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    Results for "advanced statistics"

    • 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

    • U

      University of Michigan

      User Experience Research and Design

      Skills you'll gain: UI/UX Research, Surveys, Qualitative Research, Usability Testing, User Experience Design, User Experience, User Research, User Story, Prototyping, Wireframing, User Centered Design, Survey Creation, Sampling (Statistics), Design Research, Interaction Design, Ideation, A/B Testing, Conceptual Design, User Interface (UI), Test Case

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.2K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      Universidade de São Paulo

      Marketing Digital

      Skills you'll gain: Search Engine Marketing, Digital Marketing, Email Marketing, Google Ads, Marketing Strategies, Search Engine Optimization, Google Analytics, Online Advertising, A/B Testing, Pay Per Click Advertising, Advertising Campaigns, Web Analytics, Digital Advertising, Facebook, Campaign Management, Marketing Analytics, Return On Investment, Performance Measurement

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.3K reviews

      Beginner · Course · 1 - 3 Months

    • L

      LearnQuest

      Advanced Java

      Skills you'll gain: Apache Maven, Spring Framework, Microservices, Spring Boot, Java, Back-End Web Development, Software Architecture, Server Side, Load Balancing, Cloud Development, Restful API, Systems Architecture, Web Applications, Scalability

      4.4
      Rating, 4.4 out of 5 stars
      ·
      48 reviews

      Advanced · Course · 1 - 4 Weeks

    • T

      The Hong Kong University of Science and Technology

      Mathematics for Engineers

      Skills you'll gain: Differential Equations, Linear Algebra, Matlab, Engineering Calculations, Engineering Analysis, Numerical Analysis, Finite Element Methods, Integral Calculus, Mathematical Software, Mechanical Engineering, Calculus, Algebra, Applied Mathematics, Mathematical Modeling, Engineering, Simulation and Simulation Software, Advanced Mathematics, Geometry, Computational Thinking, Estimation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Toronto

      GIS, Mapping, and Spatial Analysis

      Skills you'll gain: ArcGIS, Spatial Data Analysis, Spatial Analysis, Geographic Information Systems, Geospatial Mapping, GIS Software, Data Mapping, Data Visualization, Metadata Management, Query Languages, Global Positioning Systems, Quantitative Research, Data Compilation, Data Modeling, Typography, Data Manipulation, Data Processing, Data Storytelling, Design Elements And Principles, Image Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3K reviews

      Beginner · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Advanced Linear Models for Data Science 2: Statistical Linear Models

      Skills you'll gain: Regression Analysis, Linear Algebra, R Programming, Probability Distribution, Statistical Modeling, Mathematical Modeling, Probability & Statistics, Applied Mathematics, Statistical Analysis, Integral Calculus

      4.6
      Rating, 4.6 out of 5 stars
      ·
      101 reviews

      Advanced · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Advanced R Programming

      Skills you'll gain: Object Oriented Programming (OOP), R Programming, Debugging, Functional Design, Programming Principles, Data Manipulation, Data Structures, Software Design Patterns, Performance Tuning

      4.2
      Rating, 4.2 out of 5 stars
      ·
      574 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      Y

      Yale University

      A Law Student's Toolkit

      Skills you'll gain: Legal Strategy, Legal Research, Legal Writing, Case Law, Civil Procedures, Policy Analysis, Learning Strategies, Economics

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

      Mixed · Course · 1 - 3 Months

    • Status: Free
      Free
      T

      The University of Edinburgh

      Fundamentals of Music Theory

      Skills you'll gain: Music Theory, Music, Classical Music, Musical Composition

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

      Beginner · 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

    • I

      IBM

      Applied Data Science Capstone

      Skills you'll gain: Plotly, Exploratory Data Analysis, Predictive Modeling, Data Science, Data-Driven Decision-Making, Data Presentation, Data Analysis, Pandas (Python Package), Web Scraping, Statistical Modeling, Data Wrangling, Machine Learning Methods, Data Collection

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.3K reviews

      Intermediate · Course · 1 - 3 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…171819…262

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

    • TensorFlow: Advanced Techniques: DeepLearning.AI
    • User Experience Research and Design: University of Michigan
    • Marketing Digital: Universidade de São Paulo
    • Advanced Java: LearnQuest
    • Mathematics for Engineers: The Hong Kong University of Science and Technology
    • GIS, Mapping, and Spatial Analysis: University of Toronto
    • Advanced Linear Models for Data Science 2: Statistical Linear Models: Johns Hopkins University
    • Advanced R Programming: Johns Hopkins University
    • A Law Student's Toolkit: Yale University
    • Fundamentals of Music Theory: The University of Edinburgh

    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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