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    • Applied Statistics

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    1080 results for "applied statistics"

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      University of Toronto

      Self-Driving Cars

      Skills you'll gain: Computer Programming, Python Programming, Algorithms, Machine Learning, Theoretical Computer Science, Applied Machine Learning, Computer Science, Data Science, General Statistics, Probability & Statistics, Probability Distribution, Artificial Neural Networks, Linear Algebra, Mathematics, Application Development, Applied Mathematics, Calculus, Computational Logic, Differential Equations, Geometry, Machine Learning Algorithms, Software Engineering, Communication, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Vision, Decision Making, Deep Learning, Entrepreneurship, Estimation, Feature Engineering, Graph Theory, Leadership and Management, Machine Learning Software, Mathematical Theory & Analysis, Network Model, Planning, Statistical Programming, Supply Chain and Logistics

      4.7

      (3.2k reviews)

      Advanced · Specialization · 3-6 Months

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      Stanford University

      Probabilistic Graphical Models

      Skills you'll gain: Probability & Statistics, Machine Learning, Bayesian Network, General Statistics, Markov Model, Bayesian Statistics, Probability Distribution, Computer Architecture, Distributed Computing Architecture, Leadership and Management, Other Programming Languages, Computer Programming, Machine Learning Algorithms, Statistical Machine Learning, Applied Machine Learning, Correlation And Dependence, Behavioral Economics, Business Psychology, Data Analysis, Graph Theory, Mathematics, Algebra, Geovisualization

      4.6

      (1.5k reviews)

      Advanced · Specialization · 3-6 Months

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      IBM Skills Network

      Advanced Data Science with IBM

      Skills you'll gain: Machine Learning, Data Management, Statistical Programming, Python Programming, Machine Learning Algorithms, Apache, Deep Learning, Machine Learning Software, Artificial Neural Networks, Probability & Statistics, Cloud Computing, Statistical Machine Learning, Extract, Transform, Load, Basic Descriptive Statistics, General Statistics, IBM Cloud, Data Model, Applied Machine Learning, Data Analysis, Data Visualization, Dimensionality Reduction, SQL, Statistical Visualization, Feature Engineering, Linear Algebra, Mathematics, Natural Language Processing, Tensorflow, Bayesian Network, Cloud Platforms, Cloud Storage, Computer Vision, Correlation And Dependence, Data Structures, Data Warehousing, Database Application, NoSQL, Plot (Graphics), Probability Distribution, R Programming, Regression, Algorithms, Bayesian Statistics, Big Data, Change Management, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis Software, Data Mining, Distributed Computing Architecture, Estimation, Exploratory Data Analysis, Internet Of Things, Leadership and Management, Programming Principles, Statistical Analysis, Strategy and Operations, Theoretical Computer Science

      4.3

      (3k reviews)

      Advanced · Specialization · 3-6 Months

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      University of California San Diego

      Introduction to Discrete Mathematics for Computer Science

      Skills you'll gain: Mathematics, Graph Theory, Computer Science, Computer Programming, Python Programming, Statistical Programming, Data Science, Algebra, Theoretical Computer Science, Probability & Statistics, Algorithms, Calculus, Combinatorics, Data Analysis, Statistical Analysis, General Statistics, Mathematical Theory & Analysis, Programming Principles, Bayesian Statistics, Computational Thinking, Geometry, Applied Mathematics, Correlation And Dependence, Estimation, Probability Distribution, Computational Logic, Business Analysis, Communication, Computer Architecture, Critical Thinking, Cryptography, Data Visualization, Entrepreneurship, Leadership and Management, Machine Learning, Markov Model, Microarchitecture, Problem Solving, Research and Design, Scientific Visualization, Security Engineering, Statistical Visualization, Strategy and Operations

      4.5

      (3.3k reviews)

      Beginner · Specialization · 3-6 Months

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      Johns Hopkins University

      Data Science: Statistics and Machine Learning

      Skills you'll gain: R Programming, Statistical Programming, General Statistics, Statistical Analysis, Data Analysis, Machine Learning, Probability & Statistics, Statistical Tests, Data Science, Machine Learning Software, Basic Descriptive Statistics, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Linear Algebra, Regression, Exploratory Data Analysis, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Probability Distribution, Plot (Graphics), Machine Learning Algorithms, Algorithms, Applied Machine Learning, Business Analysis

      4.4

      (7.1k reviews)

      Intermediate · Specialization · 3-6 Months

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      University of Colorado Boulder

      Data Science Foundations: Statistical Inference

      Skills you'll gain: General Statistics, Probability & Statistics, Probability Distribution, Statistical Tests, Calculus, Estimation, Mathematics, Business Analysis, Differential Equations, Econometrics, Spreadsheet Software

      4.4

      (138 reviews)

      Intermediate · Specialization · 3-6 Months

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      University of Michigan

      Sports Performance Analytics

      Skills you'll gain: Data Analysis, Business Analysis, Probability & Statistics, Statistical Analysis, Computer Programming, General Statistics, Python Programming, Statistical Programming, Regression, Machine Learning, Machine Learning Algorithms, Econometrics, Applied Machine Learning, Correlation And Dependence, Data Visualization, Plot (Graphics), Critical Thinking, Research and Design, Strategy and Operations, Basic Descriptive Statistics, Bayesian Statistics, Advertising, Communication, Marketing

      4.4

      (172 reviews)

      Intermediate · Specialization · 3-6 Months

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      University of California San Diego

      Big Data

      Skills you'll gain: Data Management, Big Data, Data Analysis, Exploratory Data Analysis, Probability & Statistics, Distributed Computing Architecture, Machine Learning, Business Analysis, Statistical Programming, Data Science, Graph Theory, Mathematics, Apache, Computer Architecture, Databases, Data Analysis Software, NoSQL, Data Architecture, Machine Learning Algorithms, Business, Data Model, Data Structures, Spreadsheet Software, Data Mining, Python Programming, Data Visualization, SQL, Statistical Machine Learning, Statistical Visualization, Database Application, Information Technology, Cloud Computing, Software As A Service, Applied Machine Learning, Basic Descriptive Statistics, Computer Programming, Correlation And Dependence, Database Administration, Dimensionality Reduction, Feature Engineering, General Statistics, PostgreSQL, Regression, Statistical Analysis, Algorithms, Data Warehousing, Theoretical Computer Science

      4.5

      (13.5k reviews)

      Beginner · Specialization · 3-6 Months

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      Georgia Institute of Technology

      Fundamentals of Engineering Exam Review

      Skills you'll gain: Algebra, Entrepreneurship, Leadership and Management, Problem Solving, Research and Design, Geometry, Mathematics, Applied Mathematics, Calculus, Computer Graphic Techniques, Computer Graphics, Differential Equations, General Statistics, Graphics Software, Linear Algebra, Probability & Statistics, Probability Distribution, Mathematical Theory & Analysis

      4.7

      (596 reviews)

      Mixed · Course · 1-3 Months

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      IBM Skills Network

      Machine Learning with Python

      Skills you'll gain: Data Mining, Machine Learning, Machine Learning Algorithms, Python Programming, General Statistics, Applied Machine Learning, Data Analysis, Regression, Statistical Analysis, Statistical Machine Learning, Deep Learning, Probability & Statistics, Estimation, Algorithms, Data Management, Data Structures, Entrepreneurship, Supply Chain Systems, Supply Chain and Logistics

      4.7

      (13.8k reviews)

      Intermediate · Course · 1-3 Months

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      Northwestern University

      Modern Robotics: Mechanics, Planning, and Control

      Skills you'll gain: Mathematics, Linear Algebra, Calculus, Theoretical Computer Science, Algebra, Applied Mathematics, Computer Graphic Techniques, Computer Programming, Differential Equations, Entrepreneurship, Leadership and Management, Planning, Supply Chain and Logistics, Algorithms, General Statistics, Geometry, Graph Theory, Human Computer Interaction, Probability & Statistics, Virtual Reality, Business Analysis, Critical Thinking, Data Analysis, Data Analysis Software, Matlab, Operating Systems, Project Management, Python Programming, Research and Design, Strategy and Operations, Systems Design

      4.7

      (985 reviews)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Sequence Models

      Skills you'll gain: Artificial Neural Networks, Deep Learning, Machine Learning, Natural Language Processing, Applied Machine Learning, Dimensionality Reduction, Feature Engineering, Linear Algebra, Machine Learning Algorithms, Probability & Statistics, Python Programming, Statistical Machine Learning, Statistical Programming, Differential Equations, Advertising, Business Psychology, Communication, Entrepreneurship, Estimation, Forecasting, Marketing, Applied Mathematics, General Statistics, Tensorflow, Algorithms, Big Data, Data Management, Human Computer Interaction, Machine Learning Software, Theoretical Computer Science, User Experience, Computer Graphics, Interactive Design

      4.8

      (29.2k reviews)

      Intermediate · Course · 1-4 Weeks

    1…456…84

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

    • Self-Driving Cars: University of Toronto
    • Probabilistic Graphical Models: Stanford University
    • Advanced Data Science with IBM: IBM Skills Network
    • Introduction to Discrete Mathematics for Computer Science: University of California San Diego
    • Data Science: Statistics and Machine Learning: Johns Hopkins University
    • Data Science Foundations: Statistical Inference: University of Colorado Boulder
    • Sports Performance Analytics: University of Michigan
    • Big Data: University of California San Diego
    • Fundamentals of Engineering Exam Review: Georgia Institute of Technology
    • Machine Learning with Python: IBM Skills Network

    Frequently Asked Questions about Applied Statistics

    • Applied statistics is the use of statistical techniques to solve real-world data analysis problems. In contrast to the pure study of mathematical statistics, applied statistics is typically used by and for non-mathematicians in fields ranging from social science to business. Indeed, in the big data era, applied statistics has become important for deriving insights and guiding decision-making in virtually every industry.

      The increased reliance on data and statistics to help understand our world has made the careful application of these techniques even more essential; too often, statistics can be used erroneously or even misleadingly when methods of analysis are not properly connected to research questions. Thus, a major aspect of applied statistics is the accurate communication of findings for a non-technical audience, including specifics about data sources, relevance to the problem at hand, and degrees of uncertainty.

      That said, the statistical approaches used in this field are the same as in the study of mathematical statistics. Rigorous use of statistical hypothesis testing, statistical inference, linear regression techniques, and analysis of variance (ANOVA) are core to the work of applied statistics. And, as in other areas of data science, Python programming and R programming are often used to analyze large datasets when Microsoft Excel is not sufficiently powerful.‎

    • Demand for data-driven insights is growing fast across all fields, making a background in applied statistics the gateway to a wide variety of careers. Financial institutions and companies of all kinds rely on business analytics to guide investments and operations; political candidates and advocacy groups need to conduct surveys and understand public polling data to understand popular opinion on today’s issues; and even sports teams are increasingly hiring experts in applied statistics to make decisions regarding personnel as well as in-game strategy.

      While many jobs in applied statistics may require only a bachelor’s degree in fields such as mathematics or computer science, high-level roles often expect a master’s degree in statistics. According to the Bureau of Labor Statistics, professional statisticians earn a median annual salary of $91,160 as of May 2019, and these jobs are expected to grow much faster than average due to the need to analyze fast-growing volumes of electronic data.‎

    • Yes, with absolute certainty. Coursera offers courses and Specializations in applied statistics for business, social science, and other areas, as well as related topics such as data science and Python programming. These courses are offered by top-ranked universities and leading companies from around the world, including the University of Michigan, the University of Amsterdam, and the University of Virginia, and IBM. Regardless of whether you’re a student looking to learn more about this exciting field or a mid-career professional upgrading their skill set, the combination of a high-quality education and the flexibility of learning online makes Coursera a great choice.‎

    • It's very helpful to have strong math skills, analytical skills, and experience solving problems before starting to learn applied statistics. It's also good to have experience and a good comfort level with technology and computers. Previous experience in statistics is also helpful, although not required. You may also benefit from having prior experience using Excel spreadsheets as you begin to learn applied statistics.‎

    • People best suited for roles in applied statistics are analytical thinkers. They enjoy problem-solving by taking available data and analyzing it to arrive at solutions. They also have effective communication skills so that information can flow clearly to all stakeholders within an organization. Organization and multitasking come easily to people best suited for roles in applied statistics because these individuals need to deal with large amounts of information and manage their time and resources efficiently. People well suited for these roles also pay close attention to detail to make sure the outcomes they're tasked with delivering meet or exceed expectations.‎

    • While the use of applied statistics can be found in almost every industry, learning applied statistics may be especially interesting to you if you're seeking a career in the insurance, web analytics, or energy sectors. These are some of the top industries that currently utilize applied statistics. However, a person in any position in which data is gathered and analyzed to create solutions, innovations, or improvements would benefit from learning applied statistics, from coaches and hospital administrators to bloggers, data scientists, and bankers. If you would like to know how to ensure you're collecting the right data, how to analyze data correctly, and how to effectively report your findings so they can be applied in real-world situations, learning applied statistics may be 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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