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    Results for "advanced statistics"

    • I

      Imperial College London

      Statistical Analysis with R for Public Health

      Skills you'll gain: Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Software, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Statistical Programming, Statistics, Statistical Methods, Public Health, Probability & Statistics, Epidemiology, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      Duke University

      Introduction to Probability and Data with R

      Skills you'll gain: Sampling (Statistics), Exploratory Data Analysis, Statistical Inference, Probability Distribution, Bayesian Statistics, R Programming, Data Analysis, Probability, Statistics, Statistical Analysis, Statistical Software, Descriptive Statistics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.7K reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      Introduction to Data Science

      Skills you'll gain: SQL, Jupyter, Data Literacy, Data Mining, Peer Review, Data Modeling, Databases, Stored Procedure, Relational Databases, Database Design, Query Languages, Data Science, Database Management, Big Data, Computer Programming Tools, Data Analysis Software, Data Cleansing, GitHub, Cloud Computing, Data Analysis

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      98K reviews

      Beginner · Specialization · 3 - 6 Months

    • I

      IBM

      Data Science Fundamentals with Python and SQL

      Skills you'll gain: Dashboard, SQL, Descriptive Statistics, Jupyter, Statistical Analysis, Data Analysis, Probability Distribution, Pandas (Python Package), Data Visualization Software, Statistics, Data Visualization, Databases, Stored Procedure, Web Scraping, Relational Databases, Computer Programming Tools, Automation, Data Analysis Software, Data Science, Python Programming

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      71K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Skills you'll gain: Bayesian Statistics, Statistical Modeling, Statistical Methods, Markov Model, Statistical Analysis, Regression Analysis, R Programming, Simulations, Statistical Inference, Data Analysis, Probability, Probability Distribution

      4.8
      Rating, 4.8 out of 5 stars
      ·
      489 reviews

      Intermediate · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Clinical Trials Management and Advanced Operations

      Skills you'll gain: Clinical Trials, Healthcare Ethics, Clinical Research, Regulatory Compliance, Patient Safety, Scientific Methods, Record Keeping, Safety Audits, Risk Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      91 reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      Applied Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Plotly, Data Wrangling, Data Visualization, Interactive Data Visualization, Exploratory Data Analysis, Data Cleansing, Jupyter, Matplotlib, Data Analysis, Pandas (Python Package), Data Manipulation, Seaborn, Data Import/Export, Predictive Modeling, Web Scraping, Automation, Data Science, Python Programming

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      58K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Amsterdam

      Inferential Statistics

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Regression Analysis, Statistical Inference, Statistical Analysis, Quantitative Research, Statistical Modeling, Probability Distribution, R Programming

      4.3
      Rating, 4.3 out of 5 stars
      ·
      597 reviews

      Mixed · Course · 1 - 3 Months

    • Status: New
      New
      I

      IBM

      IBM Relational Database Administrator

      Skills you'll gain: Data Warehousing, SQL, Extract, Transform, Load, Apache Airflow, Web Scraping, Linux Commands, Database Design, Database Administration, MySQL, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Shell Script, Database Architecture and Administration, Data Transformation, Data Store, IBM DB2, Jupyter, Generative AI

      4.6
      Rating, 4.6 out of 5 stars
      ·
      57K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • U

      University of Colorado Boulder

      Statistics and Data Analysis with Excel, Part 1

      Skills you'll gain: Descriptive Statistics, Statistical Visualization, Data Transformation, Data Cleansing, Probability, Box Plots, Histogram, Probability Distribution, Probability & Statistics, Scatter Plots, Statistics, Microsoft Excel, Excel Formulas, Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      27 reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      Data Engineering Foundations

      Skills you'll gain: SQL, Web Scraping, Database Design, MySQL, Data Transformation, Data Store, Extract, Transform, Load, IBM DB2, Relational Databases, Data Architecture, Jupyter, Data Pipelines, Big Data, Data Warehousing, Data Governance, Databases, Stored Procedure, Data Manipulation, Automation, Python Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      56K reviews

      Beginner · Specialization · 3 - 6 Months

    • A

      American Psychological Association

      Statistics in Psychological Research

      Skills you'll gain: Descriptive Statistics, Plot (Graphics), Graphing, Statistical Visualization, Statistical Hypothesis Testing, Probability & Statistics, Statistical Inference, Quantitative Research, Statistics, Sample Size Determination, Data Analysis, Scatter Plots, Psychology, Research Design, Experimentation, Scientific Methods

      4.6
      Rating, 4.6 out of 5 stars
      ·
      44 reviews

      Beginner · Course · 1 - 3 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…91011…260

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

    • Statistical Analysis with R for Public Health: Imperial College London
    • Introduction to Probability and Data with R: Duke University
    • Introduction to Data Science: IBM
    • Data Science Fundamentals with Python and SQL: IBM
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Clinical Trials Management and Advanced Operations: Johns Hopkins University
    • Applied Data Science: IBM
    • Inferential Statistics: University of Amsterdam
    • IBM Relational Database Administrator: IBM
    • Statistics and Data Analysis with Excel, Part 1: University of Colorado Boulder

    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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