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    Results for "advanced statistics"

    • U

      University of California, Davis

      Fundamentals of GIS

      Skills you'll gain: ArcGIS, GIS Software, Spatial Data Analysis, Data Sharing, Spatial Analysis, Geographic Information Systems, Geospatial Mapping, Metadata Management, Data Quality, Data Mapping, File Management, Data Presentation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      5.8K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Toronto

      The Arts and Science of Relationships: Understanding Human Needs

      Skills you'll gain: Relationship Building, Social Skills, Active Listening, Interpersonal Communications, Communication, Empathy, Self-Awareness, Emotional Intelligence, Needs Assessment, Cultural Diversity

      4.5
      Rating, 4.5 out of 5 stars
      ·
      1.8K reviews

      Beginner · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Summary Statistics in Public Health

      Skills you'll gain: Biostatistics, Statistical Visualization, Epidemiology, Medical Science and Research, Quantitative Research, Descriptive Statistics, Statistical Methods, Data Literacy, Probability Distribution, Public Health, Statistical Analysis, Clinical Trials, Sample Size Determination

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2K reviews

      Beginner · Course · 1 - 3 Months

    • L

      LearnQuest

      Advanced Network Security

      Skills you'll gain: Incident Response, Cyber Threat Hunting, Computer Security Incident Management, Intrusion Detection and Prevention, Cyber Threat Intelligence, Continuous Monitoring, Threat Detection, Cloud Security, Disaster Recovery, Threat Management, Firewall, Security Management, Network Security, Cybersecurity, Cloud Services, Security Information and Event Management (SIEM), Network Monitoring, Security Strategy, Security Controls, Information Systems Security

      4.3
      Rating, 4.3 out of 5 stars
      ·
      57 reviews

      Advanced · Specialization · 1 - 3 Months

    • D

      DeepLearning.AI

      TensorFlow: Data and Deployment

      Skills you'll gain: Extract, Transform, Load, Data Pipelines, Image Analysis, Data Import/Export, Tensorflow, iOS Development, Application Deployment, Android Development, Data Processing, Computer Vision, MLOps (Machine Learning Operations), Swift Programming, Feature Engineering, Mobile Development, Data Transformation, JSON, Applied Machine Learning, Data Sharing, Javascript, Deep Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      TensorFlow: Advanced Techniques

      Skills you'll gain: Generative AI, Tensorflow, Computer Vision, Image Analysis, Deep Learning, Keras (Neural Network Library), Applied Machine Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering, Distributed Computing, Computer Graphics, Unsupervised Learning, Network Model, Visualization (Computer Graphics), Network Architecture, Performance Tuning, Machine Learning Methods, API Design, Object Oriented Programming (OOP)

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.7K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free
      Free
      C

      Case Western Reserve University

      Introduction to International Criminal Law

      Skills you'll gain: Trial Preparation, Court Systems, Criminal Investigation and Forensics, Legal Proceedings, Legal Hearings, Litigation and Civil Justice, International Relations, Legal Strategy, Diplomacy, Public Safety and National Security, Ethical Standards And Conduct, Case Law, Intelligence Collection and Analysis, Interactive Learning, Student Engagement

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.9K reviews

      Mixed · Course · 1 - 3 Months

    • G

      Google

      Get Started with Python

      Skills you'll gain: Object Oriented Programming (OOP), Data Analysis, Data Structures, Jupyter, Python Programming, NumPy, Pandas (Python Package), Programming Principles, Scripting, Data Manipulation, Algorithms

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.5K reviews

      Advanced · Course · 1 - 3 Months

    • Status: Free
      Free
      T

      The University of North Carolina at Chapel Hill

      Epidemiology: The Basic Science of Public Health

      Skills you'll gain: Epidemiology, Public Health and Disease Prevention, Public Health, Infectious Diseases, Research Design, Health Policy, Biostatistics, Science and Research, Health Care, Probability & Statistics, Statistical Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      4.9K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free
      Free
      C

      Coursera Project Network

      Introduction to Data Analysis using Microsoft Excel

      Skills you'll gain: Microsoft Excel, Excel Formulas, Spreadsheet Software, Pivot Tables And Charts, Data Analysis, Data Manipulation, Data Mining

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.9K reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • R

      Rice University

      Business Finance and Data Analysis Fundamentals

      Skills you'll gain: Capital Budgeting, Cash Flows, Financial Statements, Microsoft Excel, Descriptive Statistics, Financial Accounting, Business Analytics, Box Plots, Probability Distribution, Finance, Financial Analysis, Data Visualization, Probability, Statistics, Business Valuation, Financial Statement Analysis, Business Mathematics, Accounting, Return On Investment, General Accounting

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.8K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University at Buffalo

      Advanced Manufacturing Process Analysis

      Skills you'll gain: Anomaly Detection, Process Analysis, Manufacturing Processes, Data Collection, Data Storage, Manufacturing Operations, Systems Engineering, Data Analysis, Industrial Engineering, Analysis, Data Cleansing, Digital Transformation, Big Data, Internet Of Things, Predictive Analytics, Computing Platforms, Cloud Computing

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

      Beginner · Course · 1 - 4 Weeks

    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

    • Fundamentals of GIS: University of California, Davis
    • The Arts and Science of Relationships: Understanding Human Needs: University of Toronto
    • Summary Statistics in Public Health : Johns Hopkins University
    • Advanced Network Security: LearnQuest
    • TensorFlow: Data and Deployment: DeepLearning.AI
    • TensorFlow: Advanced Techniques: DeepLearning.AI
    • Introduction to International Criminal Law: Case Western Reserve University
    • Get Started with Python: Google
    • Epidemiology: The Basic Science of Public Health: The University of North Carolina at Chapel Hill
    • Introduction to Data Analysis using Microsoft Excel: Coursera Project Network

    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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