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    Results for "advanced statistics"

    • U

      Universidad de los Andes

      Maestría en Inteligencia Artificial

      Skills you'll gain: Real-Time Operating Systems, Supervised Learning, Unsupervised Learning, Anomaly Detection, Computer Vision, Reinforcement Learning, Cloud-Native Computing, Natural Language Processing, Containerization, Deep Learning, Dimensionality Reduction, Artificial Intelligence, Machine Learning, CI/CD, Probability & Statistics, Control Systems, Data Ethics, Application Deployment, Epidemiology, Education Software and Technology

      Earn a degree

      Degree · 1 - 4 Years

    • P

      Pontificia Universidad Católica de Chile

      Magíster en Salud Pública Global

      Skills you'll gain: Epidemiology, Public Health and Disease Prevention, Descriptive Analytics, Biostatistics, Statistical Reporting, Gerontology, Digital Transformation, Health Policy, Healthcare Ethics, Sampling (Statistics), Cloud Security, Assertiveness, Telehealth, Health Disparities, Public Health, Object Oriented Programming (OOP), Business Analytics, Program Evaluation, Occupational Safety and Health Administration (OSHA), Health Systems

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      Degree · 1 - 4 Years

    • U

      University of London

      International Foundation Programme for Computer Science (IFP)

      Skills you'll gain:

      Earn a degree

      Degree · 1 - 4 Years

    • U

      University of Colorado Boulder

      Data Science Graduate Certificate

      Skills you'll gain: Data Mining, Unsupervised Learning, Supervised Learning, Deep Learning, Statistical Modeling, Machine Learning Algorithms, Statistical Inference, Statistical Hypothesis Testing, Service Level, Statistical Machine Learning, Exploratory Data Analysis, Probability, Dimensionality Reduction, Applied Machine Learning, Data Warehousing, Data Pipelines, Data Processing, Performance Testing, Bash (Scripting Language), Data Science

      Credit offered

      Graduate Certificate · 6 - 12 Months

    • U

      University of Illinois at Urbana-Champaign

      Master of Computer Science (feat. Data Science Track)

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      Degree · 1 - 4 Years

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    advanced statistical analysis and tools
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    seaborn: visualizing basics to advanced statistical plots
    advanced probability and statistical methods
    statistical analysis and advanced techniques
    advanced linear models for data science 2: statistical linear models
    1…262263264265

    In summary, here are 5 of our most popular advanced statistics courses

    • Maestría en Inteligencia Artificial: Universidad de los Andes
    • Magíster en Salud Pública Global: Pontificia Universidad Católica de Chile
    • International Foundation Programme for Computer Science (IFP): University of London
    • Data Science Graduate Certificate: University of Colorado Boulder
    • Master of Computer Science (feat. Data Science Track): University of Illinois at Urbana-Champaign

    Frequently Asked Questions about Advanced Statistics

    Advanced statistics are the mathematical tools used to discover and explore complex relationships between different variables in large datasets. In contrast to basic statistics such as average and analysis of variance (ANOVA) that simply describe the characteristics of a dataset, advanced statistical approaches often seek to make predictions about the world. This requires the use of more sophisticated statistical inference tools, such as generalized linear models for regression analysis capable of establishing how multiple interrelated factors may impact projected outcomes.

    These advanced statistical methods are increasingly important in the field of data science, which is tasked with uncovering important business insights and developing predictive models from diverse big data-scale datasets. These techniques are also especially important for the proper training and use of machine learning algorithms. As in data science and machine learning more generally, R programming and Python programming skills are typically relied upon to conduct these advanced statistical analyses.‎

    Advanced statistics skills are essential for work in data science, machine learning, and artificial intelligence (AI), as statistical approaches are at the heart of the learning algorithms that make these applications possible. An understanding of statistics is likewise important for professionals in finance, healthcare, and other industries that are increasingly making use of machine learning and AI, as they increasingly need to work closely with data scientists to ensure that these powerful techniques are developed to solve the right business problems.

    Those wishing to delve deeper into advanced statistical methods and help develop new mathematical approaches in the field may pursue a master’s or even a PhD in statistics. These experts work in academia, government, or at private sector companies involved in scientific or engineering research. According to the Bureau of Labor Statistics, professional statisticians earn a median annual salary of $91,160, and this specialized career path is expected to be in high demand due to expanding opportunities to use statistics to navigate our data-rich world.‎

    Certainly. Coursera offers a variety of courses in advanced statistics as well as their applications in the context of fields like data science and machine learning. In fact, coursework in statistics is often a prerequisite for data science classes. Regardless of your level of expertise and needs in these areas, Coursera enables you to learn remotely from top-ranked schools like the University of Michigan, Johns Hopkins University, and Duke University. And, since you can view course materials and complete coursework on a flexible schedule, there’s an exceedingly high probability that you can fit online learning about advanced statistics into your existing school or work life.‎

    You need to have strong math skills, especially in basic calculus, linear algebra, and statistics before starting to learn advanced statistics. It's important that you have strong technical skills and are very comfortable on the computer, strong analytical skills, and the ability to carefully examine and question data that is presented to you so that you can organize and draw conclusions from it. For learning some concepts in advanced statistics, you'll need to have experience using the R statistical software package and understand Bayesian estimation, principles of maximum-likelihood estimation, and calculus-based probability.‎

    People who enjoy mathematics are best suited for roles in advanced statistics, especially those who enjoy concepts like probability, linear models, and statistics and how they relate to data science. They can quickly grasp and apply complex technical concepts as well. Those who enjoy testing hypotheses and figuring out uncertain outcomes based on probability are also well suited for roles in advanced statistics. Also, people who have wide-ranging computer skills, the ability to communicate their statistical findings in plain language, problem-solving and analytical skills, and teamwork and collaborative skills are best suited for roles involving advanced statistics.‎

    If you're aspiring to be a biostatistician or data scientist, learning advanced statistics is probably right for you. If you're interested in machine learning and the development of data products, you may also find learning advanced statistics is right for you. People who want to have a career as a statistician, statistical epidemiologist, sports analyst, actuary, market researcher, or investment analyst may also find learning advanced statistics to be the right choice. And if you need to understand how to transform complex sets of data into practical applications, learning advanced statistics is right for you.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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