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    Results for "advanced statistics"

    • D

      DeepLearning.AI

      Natural Language Processing with Attention Models

      Skills you'll gain: Natural Language Processing, PyTorch (Machine Learning Library), Keras (Neural Network Library), Deep Learning, Tensorflow, Machine Learning Methods, Artificial Intelligence, Artificial Neural Networks, Text Mining, Data Processing

      4.4
      Rating, 4.4 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      M

      Microsoft

      Microsoft Python Development

      Skills you'll gain: Web Scraping, Data Structures, Git (Version Control System), Generative AI, Version Control, Matplotlib, Plotly, DevOps, Agile Methodology, Web Development, Data Ethics, Flask (Web Framework), Scripting, GitHub, Debugging, Data Cleansing, Data Manipulation, Restful API, Docker (Software), Test Driven Development (TDD)

      4.3
      Rating, 4.3 out of 5 stars
      ·
      156 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of Michigan

      Applied Text Mining in Python

      Skills you'll gain: Text Mining, Natural Language Processing, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data Processing, Feature Engineering, Data Manipulation, Data Cleansing, Unsupervised Learning, Supervised Learning

      4.2
      Rating, 4.2 out of 5 stars
      ·
      3.8K reviews

      Intermediate · Course · 1 - 4 Weeks

    • B

      Berklee

      Developing Your Musicianship

      Skills you'll gain: Musical Composition, Music Theory, Peer Review, Music, Music Performance, Instrumental Music, Creativity

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.6K reviews

      Beginner · Specialization · 3 - 6 Months

    • É

      École Polytechnique Fédérale de Lausanne

      Digital Signal Processing

      Skills you'll gain: Digital Communications, Image Analysis, Telecommunications, Electrical Engineering, Electrical and Computer Engineering, Embedded Systems, Electronic Systems, Electronics Engineering, Computer Engineering, Engineering Calculations, Electronics, Programming Principles, Engineering Analysis, Numerical Analysis, Advanced Mathematics, Algorithms, Linear Algebra, Mathematical Modeling, Calculus, Applied Mathematics

      4.5
      Rating, 4.5 out of 5 stars
      ·
      777 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      AI for Good

      Skills you'll gain: Disaster Recovery, Data Analysis, Jupyter, Exploratory Data Analysis, Data Ethics, Image Analysis, Emergency Response, Artificial Intelligence, Machine Learning, Predictive Modeling, Applied Machine Learning, Mitigation, Text Mining, Environmental Monitoring, Natural Language Processing, Unstructured Data, Information Privacy, Deep Learning, Data Processing, Forecasting

      4.7
      Rating, 4.7 out of 5 stars
      ·
      459 reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: Free
      Free
      K

      Korea Advanced Institute of Science and Technology(KAIST)

      MRI Fundamentals

      Skills you'll gain: Magnetic Resonance Imaging, Medical Imaging, Medical Equipment and Technology, Image Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      448 reviews

      Intermediate · Course · 1 - 3 Months

    • M

      Meta

      Introduction to Data Analytics

      Skills you'll gain: Data Storytelling, Business Metrics, Key Performance Indicators (KPIs), Data Cleansing, Data Modeling, Analytics, Data Analysis, Data Visualization, Data Validation, Exploratory Data Analysis, Data Quality, Data Manipulation, Generative AI, Data Collection

      4.8
      Rating, 4.8 out of 5 stars
      ·
      819 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Pennsylvania

      A Crash Course in Causality: Inferring Causal Effects from Observational Data

      Skills you'll gain: R Programming, Statistical Analysis, Statistical Methods, Statistical Software, Statistical Modeling, Statistical Inference, Data Analysis, Probability & Statistics, Regression Analysis, Research Design, Graph Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      564 reviews

      Intermediate · Course · 1 - 3 Months

    • M

      Microsoft

      Microsoft Azure Data Scientist Associate (DP-100) Exam Prep

      Skills you'll gain: Databricks, Unsupervised Learning, PySpark, Microsoft Azure, Apache Spark, Scikit Learn (Machine Learning Library), MLOps (Machine Learning Operations), PyTorch (Machine Learning Library), Exploratory Data Analysis, Deep Learning, Data Visualization, Applied Machine Learning, Regression Analysis, Data Science, Predictive Modeling, Data Analysis, Image Analysis, Pandas (Python Package), Artificial Intelligence and Machine Learning (AI/ML), Cloud Computing

      4.2
      Rating, 4.2 out of 5 stars
      ·
      545 reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • U

      University of Colorado Boulder

      Foundations of Data Structures and Algorithms

      Skills you'll gain: Theoretical Computer Science, Algorithms, Graph Theory, Data Structures, Operations Research, Public Key Cryptography Standards (PKCS), Cryptography, Programming Principles, Computational Thinking, Design Strategies, Applied Mathematics, Computer Science, Encryption, Network Model, Linear Algebra, Combinatorics, Mathematical Modeling, Tree Maps, Advanced Mathematics, Analysis

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      785 reviews

      Advanced · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Regression Models

      Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Data Science, Predictive Modeling, Probability & Statistics, Statistical Inference

      4.4
      Rating, 4.4 out of 5 stars
      ·
      3.4K reviews

      Mixed · 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

    • Natural Language Processing with Attention Models: DeepLearning.AI
    • Microsoft Python Development: Microsoft
    • Applied Text Mining in Python: University of Michigan
    • Developing Your Musicianship: Berklee
    • Digital Signal Processing: École Polytechnique Fédérale de Lausanne
    • AI for Good: DeepLearning.AI
    • MRI Fundamentals: Korea Advanced Institute of Science and Technology(KAIST)
    • Introduction to Data Analytics: Meta
    • A Crash Course in Causality: Inferring Causal Effects from Observational Data: University of Pennsylvania
    • Microsoft Azure Data Scientist Associate (DP-100) Exam Prep: Microsoft

    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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