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    Results for "advanced statistics"

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      Google

      Troubleshooting and Debugging Techniques

      Skills you'll gain: Debugging, Technical Communication, Network Troubleshooting, Performance Tuning, Problem Management, Technical Documentation, Technical Support, System Monitoring, System Support, Incident Management, Application Performance Management, Continuous Integration, Scripting

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      G

      Google

      Google Prompting Essentials

      Skills you'll gain: Data Visualization, Prompt Engineering, Data Presentation, Data Storytelling, Sampling (Statistics), Presentations, Graphing, Generative AI Agents, ChatGPT, Data Ethics, AI Personalization, Generative AI, Brainstorming, Timelines, Excel Formulas, Data Analysis, Taking Meeting Minutes, Concision, Oral Expression, Artificial Intelligence

      4.8
      Rating, 4.8 out of 5 stars
      ·
      197 reviews

      Beginner · Specialization · 1 - 4 Weeks

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      U

      University of Michigan

      Understanding and Visualizing Data with Python

      Skills you'll gain: Sampling (Statistics), Data Visualization, Statistics, Matplotlib, Statistical Visualization, Probability & Statistics, Jupyter, Statistical Methods, Data Visualization Software, Data Analysis, Statistical Analysis, Exploratory Data Analysis, Descriptive Statistics, Statistical Inference, Data Collection, NumPy, Histogram, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
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      2.7K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      E

      EDHEC Business School

      Investment Management with Python and Machine Learning

      Skills you'll gain: Investment Management, Portfolio Management, Text Mining, Asset Management, Network Analysis, Data Visualization Software, Machine Learning Methods, Financial Data, Unstructured Data, Predictive Modeling, Web Scraping, Machine Learning, Advanced Analytics, Financial Statements, Applied Machine Learning, Financial Market, Financial Analysis, Financial Modeling, Return On Investment, Risk Analysis

      4.6
      Rating, 4.6 out of 5 stars
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      1.8K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
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      U

      University of Colorado Boulder

      Excel/VBA for Creative Problem Solving, Part 1

      Skills you'll gain: Excel Macros, Programming Principles, Visual Basic (Programming Language), Data Structures, Program Development, Computer Programming, Spreadsheet Software, Automation, Algorithms, Debugging, Data Management, Data Cleansing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      4.3K reviews

      Beginner · Course · 1 - 3 Months

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      Free Trial
      J

      Johns Hopkins University

      Getting and Cleaning Data

      Skills you'll gain: Data Manipulation, Data Cleansing, Data Wrangling, Data Integration, Data Quality, Data Transformation, Data Import/Export, Data Collection, Data Management, Web Scraping, Data Access, R Programming, Exploratory Data Analysis, MySQL, File Management, SQL, Application Programming Interface (API)

      4.5
      Rating, 4.5 out of 5 stars
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      8.1K reviews

      Mixed · Course · 1 - 4 Weeks

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      Multiple educators

      Machine Learning for Trading

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning, Google Cloud Platform, Applied Machine Learning, Financial Trading, Reinforcement Learning, Supervised Learning, Data Pipelines, Time Series Analysis and Forecasting, Statistical Machine Learning, Technical Analysis, Deep Learning, Portfolio Management, Machine Learning Methods, Artificial Neural Networks, Market Trend, Securities Trading, Artificial Intelligence and Machine Learning (AI/ML), Financial Market

      3.9
      Rating, 3.9 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
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      S

      SAS

      Getting Started with SAS Programming

      Skills you'll gain: SAS (Software), Data Import/Export, Data Validation, Data Access, Data Manipulation, Exploratory Data Analysis, Data Analysis, SQL, Data Presentation, Microsoft Excel, Descriptive Statistics

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.1K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      Imperial College London

      Mathematics for Machine Learning: PCA

      Skills you'll gain: Dimensionality Reduction, NumPy, Probability & Statistics, Feature Engineering, Jupyter, Data Science, Statistics, Linear Algebra, Python Programming, Advanced Mathematics, Machine Learning, Calculus

      4
      Rating, 4 out of 5 stars
      ·
      3.1K reviews

      Intermediate · Course · 1 - 4 Weeks

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      Free Trial
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      DeepLearning.AI

      Natural Language Processing with Probabilistic Models

      Skills you'll gain: Natural Language Processing, Markov Model, Text Mining, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Machine Learning Methods, Data Processing, Algorithms, Data Cleansing, Probability & Statistics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.8K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
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      Packt

      Microsoft Project 2021 From Beginners to Advanced

      Skills you'll gain: Microsoft Project, Cost Management, Budget Management, Project Management Software, Resource Allocation, Work Breakdown Structure, Project Schedules, Timelines, Resource Management, Calendar Management, Project Controls, Management Reporting, Project Management, Resource Planning, Excel Macros, Scheduling, Financial Reporting, Consolidation

      Intermediate · Course · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of London

      Statistics for International Business

      Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Data Presentation, Statistics, Estimation, Probability, Data-Driven Decision-Making, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing, Probability Distribution, Data Visualization, Data Analysis, Histogram, Graphing

      3.8
      Rating, 3.8 out of 5 stars
      ·
      308 reviews

      Mixed · Course · 1 - 4 Weeks

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    1…202122…267

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

    • Troubleshooting and Debugging Techniques: Google
    • Google Prompting Essentials: Google
    • Understanding and Visualizing Data with Python: University of Michigan
    • Investment Management with Python and Machine Learning: EDHEC Business School
    • Excel/VBA for Creative Problem Solving, Part 1: University of Colorado Boulder
    • Getting and Cleaning Data: Johns Hopkins University
    • Machine Learning for Trading: Google Cloud
    • Getting Started with SAS Programming: SAS
    • Mathematics for Machine Learning: PCA: Imperial College London
    • Natural Language Processing with Probabilistic Models: DeepLearning.AI

    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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