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    Results for "advanced statistics"

    • 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

    • U

      University of Michigan

      Moneyball and Beyond

      Skills you'll gain: Analytics, Data Analysis, Statistical Analysis, Probability & Statistics, Statistics, Python Programming, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      51 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Create and Design Digital Products using Canva

      Skills you'll gain: Canva (Software), Design, Digital Design, Design Elements And Principles, Product Design, Color Theory, Intellectual Property, Product Promotion, E-Commerce, Digital Assets, Marketing Materials

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.2K reviews

      Advanced · Guided Project · Less Than 2 Hours

    • U

      University of Colorado Boulder

      The Structured Query Language (SQL)

      Skills you'll gain: SQL, Database Systems, Database Theory, Database Management, Query Languages, Database Design, Relational Databases, Data Access, Data Analysis, Data Manipulation, Data Modeling, Data Integrity

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      999 reviews

      Beginner · Course · 1 - 3 Months

    • 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

    • I

      Imperial College London

      Foundations of Public Health Practice

      Skills you'll gain: Public Health, Health Equity, Health Disparities, Microbiology, Health Assessment, Sanitation, Program Evaluation, Epidemiology, Infectious Diseases, Health Policy, Preventative Care, Behavioral Economics, Community Health, Data Literacy, Behavioral Health, Intelligence Collection and Analysis, Emergency Response, Health And Safety Standards, Public Health and Disease Prevention, Health Systems

      4.8
      Rating, 4.8 out of 5 stars
      ·
      624 reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Illinois Urbana-Champaign

      Financial Accounting: Advanced Topics

      Skills you'll gain: Cash Flows, Financial Accounting, Financial Statements, Fixed Asset, Financial Reporting, Financial Analysis, Depreciation, Balance Sheet, Equities, Accounting, Asset Management

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.7K reviews

      Intermediate · Course · 1 - 3 Months

    • 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

    • M

      Microsoft

      Microsoft AI & ML Engineering

      Skills you'll gain: Unsupervised Learning, Generative AI, Large Language Modeling, Data Management, Natural Language Processing, MLOps (Machine Learning Operations), Supervised Learning, Microsoft Azure, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Infrastructure Architecture, Cloud Infrastructure, Generative AI Agents, Applied Machine Learning, Reinforcement Learning, Data Ethics, Prompt Engineering, Data Processing, Artificial Intelligence, Application Deployment

      4.6
      Rating, 4.6 out of 5 stars
      ·
      127 reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • U

      University at Buffalo

      Digital Manufacturing & Design

      Skills you'll gain: Digital Transformation, Manufacturing Processes, Manufacturing Operations, Industrial Engineering, Business Transformation, Digital Design, Data-Driven Decision-Making, Product Lifecycle Management, Supply Chain, Market Dynamics, Safety and Security, Workforce Development

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.6K reviews

      Beginner · Course · 1 - 4 Weeks

    • M

      MathWorks

      MathWorks Computer Vision Engineer

      Skills you'll gain: Computer Vision, Image Analysis, Anomaly Detection, Deep Learning, Applied Machine Learning, Artificial Neural Networks, Matlab, Application Deployment, PyTorch (Machine Learning Library), Machine Learning, Motion Graphics, Data Visualization, Automation, Data Mapping, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Spatial Data Analysis, Predictive Modeling, Visualization (Computer Graphics), Data Synthesis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      299 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of California, Irvine

      Leverage Data Science for a More Agile Supply Chain

      Skills you'll gain: Supply Chain Planning, Demand Planning, Customer Demand Planning, Inventory Management System, Inventory Control, Supply Chain Management, Capacity Planning, Materials Management, Process Optimization, Forecasting, Operations Management, Resource Allocation, Service Level, Capacity Management, Performance Measurement, Microsoft Excel, Data-Driven Decision-Making, Statistical Methods, Cost Reduction, Simulation and Simulation Software

      4.5
      Rating, 4.5 out of 5 stars
      ·
      417 reviews

      Intermediate · Specialization · 1 - 3 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…303132…262

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

    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Moneyball and Beyond: University of Michigan
    • Create and Design Digital Products using Canva: Coursera Project Network
    • The Structured Query Language (SQL): University of Colorado Boulder
    • Six Sigma Advanced Define and Measure Phases: Kennesaw State University
    • Foundations of Public Health Practice: Imperial College London
    • Financial Accounting: Advanced Topics: University of Illinois Urbana-Champaign
    • A Crash Course in Causality: Inferring Causal Effects from Observational Data: University of Pennsylvania
    • Microsoft AI & ML Engineering: Microsoft
    • Digital Manufacturing & Design: University at Buffalo

    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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