This course provides an introduction to statistical predictive modelling and its applications. You will learn linear and logistic regression, as well as naive Bayes, and how to apply them to real-world scenarios. Through online learning and skill training, you will gain the knowledge and skills to use these techniques to predict outcomes and make decisions.
Introduction To R Software
1.5
Swayam42 learners
Learn More
This course introduces students to the R software, a free software used for mathematical and statistical manipulations. It covers the basics of the R programming language and its built-in functions. Lectures and online learning will help students gain the skills needed to use the software for data analysis, simulations, and programming. The course is intended for UG students of Science and Engineering, students of humanities with basic mathematical background, and working professionals in analytics. A mathematics background up to class 12 is recommended. All industries involved in mathematical and statistical computations, programming, and simulations will benefit from this course.
ANOVA and Experimental Design
1.5
Coursera0 learners
Learn More
This course introduces students to the analysis of variance (ANOVA) and experimental design. Students will learn about linear regression models, randomization, blocking, factorial design, and causality. The course is part of the Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. This interdisciplinary degree is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
Data Science: Inference and Modeling
4.0
Edx1,009 learners
Learn More
This course will teach you the fundamentals of data science, including statistical inference and modeling, through a motivating case study on election forecasting. You will learn how to use R to define estimates and margins of errors, understand confidence intervals and p-values, and apply Bayesian modeling. At the end of the course, you will be able to recreate a simplified version of an election forecast model and apply it to the 2016 election.
1. Programming Courses: These online courses cover a wide range of programming languages such as Python, Java, C++, and more. They also teach students web development, software engineering, and other technical skills.
2. Business Courses: These courses cover topics such as entrepreneurship, marketing, accounting, and financial management. Students can learn about business strategy, banking, investment, and more.
3. Language Courses: Online courses in languages such as Spanish, French, Mandarin, and more are available for students looking to improve their language skills or learn a new language.
4. Healthcare Courses: These courses cover topics such as nursing, medical coding, healthcare management, and more. Students can also learn about public health, nutrition, and other related topics.
5. Arts & Design Courses: Online courses in graphic design, animation, photography, and other artistic subjects are available for students interested in creative fields.
6. Personal Development Courses: These courses cover topics such as time management, communication skills, mindfulness, and more to help students improve their personal and professional lives.
7. STEM Courses: Science, Technology, Engineering, and Mathematics (STEM) courses cover subjects such as biology, physics, engineering, and more. Students can learn about cutting-edge technologies and scientific advancements.
Click Allow to get free Statistics & Probability courses!
Click Allow to get free Statistics & Probability courses!