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    • Applied Statistics

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    1085 results for "applied statistics"

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      DeepLearning.AI

      Supervised Machine Learning: Regression and Classification

      Skills you'll gain: Machine Learning, Probability & Statistics, Regression, General Statistics, Machine Learning Algorithms, Algorithms, Theoretical Computer Science, Econometrics, Computer Programming, Mathematics, Python Programming, Statistical Machine Learning, Statistical Programming, Applied Machine Learning, Artificial Neural Networks, Calculus, Feature Engineering, Linear Algebra, Probability Distribution, Accounting

      4.9

      (8.2k reviews)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      Skills you'll gain: Machine Learning, Deep Learning, Tensorflow, Artificial Neural Networks, Computer Vision, Data Science, Computer Programming, Python Programming, Statistical Programming, General Statistics, Natural Language Processing, Probability & Statistics, Business Psychology, Entrepreneurship, Forecasting, Applied Machine Learning, Communication, Machine Learning Algorithms, Marketing, Calculus, Data Visualization, Mathematics, Programming Principles, Statistical Machine Learning, Computer Graphic Techniques, Computer Graphics, Machine Learning Software

      4.7

      (23.3k reviews)

      Intermediate · Professional Certificate · 3-6 Months

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      IBM Skills Network

      IBM AI Engineering

      Skills you'll gain: Machine Learning, Computer Programming, Python Programming, Computer Vision, Deep Learning, Statistical Programming, Artificial Neural Networks, Machine Learning Algorithms, Probability & Statistics, General Statistics, Regression, Applied Machine Learning, Apache, Data Management, Data Mining, Data Analysis, Statistical Analysis, Big Data, Algorithms, Theoretical Computer Science, Statistical Machine Learning, Computer Graphics, Dimensionality Reduction, Tensorflow, Computer Graphic Techniques, Basic Descriptive Statistics, Business Analysis, Correlation And Dependence, Databases, Mathematics, NoSQL, SQL, Econometrics, Estimation, Entrepreneurship, Machine Learning Software, Probability Distribution, Data Science, Data Structures, IBM Cloud, Supply Chain Systems, Supply Chain and Logistics

      4.6

      (16k reviews)

      Intermediate · Professional Certificate · 3-6 Months

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      DeepLearning.AI

      Machine Learning Engineering for Production (MLOps)

      Skills you'll gain: Machine Learning, Applied Machine Learning, DevOps, Python Programming, Statistical Programming, Tensorflow, Exploratory Data Analysis, Feature Engineering, Probability & Statistics, Cloud Computing, Data Management, Data Warehousing, Extract, Transform, Load, Computer Programming, Computer Vision, Deep Learning, Advertising, Business Analysis, Change Management, Communication, Computer Networking, Data Analysis, Data Visualization, Estimation, General Statistics, Leadership and Management, Machine Learning Algorithms, Marketing, Network Security, Security Engineering, Security Strategy, Statistical Visualization, Strategy and Operations

      4.7

      (2.9k reviews)

      Advanced · Specialization · 3-6 Months

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      DeepLearning.AI

      Natural Language Processing

      Skills you'll gain: Machine Learning, Natural Language Processing, Statistical Programming, Python Programming, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Data Science, Statistical Machine Learning, Human Computer Interaction, Probability & Statistics, User Experience, Algorithms, Bayesian Statistics, Communication, Computer Graphics, Computer Programming, Dimensionality Reduction, Experiment, General Statistics, Machine Learning Software, Markov Model, Mathematics, Operations Research, Regression, Research and Design, Strategy and Operations, Theoretical Computer Science

      4.6

      (5k reviews)

      Intermediate · Specialization · 3-6 Months

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      University of Pennsylvania

      Business Analytics

      Skills you'll gain: Business Analysis, Data Analysis, Probability & Statistics, Statistical Analysis, General Statistics, Research and Design, Forecasting, Strategy and Operations, Correlation And Dependence, Financial Analysis, Accounting, Human Resources, Marketing, Operational Analysis, Operations Management, Operations Research, Probability Distribution, Spreadsheet Software, Supply Chain and Logistics, Customer Analysis, Financial Accounting, Market Analysis, Market Research, Basic Descriptive Statistics, Exploratory Data Analysis, Finance, People Management, Performance Management, Regulations and Compliance, Statistical Tests, Talent Management, Collaboration, Communication, Critical Thinking, Data Management, Data Mining, Data Model, Data Visualization, Generally Accepted Accounting Principles (GAAP), HR Tech, Leadership Development, Leadership and Management, MarTech, Marketing Management, Media Strategy & Planning, Microsoft Excel, Organizational Development, Plot (Graphics), Process Analysis, Recruitment, Statistical Programming, Statistical Visualization, Applied Mathematics, Big Data, Business Psychology, Computational Logic, Computer Programming, Computer Programming Tools, Data Analysis Software, Data Structures, Decision Making, Entrepreneurship, Estimation, Mathematics, Network Analysis, People Analysis, People Development, Regression, Sales, Strategy, Theoretical Computer Science

      4.6

      (17.2k reviews)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      Advanced Learning Algorithms

      Skills you'll gain: Machine Learning, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Applied Machine Learning, Computer Programming, Python Programming, Statistical Programming, Theoretical Computer Science, Tensorflow, Algorithms, Data Management, Data Structures, Probability & Statistics, General Statistics, Statistical Machine Learning, Computer Vision, Mathematics, Probability Distribution, Computational Logic, Linear Algebra, Mathematical Theory & Analysis

      4.9

      (2k reviews)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI

      Neural Networks and Deep Learning

      Skills you'll gain: Artificial Neural Networks, Deep Learning, Machine Learning, Machine Learning Algorithms, Python Programming, Linear Algebra, Regression, General Statistics, Probability & Statistics, Business Psychology, Computer Programming, Dimensionality Reduction, Entrepreneurship, Feature Engineering, Statistical Programming, Supply Chain Systems, Supply Chain and Logistics, Applied Machine Learning, Mathematics, Statistical Machine Learning, Machine Learning Software, Bayesian Statistics, Statistical Tests, Algebra, Algorithms, Computational Logic, Computer Architecture, Computer Networking, Data Structures, Estimation, Hardware Design, Markov Model, Mathematical Theory & Analysis, Network Model, Theoretical Computer Science

      4.9

      (117.9k reviews)

      Intermediate · Course · 1-4 Weeks

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      Johns Hopkins University

      Data Science

      Skills you'll gain: R Programming, Data Analysis, Statistical Programming, Data Science, General Statistics, Statistical Analysis, Probability & Statistics, Statistical Tests, Machine Learning, Exploratory Data Analysis, Basic Descriptive Statistics, Machine Learning Software, Linear Algebra, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Regression, Data Visualization Software, Software Visualization, Statistical Visualization, Probability Distribution, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Plot (Graphics), Big Data, Computer Programming, Computer Programming Tools, Data Structures, Experiment, Machine Learning Algorithms, Software Engineering Tools, Spreadsheet Software, Algorithms, Application Development, Applied Machine Learning, Business Analysis, Data Management, Extract, Transform, Load, Knitr

      4.5

      (50k reviews)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      IBM Machine Learning

      Skills you'll gain: Machine Learning, Probability & Statistics, General Statistics, Forecasting, Machine Learning Algorithms, Regression, Deep Learning, Data Analysis, Theoretical Computer Science, Artificial Neural Networks, Statistical Machine Learning, Algorithms, Business Analysis, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Computer Vision, Applied Machine Learning, Bayesian Statistics, NoSQL, Probability Distribution, Human Resources, Leadership Development, Leadership and Management, Data Management, Data Structures, Experiment, Linear Algebra, Mathematics, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Visualization, Natural Language Processing, Python Programming, Reinforcement Learning, Statistical Programming, Statistical Visualization, Algebra, Application Development, Basic Descriptive Statistics, Correlation And Dependence, Data Analysis Software, Estimation, SQL, Software Engineering, Statistical Analysis, Statistical Tests

      4.6

      (1.5k reviews)

      Intermediate · Professional Certificate · 3-6 Months

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      Google Cloud

      Preparing for Google Cloud Certification: Machine Learning Engineer

      Skills you'll gain: Machine Learning, Cloud Computing, Google Cloud Platform, Computer Programming, Cloud Platforms, Statistical Programming, Python Programming, Data Management, Applied Machine Learning, Feature Engineering, Tensorflow, Deep Learning, DevOps, Entrepreneurship, Probability & Statistics, Data Analysis, Big Data, Artificial Neural Networks, Business Psychology, Data Visualization, Exploratory Data Analysis, Regression, SQL, Statistical Visualization, Theoretical Computer Science, Data Science, Kubernetes, Apache, Basic Descriptive Statistics, Bayesian Statistics, Computational Thinking, Computer Architecture, Computer Networking, Data Model, Data Structures, Extract, Transform, Load, General Statistics, Hardware Design, Machine Learning Algorithms, Machine Learning Software, Network Security, Performance Management, Security Engineering, Security Strategy, Statistical Machine Learning, Strategy and Operations, Algorithms, Business Analysis, Cloud Applications, Cloud Infrastructure, Cloud Storage, Data Analysis Software, Data Architecture, Data Warehousing, Database Application, Databases, Dimensionality Reduction, Distributed Computing Architecture, Full-Stack Web Development, Information Technology, Natural Language Processing, Statistical Analysis, Web Development

      4.6

      (25.1k reviews)

      Intermediate · Professional Certificate · 3-6 Months

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      IBM Skills Network

      IBM Introduction to Machine Learning

      Skills you'll gain: Machine Learning, Machine Learning Algorithms, Regression, Data Analysis, General Statistics, Probability & Statistics, Theoretical Computer Science, Statistical Machine Learning, Algorithms, Dimensionality Reduction, Business Analysis, Exploratory Data Analysis, Feature Engineering, Bayesian Statistics, NoSQL, Probability Distribution, Human Resources, Leadership Development, Leadership and Management, Applied Machine Learning, Computer Vision, Data Management, Data Structures, Experiment, Linear Algebra, Mathematics, Algebra, Basic Descriptive Statistics, Computer Programming, Correlation And Dependence, Data Analysis Software, Data Visualization, Deep Learning, Estimation, Python Programming, SQL, Statistical Analysis, Statistical Programming, Statistical Tests

      4.6

      (1.4k reviews)

      Intermediate · Specialization · 3-6 Months

    1234…84

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

    • Supervised Machine Learning: Regression and Classification: DeepLearning.AI
    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • IBM AI Engineering: IBM Skills Network
    • Machine Learning Engineering for Production (MLOps): DeepLearning.AI
    • Natural Language Processing: DeepLearning.AI
    • Business Analytics: University of Pennsylvania
    • Advanced Learning Algorithms: DeepLearning.AI
    • Neural Networks and Deep Learning: DeepLearning.AI
    • Data Science: Johns Hopkins University
    • IBM Machine Learning: 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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