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    Results for "advanced statistics"

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      Packt

      AWS Step Functions – Automate Complex Tasks with Workflows

      Skills you'll gain: Debugging, Amazon Web Services, AWS CloudFormation, Business Process Automation, Automation, Amazon S3, AWS Identity and Access Management (IAM), Amazon CloudWatch, Serverless Computing, API Gateway, Amazon DynamoDB, JSON, Data Validation

      Intermediate · Course · 1 - 3 Months

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      Packt

      Matrix Calculus for Data Science & Machine Learning

      Skills you'll gain: Applied Mathematics, NumPy, Calculus, Numerical Analysis, Advanced Mathematics, Linear Algebra, Data Science, Jupyter, Machine Learning Algorithms, Derivatives, Machine Learning, Algorithms

      Intermediate · Course · 1 - 3 Months

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      University of Maryland, College Park

      Digital Marketing 2

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      University of Leeds

      Master of Science in Data Science (Statistics)

      Skills you'll gain: Regression Analysis, Web Scraping, Statistical Hypothesis Testing, Matplotlib, Correlation Analysis, Statistical Machine Learning, Statistical Visualization, Statistical Methods, Classification And Regression Tree (CART), Exploratory Data Analysis, Network Analysis, Planning, Applied Machine Learning, Bayesian Statistics, Unsupervised Learning, Dimensionality Reduction, Data Visualization Software, Supervised Learning, Statistical Modeling, Artificial Intelligence and Machine Learning (AI/ML)

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      Fractal Analytics

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      Packt

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      Packt

      Introduction to Object-Oriented Programming with Arduino

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      Intermediate · Course · 1 - 3 Months

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      Microsoft

      Exploratory Data Analysis and Visualization

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      Board Infinity

      Effective Code Review Practices

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      Intermediate · Course · 1 - 4 Weeks

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    In summary, here are 10 of our most popular advanced statistics courses

    • AWS Step Functions – Automate Complex Tasks with Workflows: Packt
    • Matrix Calculus for Data Science & Machine Learning: Packt
    • Digital Marketing 2: University of Maryland, College Park
    • Master of Science in Data Science (Statistics): University of Leeds
    • Data Frameworks for Generative AI: Fractal Analytics
    • Web Application Security Testing with Google Hacking: Packt
    • Introduction to Object-Oriented Programming with Arduino : Packt
    • Exploratory Data Analysis and Visualization: Microsoft
    • Strategic Analysis and Solution Design: SAP
    • Effective Code Review Practices: Board Infinity

    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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