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    • Applied Statistics

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    1083 results for "applied statistics"

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      Imperial College London

      Mathematics for Machine Learning

      Skills you'll gain: Mathematics, Linear Algebra, Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Mathematical Theory & Analysis, Applied Mathematics, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model

      4.6

      (13.6k reviews)

      Beginner · Specialization · 3-6 Months

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      Google Cloud

      Preparing for Google Cloud Certification: Cloud Data Engineer

      Skills you'll gain: Cloud Computing, Computer Architecture, Data Management, Google Cloud Platform, Cloud Platforms, Machine Learning, Big Data, Distributed Computing Architecture, Hardware Design, SQL, Information Technology, Data Science, Apache, Cloud Storage, Extract, Transform, Load, Cloud Engineering, Cloud Management, Databases, Full-Stack Web Development, Python Programming, Web Development, Computer Programming, Statistical Programming, Applied Machine Learning, Computer Science, Computer Networking, Data Analysis, Data Analysis Software, Data Visualization, Data Warehousing, Database Administration, Database Application, Database Theory, Kubernetes, Network Architecture, Operating Systems, Software Framework, System Programming, Theoretical Computer Science, Bayesian Statistics, Business Psychology, Cloud Applications, Cloud Infrastructure, Computational Thinking, Data Architecture, Data Model, Data Structures, Deep Learning, Entrepreneurship, Exploratory Data Analysis, Feature Engineering, General Statistics, Machine Learning Algorithms, Machine Learning Software, Probability & Statistics, Tensorflow

      4.6

      (17.7k reviews)

      Intermediate · Professional Certificate · 3-6 Months

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      DeepLearning.AI

      Unsupervised Learning, Recommenders, Reinforcement Learning

      Skills you'll gain: Machine Learning, Probability & Statistics, General Statistics, Machine Learning Algorithms, Applied Machine Learning, Mathematics, Reinforcement Learning, Theoretical Computer Science, Econometrics, Algorithms, Data Management, Data Structures, Tensorflow, Artificial Neural Networks, Data Analysis, Data Mining, Mathematical Theory & Analysis, Probability Distribution, Bayesian Statistics, Computer Programming, Operations Research, Python Programming, Research and Design, Statistical Programming, Strategy and Operations, Communication

      4.9

      (945 reviews)

      Beginner · Course · 1-4 Weeks

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      University of Michigan

      Applied Data Science with Python

      Skills you'll gain: Python Programming, Machine Learning, Data Analysis, Data Mining, Data Science, Machine Learning Algorithms, Computer Science, Statistical Programming, Applied Machine Learning, Graph Theory, Mathematics, General Statistics, Basic Descriptive Statistics, Statistical Machine Learning, Data Structures, Natural Language Processing, Regression, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Statistical Tests, Correlation And Dependence, Estimation, Linear Algebra, Computer Programming, Data Architecture, Probability & Statistics, Statistical Visualization, Algorithms, Artificial Neural Networks, Computational Logic, Computer Graphics, Data Visualization, Econometrics, Machine Learning Software, Mathematical Theory & Analysis, Network Analysis, Plot (Graphics), Programming Principles, Theoretical Computer Science

      4.5

      (33.1k reviews)

      Intermediate · Specialization · 3-6 Months

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      Google Cloud

      Data Engineering, Big Data, and Machine Learning on GCP

      Skills you'll gain: Cloud Computing, Data Management, Computer Architecture, Cloud Platforms, Google Cloud Platform, Big Data, Distributed Computing Architecture, Machine Learning, SQL, Apache, Data Science, Hardware Design, Extract, Transform, Load, Cloud Storage, Full-Stack Web Development, Web Development, Databases, Information Technology, Python Programming, Statistical Programming, Computer Networking, Computer Programming, Data Analysis, Data Analysis Software, Data Visualization, Data Warehousing, Database Administration, Database Application, Database Theory, Kubernetes, Network Architecture, Operating Systems, Software Framework, System Programming, Theoretical Computer Science, Applied Machine Learning, Bayesian Statistics, Business Psychology, Cloud Applications, Cloud Infrastructure, Computational Thinking, Data Architecture, Data Model, Data Structures, Deep Learning, Entrepreneurship, Exploratory Data Analysis, Feature Engineering, General Statistics, Machine Learning Algorithms, Machine Learning Software, Probability & Statistics, Tensorflow

      4.6

      (17.5k reviews)

      Intermediate · Specialization · 3-6 Months

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      Duke University

      AI Product Management

      Skills you'll gain: Machine Learning, Machine Learning Algorithms, Applied Machine Learning, Probability & Statistics, Regression, Human Computer Interaction, Data Analysis, Data Mining, Deep Learning, Leadership and Management, Business Psychology, Computer Networking, Database Administration, Databases, Finance, Network Security, Regulations and Compliance, Research and Design, Security Engineering, User Experience, User Experience Design, Artificial Neural Networks, Computer Vision, Natural Language Processing, Statistical Machine Learning, Algorithms, Data Management, Data Science, Data Structures, Design and Product, Econometrics, Entrepreneurship, Feature Engineering, General Statistics, Operating Systems, Product Management, Project Management, Strategy and Operations, Systems Design, Theoretical Computer Science

      4.7

      (181 reviews)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      NoSQL, Big Data, and Spark Foundations

      Skills you'll gain: Big Data, Data Architecture, Apache, Data Management, Databases, NoSQL, Distributed Computing Architecture, Database Theory, Database Administration, Data Structures, Database Application, Data Model, Computer Architecture, Data Analysis, Extract, Transform, Load, Applied Machine Learning, Correlation And Dependence, Feature Engineering, General Statistics, Graph Theory, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Regression, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Database Design, Data Warehousing, SQL, Cloud Applications, Cloud Computing, DevOps, Kubernetes, Network Architecture, Other Programming Languages, Algorithms, Computational Thinking, Computer Networking, Computer Programming, IBM Cloud, Mathematics, Theoretical Computer Science

      4.3

      (338 reviews)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      Applied Data Science with R

      Skills you'll gain: R Programming, Data Analysis, Plot (Graphics), Exploratory Data Analysis, Data Mining, Data Visualization, SQL, Basic Descriptive Statistics, General Statistics, Data Management, Data Visualization Software, Databases, Interactive Data Visualization, Statistical Analysis, Statistical Programming, Statistical Visualization, Probability & Statistics, Data Analysis Software, Database Theory, Regression, Statistical Tests, Big Data, Software Visualization, Data Structures, User Experience, Machine Learning, Probability Distribution, Applied Machine Learning, Deep Learning, Estimation, Geovisualization, Linear Algebra, Machine Learning Algorithms, Machine Learning Software, SAS (Software), Spatial Data Analysis, Statistical Machine Learning, Data Architecture, Data Model, Database Administration, Database Application, Database Design, Visualization (Computer Graphics), Advertising, Business Analysis, Communication, Computational Logic, Computer Programming, Data Science, Extract, Transform, Load, Marketing, Programming Principles, Theoretical Computer Science

      4.5

      (591 reviews)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      AI for Medicine

      Skills you'll gain: Machine Learning, Machine Learning Algorithms, Python Programming, Deep Learning, Machine Learning Software, Statistical Programming, General Statistics, Artificial Neural Networks, Computer Vision, Data Analysis, Probability & Statistics, Algorithms, Applied Machine Learning, Basic Descriptive Statistics, Estimation, Exploratory Data Analysis, Natural Language Processing, Plot (Graphics), Scientific Visualization, Statistical Tests, Statistical Visualization, Theoretical Computer Science, Computer Graphic Techniques, Computer Graphics, Computer Programming, Business Analysis, Data Management, Data Structures, Feature Engineering, Statistical Analysis

      4.7

      (2.2k reviews)

      Intermediate · Specialization · 1-3 Months

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      DeepLearning.AI

      Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

      Skills you'll gain: Deep Learning, Machine Learning, Probability & Statistics, Statistical Machine Learning, Statistical Programming, Artificial Neural Networks, Machine Learning Algorithms, Python Programming, General Statistics, Linear Algebra, Applied Machine Learning, Applied Mathematics, Dimensionality Reduction, Feature Engineering, Machine Learning Software, Mathematical Theory & Analysis, Mathematics, Statistical Visualization, Computer Programming, Tensorflow, Algorithms, Calculus, Probability Distribution, Regression, Theoretical Computer Science

      4.9

      (61.9k reviews)

      Intermediate · Course · 1-4 Weeks

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      Google Cloud

      Machine Learning on Google Cloud

      Skills you'll gain: Machine Learning, Cloud Computing, Computer Programming, Statistical Programming, Python Programming, Applied Machine Learning, Feature Engineering, Google Cloud Platform, Tensorflow, Deep Learning, Probability & Statistics, Data Analysis, Entrepreneurship, Artificial Neural Networks, Data Visualization, Exploratory Data Analysis, Regression, SQL, Statistical Visualization, Data Science, Apache, Basic Descriptive Statistics, Bayesian Statistics, Computational Thinking, Computer Architecture, Data Management, General Statistics, Hardware Design, Machine Learning Algorithms, Machine Learning Software, Statistical Machine Learning, Theoretical Computer Science, Algorithms, Business Analysis, Business Psychology, Data Analysis Software, Dimensionality Reduction, Distributed Computing Architecture, Full-Stack Web Development, Information Technology, Natural Language Processing, Statistical Analysis, Web Development

      4.5

      (9.4k reviews)

      Intermediate · Specialization · 3-6 Months

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      DeepLearning.AI

      Introduction to Machine Learning in Production

      Skills you'll gain: Machine Learning, Applied Machine Learning, Computer Vision, Change Management, Data Management, Estimation, General Statistics, Leadership and Management, Machine Learning Algorithms, Probability & Statistics, Strategy and Operations

      4.8

      (2.3k reviews)

      Advanced · Course · 1-4 Weeks

    1234…84

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

    • Mathematics for Machine Learning: Imperial College London
    • Preparing for Google Cloud Certification: Cloud Data Engineer: Google Cloud
    • Unsupervised Learning, Recommenders, Reinforcement Learning: DeepLearning.AI
    • Applied Data Science with Python: University of Michigan
    • Data Engineering, Big Data, and Machine Learning on GCP: Google Cloud
    • AI Product Management: Duke University
    • NoSQL, Big Data, and Spark Foundations: IBM Skills Network
    • Applied Data Science with R: IBM Skills Network
    • AI for Medicine: DeepLearning.AI
    • Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization: DeepLearning.AI

    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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