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    Results for "advanced statistics"

    • S

      S.P. Jain Institute of Management and Research

      Strategic Brand Management

      Skills you'll gain: Brand Strategy, Brand Management, Branding, Brand Awareness, Brand Loyalty, Product Lifecycle Management, Strategic Marketing, Marketing Management, Integrated Marketing Communications, Market Share, Value Propositions, Portfolio Management, Competitive Analysis, Business Metrics, Consumer Behaviour, Storytelling

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      9 reviews

      Beginner · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Linear Algebra: Orthogonality and Diagonalization

      Skills you'll gain: Linear Algebra, Artificial Intelligence and Machine Learning (AI/ML), Numerical Analysis, Geometry, Applied Mathematics, Mathematical Modeling

      4.9
      Rating, 4.9 out of 5 stars
      ·
      36 reviews

      Intermediate · Course · 1 - 4 Weeks

    • G

      GRAMMY GO

      Music Production: Crafting Award-Worthy Songs

      Skills you'll gain: Music Theory, Musical Composition, Music, Instrumental Music, Post-Production, Media Production, Editing, Creativity, System Configuration

      4.2
      Rating, 4.2 out of 5 stars
      ·
      6 reviews

      Beginner · Specialization · 1 - 3 Months

    • U

      University of Illinois Urbana-Champaign

      Accounting for Mergers and Acquisitions: Advanced Topics

      Skills you'll gain: Mergers & Acquisitions, Financial Statement Analysis, Corporate Accounting, Specialized Accounting, Private Equity, Financial Analysis, Corporate Tax, Financial Modeling, Financial Accounting, Accounting, Corporate Finance, Tax, Equities

      4.7
      Rating, 4.7 out of 5 stars
      ·
      21 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      E

      EDUCBA

      Regression & Forecasting for Data Scientists using Python

      Skills you'll gain: Time Series Analysis and Forecasting, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Forecasting, Regression Analysis, Python Programming, Data Analysis, Predictive Modeling, Pandas (Python Package), Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Supervised Learning, Data Cleansing, Data Transformation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      39 reviews

      Intermediate · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Introduction to the Tidyverse

      Skills you'll gain: Tidyverse (R Package), Data Wrangling, Data Cleansing, Data Transformation, Data Manipulation, R Programming, Data Science, Exploratory Data Analysis, Data Analysis, Ggplot2, File Management, Data Import/Export

      4.4
      Rating, 4.4 out of 5 stars
      ·
      50 reviews

      Beginner · Course · 1 - 3 Months

    • F

      Fractal Analytics

      Python for Data Science

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Visualization, Statistical Analysis, Probability & Statistics, Statistics, Data Wrangling, Pandas (Python Package), Data Analysis, Jupyter, Data Processing, Data Manipulation, Data Science, Descriptive Statistics, Data Transformation, Applied Machine Learning, Data Cleansing, Python Programming

      4
      Rating, 4 out of 5 stars
      ·
      45 reviews

      Beginner · Course · 1 - 3 Months

    • Status: New
      New
      U

      University of Michigan

      Statistics with Python Using NumPy, Pandas, and SciPy

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Probability & Statistics, NumPy, Statistical Methods, Probability, Statistics, Probability Distribution, Statistical Analysis, Data Analysis, Histogram, Scatter Plots, Regression Analysis, Pandas (Python Package), Linear Algebra

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      Private Cloud Management on IBM Power Systems

      Skills you'll gain: Private Cloud, IBM Cloud, Cloud Management, Virtual Machines, Infrastructure As A Service (IaaS), Systems Administration, Cloud Infrastructure, Cloud Computing, Virtualization and Virtual Machines, Cloud Computing Architecture, Linux Administration, Identity and Access Management, Configuration Management

      4.9
      Rating, 4.9 out of 5 stars
      ·
      71 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      T

      The Hong Kong University of Science and Technology

      Understanding China, 1700-2000: A Data Analytic Approach, Part 1

      Skills you'll gain: Social Sciences, Socioeconomics, World History, Sociology, Trend Analysis, Data Analysis, Big Data, Research, Economics, Economic Development, Higher Education, Political Sciences, Culture, Data Collection, Education and Training

      4.6
      Rating, 4.6 out of 5 stars
      ·
      124 reviews

      Mixed · Course · 1 - 3 Months

    • K

      Kennesaw State University

      The Control Phase for the 6 σ Black Belt

      Skills you'll gain: Statistical Process Controls, Lean Six Sigma, Six Sigma Methodology, Lean Manufacturing, Process Improvement, Process Capability, Quality Improvement, Continuous Improvement Process, Process Analysis, Variance Analysis, Facility Repair And Maintenance, Document Management, Training Programs

      4.4
      Rating, 4.4 out of 5 stars
      ·
      30 reviews

      Mixed · Course · 1 - 3 Months

    • D

      Databricks

      Introduction to Computational Statistics for Data Scientists

      Skills you'll gain: Bayesian Statistics, Databricks, Sampling (Statistics), Statistical Modeling, Probability, Classification And Regression Tree (CART), Jupyter, Regression Analysis, Statistical Programming, Predictive Modeling, Statistical Analysis, Statistical Machine Learning, Probability Distribution, Data Science, Markov Model, Statistics, NumPy, Simulations, Mathematical Software, Statistical Inference

      4
      Rating, 4 out of 5 stars
      ·
      109 reviews

      Beginner · Specialization · 1 - 3 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…9899100…264

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

    • Strategic Brand Management: S.P. Jain Institute of Management and Research
    • Linear Algebra: Orthogonality and Diagonalization: Johns Hopkins University
    • Music Production: Crafting Award-Worthy Songs: GRAMMY GO
    • Accounting for Mergers and Acquisitions: Advanced Topics: University of Illinois Urbana-Champaign
    • Regression & Forecasting for Data Scientists using Python: EDUCBA
    • Introduction to the Tidyverse: Johns Hopkins University
    • Python for Data Science: Fractal Analytics
    • Statistics with Python Using NumPy, Pandas, and SciPy: University of Michigan
    • Private Cloud Management on IBM Power Systems: IBM
    • Understanding China, 1700-2000: A Data Analytic Approach, Part 1: The Hong Kong University of Science and Technology

    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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