Skills you'll gain: SAS (Software), Statistical Programming, Data Analysis, Data Mining, Machine Learning, Probability & Statistics, Regression, Statistical Analysis, Statistical Machine Learning, Exploratory Data Analysis, Machine Learning Algorithms, Statistical Tests, Advertising, Business Analysis, Computer Programming, General Statistics, Marketing, Python Programming
Intermediate · Course · 1-3 Months
Skills you'll gain: Probability & Statistics, Machine Learning, General Statistics, Business Analysis, Data Analysis, Statistical Analysis, Experiment, Probability Distribution, Python Programming, Applied Machine Learning, Regression, Statistical Tests, Advertising, Algebra, Communication, Data Management, Data Structures, Linear Algebra, Machine Learning Algorithms, Marketing, Theoretical Computer Science
Mastertrack · 6-12 Months
Skills you'll gain: Theoretical Computer Science, Machine Learning, Software Engineering, Algorithms, Software Architecture, Statistical Machine Learning, Software Testing, Computer Programming, Data Visualization, Mobile Development, Deep Learning, Security Engineering, Databases, Probability & Statistics, Computational Logic, Mathematics, Computational Thinking, Computer Architecture, General Statistics, Operating Systems, Programming Principles, Bayesian Network, Computer Vision, Mathematical Theory & Analysis, Data Analysis, Data Mining, Data Management, Design and Product, Distributed Computing Architecture, Feature Engineering, NoSQL, Product Design, BlockChain, Finance, Artificial Neural Networks, Operations Research, Probability Distribution, Research and Design, Strategy and Operations, Amazon Web Services, Application Development, Calculus, Cloud Computing, Communication, Cryptography, Data Model, Data Structures, Database Administration, Database Application, Database Design, Dimensionality Reduction, Journalism, Microarchitecture, Mobile Security, SQL, Security Strategy, Software Framework, Statistical Programming, System Programming, System Security, iOS Development, Advertising, Algebra, Computer Graphics, Computer Networking, Critical Thinking, Docker (Software), Econometrics, Entrepreneurship, Geovisualization, Hardware Design, Human Computer Interaction, Leadership and Management, Marketing, Matlab, Network Security, Other Programming Languages, Planning, Python Programming, Scala Programming, Spreadsheet Software, Statistical Tests, Supply Chain Systems, Supply Chain and Logistics, Tableau Software, User Experience, Web Development
Mastertrack · 6-12 Months
Skills you'll gain: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Applied Machine Learning, Algorithms, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication
Beginner · Specialization · 1-3 Months
Logistic regression is a technique used in statistics that allows people to estimate the probability of something happening based on existing data they have about that event taking place before. Mathematical models are used often in science and engineering disciplines to explain concepts using mathematical language, and one of these models is logical regression. Logistic regression works using binary data, meaning there are only two possible outcomes for the event: It takes place, or it doesn’t take place. To figure out the probability of these two outcomes, logistic regression uses equations that calculate odds ratios — the odds that something will happen or it won’t. This predictive modeling tool plays a large role not only in statistics but also in machine learning, which involves computers learning information that they haven’t explicitly been programmed to process.
If you’re considering going into a career field that works with data, software or mathematics, logical regression is a valuable area of study to focus on. Logistic regression becomes an important step of the programming process when you’re building software that deals with predictive modeling or data analysis. And, if you’re interested in enhancing your understanding of machine learning, logistic regression is an essential. When you understand modeling with logical regression, you can progress more easily to the complex models involved with machine learning while learning how to best prepare data for processing.
A career as a data scientist or data analyst gives you the opportunity to apply your knowledge of logistic regression, but you’ll also frequently draw upon your skills in this arena if you want to go into the field of machine learning. Although these careers are relatively broad, working with machine learning and logistic regression is also possible in a variety of specialties you’ll find in software engineering, computational linguistics and software development. As you begin to learn more about logistic regression while taking online classes, you may discover a particular area of interest you want to explore — and your new skills can help you discover more.
Taking online courses about logistic regression can give you the knowledge you need to progress in your field or start fresh. In your career as a data scientist or analyst, you know the importance of statistical approaches and the variety of data-modeling techniques you utilize on a regular basis. But if you’re ready to dig deeper into these concepts to boost your understanding and put new ideas and skills into practice, taking online courses about logistic regression can get you where you want to go. If you’re starting with the basics, take a ground-up approach with introductory courses that create a solid foundation for future learning. Or, if you’re looking to supplement your existing knowledge base with a greater understanding of logistic regression, try courses that help you learn the concept’s role in machine learning and programming software for predictive modeling. You’ll appreciate your newfound comprehension of these innovative ideas — and you’ll love the freedom to participate in online courses when and where it’s most convenient for you.