating course materials and activities. 5. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. 6. Flexibility to complete the Course at your own pace, on your own schedule. 7. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. 8. Unlock career resources for CV improvement, interview readiness, and job success. WHO IS THIS R PROGRAMMING FOR DATA SCIENCE COURSE FOR? * Beginners in data science eager to learn R programming. * Data analysts and scientists looking to enhance their skills in R. * Researchers in various fields requiring advanced data analysis tools. * Statisticians seeking to adopt R for more sophisticated data manipulations. * Professionals in finance, healthcare, and other sectors needing data-driven insights. CAREER PATH * Data Scientist (R Expertise): £30,000 - £70,000 * Data Analyst (R Programming Skills): £27,000 - £55,000 * Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 * Quantitative Analyst (R Knowledge): £40,000 - £80,000 * Research Analyst (R Usage): £25,000 - £50,000 * Business Intelligence Developer (R Familiarity): £32,000 - £65,000 PREREQUISITES This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CERTIFICATION After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. COURSE CURRICULUM Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

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ABOUT COURSE MASTER THE STATISTICAL SKILLS YOU NEED TO UNDERSTAND AND ANALYZE BIOMEDICAL RESEARCH DATA WITH THIS BIOSTATISTICS ONLINE COURSE Are you working on public health, clinical medicine, biology or related fields? Are you familiar with the process of obtaining an accurate picture from a large number of data points? This Biostatistics Online Course demonstrates how to use statistical techniques to summarize the characteristics of a data set to draw meaningful conclusions. In this course, you will learn all about Biostatistics and its application in medical and life sciences. This course is a comprehensive introduction to the field of biostatistics, covering a wide range of topics from basic statistical concepts to more advanced biostatistical methods. BIOSTATISTICS ONLINE COURSE MODULES: * MODULE 1: INTRODUCTION TO BIOSTATISTICS This module provides an overview of biostatistics, its applications in the field of health sciences, and the different types of study designs used in biomedical research. It also introduces the basic concepts of statistics, including data types, variables, inferential statistics, hypothesis testing, and the role of statistics in biostatistics and evidence-based medicine. * MODULE 2: PROBABILITY This module covers the basics of probability, including probability distributions, random variables, and sampling distributions. Students will learn how to calculate and interpret probabilities in the context of biomedical research. * MODULE 3: DESCRIPTIVE STATISTICS This module covers the different measures of central tendency and variability, as well as graphical representations of data. Students will learn how to describe and summarize data from biomedical studies using these methods. * MODULE 4: INFERENTIAL STATISTICS This module covers the fundamental concepts of inferential statistics, including estimation, hypothesis testing, confidence intervals, and p-values. Students will learn how to use these methods to draw conclusions about populations based on data from samples. * MODULE 5: REGRESSION ANALYSIS This module introduces the basics of regression analysis, including simple linear regression, multiple linear regression, and logistic regression. Students will learn how to use these methods to model relationships between variables and to make predictions. * Module 6: Biostatistics Tools This module covers a variety of biostatistical tools that are commonly used in biomedical research, including survival analysis, clinical trials, and epidemiological studies. Students will learn how to use these tools to answer specific research questions. * MODULE 7: STATISTICAL SOFTWARE AND TOOLS This module introduces students to popular statistical software programs, such as R and SPSS. Students will learn how to import, manage, and analyze data using these software programs, as well as how to perform statistical tests and generate summary statistics. * MODULE 8: ETHICAL CONSIDERATIONS AND REPORTING GUIDELINES This module covers the importance of ethical considerations in biostatistics and the reporting guidelines for statistical analysis in research publications. Students will also learn about best practices for data management and data sharing.   Why You Should Take This Course Whether you are a student, researcher, or healthcare professional, biostatistics is an essential skill for understanding and interpreting biomedical research. This course provides a comprehensive and accessible introduction to the field of biostatistics, covering all the essential topics that you need to know. By taking this course, you will learn how to: * Design and conduct biomedical studies * Collect and manage data * Analyze data using statistical methods * Interpret statistical results * Communicate statistical findings effectively This course is ideal for students in the fields of public health, medicine, nursing, epidemiology, and other health sciences. It is also beneficial for researchers, healthcare professionals, and anyone else who wants to learn more about biostatistics.   Enroll today and start your journey to becoming a biostatistics expert!   To find more course in this topic, search more . WHAT WILL YOU LEARN? * Design and conduct biomedical studies * Collect and manage data * Analyze data using statistical methods * Interpret statistical results * Communicate statistical findings effectively COURSE CONTENT INTRODUCTION TO BIOSTATISTICS * INTRODUCTION TO BIOSTATISTICS PROBABILITY * MODULE 2 PROBABILITY DESCRIPTIVE STATISTICS * DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS * INFERENTIAL STATISTICS REGRESSION ANALYSIS * REGRESSION ANALYSIS BIOSTATISTICS TOOLS * BIOSTATISTICS TOOLS STATISTICAL SOFTWARE AND TOOLS * STATISTICAL SOFTWARE AND TOOLS ETHICAL CONSIDERATIONS AND REPORTING GUIDELINES * ETHICAL CONSIDERATIONS AND REPORTING GUIDELINES A course by Xpert Learning RequirementsBasic understanding of Mathematics and Statistics Audience Students in health sciences Researchers Healthcare professionals Anyone interested in learning about biostatistics AUDIENCE * Students in health sciences * Researchers * Healthcare professionals * Anyone interested in learning about biostatistics