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    Results for "advanced statistics"

    • Status: Free Trial
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      G

      Google Cloud

      Introduction to Trading, Machine Learning & GCP

      Skills you'll gain: Machine Learning, Google Cloud Platform, Applied Machine Learning, Supervised Learning, Time Series Analysis and Forecasting, Financial Trading, Deep Learning, Statistical Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Securities Trading, Technical Analysis, Financial Forecasting, Quantitative Research, Financial Modeling, Forecasting, Regression Analysis

      4
      Rating, 4 out of 5 stars
      ·
      874 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      Duke University

      MLOps | Machine Learning Operations

      Skills you'll gain: MLOps (Machine Learning Operations), Pandas (Python Package), AWS SageMaker, NumPy, Microsoft Azure, Data Ethics, Application Deployment, Data Manipulation, Exploratory Data Analysis, Containerization, Data Pipelines, CI/CD, Software Testing, Data Import/Export, Amazon Web Services, Feature Engineering, Cloud Solutions, Artificial Intelligence and Machine Learning (AI/ML), Docker (Software), Rust (Programming Language)

      4.2
      Rating, 4.2 out of 5 stars
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      459 reviews

      Advanced · Specialization · 3 - 6 Months

    • T

      Technion - Israel Institute of Technology

      Nanotechnology and Nanosensors, Part1

      Skills you'll gain: Engineering, Scientific, and Technical Instruments, Semiconductors, Innovation, Chemical and Biomedical Engineering, Emerging Technologies, Chemistry, Electronics, Medical Equipment and Technology, Laboratory Research, Chemical Engineering, Creative Design, Biochemistry

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.2K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      K

      Korea Advanced Institute of Science and Technology(KAIST)

      Electrodynamics

      Skills you'll gain: Electrical Engineering, Basic Electrical Systems, Electronic Components, Electrical Systems, Differential Equations, Physics, Engineering Analysis, Integral Calculus, Applied Mathematics, Advanced Mathematics, Scientific Visualization, Engineering Calculations, Calculus, Electronic Systems, Mechanics, Electronics, Energy and Utilities, Mathematical Modeling, Linear Algebra

      4.7
      Rating, 4.7 out of 5 stars
      ·
      949 reviews

      Intermediate · Specialization · 3 - 6 Months

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      Free Trial
      U

      University of Colorado Boulder

      Concepts, Strategies, and Analytics in Performance Marketing and Digital Advertising

      Skills you'll gain: Google Ads, Digital Advertising, Online Advertising, Advertising, Contextual Advertising, Pay Per Click Advertising, Advertising Campaigns, Paid media, Social Media, Search Engine Marketing, Keyword Research, Search Engine Optimization, Social Media Marketing, Digital Marketing, Social Media Strategy, Data Ethics, Web Analytics and SEO, Media Planning, Web Analytics, Content Performance Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      772 reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of London

      Foundations of Data Science: K-Means Clustering in Python

      Skills you'll gain: Data Visualization, Matplotlib, Probability & Statistics, Data Science, Unsupervised Learning, Statistics, NumPy, Python Programming, Pandas (Python Package), Data Analysis, Machine Learning Algorithms, Descriptive Statistics, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
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      717 reviews

      Beginner · Course · 1 - 3 Months

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      N

      New York Institute of Finance

      Introduction to Risk Management

      Skills you'll gain: Risk Management, Business Risk Management, Risk Modeling, Operational Risk, Enterprise Risk Management (ERM), Credit Risk, Risk Analysis, Portfolio Management, Capital Markets, Financial Market, Financial Regulation, Financial Modeling, Probability Distribution

      4.6
      Rating, 4.6 out of 5 stars
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      706 reviews

      Beginner · Course · 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
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      1.3K reviews

      Intermediate · Course · 1 - 3 Months

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      Free Trial
      U

      University of Alberta

      Sample-based Learning Methods

      Skills you'll gain: Reinforcement Learning, Sampling (Statistics), Machine Learning Algorithms, Simulations, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Algorithms, Probability Distribution

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.2K reviews

      Intermediate · Course · 1 - 3 Months

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      Free Trial
      U

      University of California, Davis

      GIS Data Formats, Design and Quality

      Skills you'll gain: Spatial Data Analysis, Spatial Analysis, ArcGIS, Geographic Information Systems, Geospatial Mapping, Data Quality, Data Mapping, Data Modeling, Data Storage, Data Sharing, Data Manipulation, Relational Databases, Query Languages

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • U

      University of Pennsylvania

      A Crash Course in Causality: Inferring Causal Effects from Observational Data

      Skills you'll gain: R Programming, Statistical Analysis, Statistical Methods, Statistical Software, Statistical Modeling, Statistical Inference, Data Analysis, Probability & Statistics, Regression Analysis, Research Design, Graph Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      564 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      N

      New York University

      Machine Learning and Reinforcement Learning in Finance

      Skills you'll gain: Supervised Learning, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Deep Learning, Market Liquidity, Financial Services

      3.7
      Rating, 3.7 out of 5 stars
      ·
      814 reviews

      Intermediate · Specialization · 3 - 6 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
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    In summary, here are 10 of our most popular advanced statistics courses

    • Introduction to Trading, Machine Learning & GCP: Google Cloud
    • MLOps | Machine Learning Operations: Duke University
    • Nanotechnology and Nanosensors, Part1: Technion - Israel Institute of Technology
    • Electrodynamics: Korea Advanced Institute of Science and Technology(KAIST)
    • Concepts, Strategies, and Analytics in Performance Marketing and Digital Advertising: University of Colorado Boulder
    • Foundations of Data Science: K-Means Clustering in Python: University of London
    • Introduction to Risk Management: New York Institute of Finance
    • Six Sigma Advanced Define and Measure Phases: Kennesaw State University
    • Sample-based Learning Methods: University of Alberta
    • GIS Data Formats, Design and Quality: University of California, Davis

    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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