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    Results for "advanced statistics"

    • Status: Free Trial
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      IBM

      Introduction to Neural Networks and PyTorch

      Skills you'll gain: PyTorch (Machine Learning Library), Artificial Neural Networks, Deep Learning, Predictive Modeling, Probability & Statistics, Machine Learning, Regression Analysis, Data Manipulation, Linear Algebra

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 3 Months

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      U

      University of Virginia

      Advanced Business Strategy

      Skills you'll gain: Business Strategy, Business Ethics, Corporate Strategy, Competitive Analysis, Strategic Thinking, Stakeholder Management, Innovation, Strategic Planning, International Finance, Business Development, Stakeholder Engagement, Growth Strategies, Strategic Decision-Making, Market Dynamics, Market Opportunities

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.1K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      B

      Board Infinity

      Java FullStack Developer

      Skills you'll gain: Angular, Web Applications, HTML and CSS, Web Development, Eclipse (Software), JavaScript Frameworks, Spring Boot, Object Oriented Programming (OOP), Restful API, Java Programming, Model View Controller, Object Oriented Design, Hypertext Markup Language (HTML), Front-End Web Development, Software Development Tools, Spring Framework, Java, Web Services, Javascript, Integrated Development Environments

      4.4
      Rating, 4.4 out of 5 stars
      ·
      657 reviews

      Intermediate · Specialization · 1 - 3 Months

    • K

      Korea Advanced Institute of Science and Technology(KAIST)

      Differential Equations Part I Basic Theory

      Skills you'll gain: Differential Equations, Mathematical Modeling, Applied Mathematics, Calculus, Linear Algebra, Vibrations, Mechanics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.4K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      M

      Meta

      Introduction to Data Analytics

      Skills you'll gain: Data Storytelling, Business Metrics, Key Performance Indicators (KPIs), Data Cleansing, Data Modeling, Analytics, Data Analysis, Data Visualization, Data Validation, Exploratory Data Analysis, Data Quality, Data Manipulation, Generative AI, Data Collection

      4.8
      Rating, 4.8 out of 5 stars
      ·
      851 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Machine Learning: Regression

      Skills you'll gain: Regression Analysis, Predictive Modeling, Supervised Learning, Statistical Modeling, Applied Machine Learning, Predictive Analytics, Feature Engineering, Machine Learning, Statistical Methods, Python Programming, Data Manipulation, Linear Algebra, Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      5.6K reviews

      Mixed · Course · 1 - 3 Months

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      U

      Universidad Austral

      Marketing Digital

      Skills you'll gain: Social Media, Social Media Strategy, Social Media Marketing, Google Ads, Digital Marketing, Customer Relationship Management, Media Buying, Digital Media Strategy, Marketing Analytics, Campaign Management, LinkedIn, Facebook, Web Analytics, Pay Per Click Advertising, Marketing Strategies, Marketing, Keyword Research, Paid media, Performance Analysis, Online Advertising

      4.7
      Rating, 4.7 out of 5 stars
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      2.7K reviews

      Beginner · Specialization · 3 - 6 Months

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      U

      University of Washington

      Practical Predictive Analytics: Models and Methods

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Statistical Machine Learning, Predictive Analytics, Advanced Analytics, Statistical Methods, Decision Tree Learning, Statistical Inference, Statistical Analysis, Machine Learning Algorithms, Machine Learning, Graph Theory, Probability & Statistics, Big Data

      4.1
      Rating, 4.1 out of 5 stars
      ·
      320 reviews

      Mixed · Course · 1 - 4 Weeks

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      U

      University of London

      Statistics for International Business

      Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Data Presentation, Statistics, Estimation, Probability, Data-Driven Decision-Making, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing, Probability Distribution, Data Visualization, Data Analysis, Histogram, Graphing

      3.8
      Rating, 3.8 out of 5 stars
      ·
      308 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      AI for Good

      Skills you'll gain: Disaster Recovery, Data Analysis, Jupyter, Exploratory Data Analysis, Image Analysis, Data Ethics, Statistical Analysis, Emergency Response, Artificial Intelligence, Machine Learning, Predictive Modeling, Applied Machine Learning, Mitigation, Text Mining, Environmental Monitoring, Natural Language Processing, Unstructured Data, Deep Learning, Data Processing, Forecasting

      4.7
      Rating, 4.7 out of 5 stars
      ·
      465 reviews

      Beginner · Specialization · 1 - 3 Months

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      Free Trial
      K

      Kennesaw State University

      Six Sigma Advanced Define and Measure Phases

      Skills you'll gain: Process Capability, Team Management, Statistical Process Controls, Exploratory Data Analysis, Six Sigma Methodology, Probability & Statistics, Process Analysis, Statistical Analysis, Lean Six Sigma, Process Mapping, Correlation Analysis, Data Analysis, Data Collection, Regression Analysis, Process Improvement, Quality Improvement, Business Process, Graphing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 3 Months

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      Free Trial
      U

      University of Pennsylvania

      AI Applications in Marketing and Finance

      Skills you'll gain: AI Personalization, Big Data, Credit Risk, Risk Management, Personalized Service, Digital Transformation, Business Risk Management, Customer Insights, Machine Learning, Marketing Analytics, Data Mining, Customer Engagement, Risk Analysis, Advanced Analytics, Data-Driven Decision-Making, Anomaly Detection, MarTech, Financial Data, Consumer Behaviour, Customer experience improvement

      4.6
      Rating, 4.6 out of 5 stars
      ·
      406 reviews

      Mixed · Course · 1 - 4 Weeks

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…272829…268

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

    • Introduction to Neural Networks and PyTorch: IBM
    • Advanced Business Strategy: University of Virginia
    • Java FullStack Developer: Board Infinity
    • Differential Equations Part I Basic Theory: Korea Advanced Institute of Science and Technology(KAIST)
    • Introduction to Data Analytics: Meta
    • Machine Learning: Regression: University of Washington
    • Marketing Digital: Universidad Austral
    • Practical Predictive Analytics: Models and Methods: University of Washington
    • Statistics for International Business: University of London
    • AI for Good: DeepLearning.AI

    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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