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    • Applied Statistics

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    1046 results for "applied statistics"

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      University of Colorado Boulder

      Power Electronics

      Skills you'll gain: Research and Design, Mathematics, Operations Research, Strategy and Operations, Algebra, Applied Mathematics, Business Analysis, Computer Architecture, Computer Networking, Computer Programming, Data Visualization, Entrepreneurship, Hardware Design, Leadership and Management, Linear Algebra, Network Model, Operational Analysis, Other Programming Languages, Problem Solving, Statistical Visualization, Supply Chain and Logistics, Advertising, Business Psychology, Human Computer Interaction, Human Learning, Marketing, Mathematical Theory & Analysis, Project Management, Supply Chain Systems, User Experience

      4.7

      (3.8k reviews)

      Intermediate · Specialization · 3-6 Months

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      IBM

      IBM Mainframe Developer

      Skills you'll gain: Computer Programming, Other Programming Languages, Software Engineering, Theoretical Computer Science, Computer Architecture, Data Management, Agile Software Development, Cryptography, DevOps, Operating Systems, Software Architecture, Software Testing, Computer Programming Tools, Databases, SQL, Scrum (Software Development), Statistical Programming, Communication, Computational Thinking, Distributed Computing Architecture, Hardware Design, Journalism, Programming Principles, Project Management, Security Engineering, Strategy and Operations

      4.7

      (823 reviews)

      Beginner · Professional Certificate · 3-6 Months

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      Google Cloud

      Digital Transformation Using AI/ML with Google Cloud

      Skills you'll gain: Cloud Computing, Machine Learning, Applied Machine Learning, Business Analysis, Business Transformation, Cloud Standards, Computer Science, Entrepreneurship, Google Cloud Platform, Innovation, Research and Design, Small Data, Data Science, Information Technology, Machine Learning Software, Statistical Programming, Cloud API, Cloud Infrastructure, Cloud Platforms, Google App Engine, Python Programming, Business Psychology, Computer Security Incident Management, Culture, Leadership and Management, Security Engineering, Security Strategy, Computer Vision, Machine Learning Algorithms, Natural Language Processing, SQL, Algorithms, Business Intelligence, Cloud Storage, Data Warehousing, Human Resources, Leadership Development, Strategy, Strategy and Operations, Theoretical Computer Science

      4.7

      (6.1k reviews)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      Build Basic Generative Adversarial Networks (GANs)

      Skills you'll gain: Artificial Neural Networks, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Python Programming, Statistical Programming, Deep Learning, Applied Machine Learning, Computer Programming, Computer Vision

      4.7

      (1.7k reviews)

      Intermediate · Course · 1-4 Weeks

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      Imperial College London

      Mathematics for Machine Learning: Linear Algebra

      Skills you'll gain: Algebra, Applied Mathematics, Linear Algebra, Machine Learning, Python Programming, Computer Programming, Machine Learning Algorithms, Mathematics, Statistical Programming

      4.7

      (11.4k reviews)

      Beginner · Course · 1-3 Months

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      University of Pennsylvania

      Computational Thinking for Problem Solving

      Skills you'll gain: Computer Programming, Python Programming, Theoretical Computer Science, Algorithms, Computational Thinking, Computer Architecture, Microarchitecture, Applied Machine Learning, Machine Learning, Inventory Management, Programming Principles, Statistical Programming, Supply Chain and Logistics

      4.7

      (1.2k reviews)

      Beginner · Course · 1-4 Weeks

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      University of London

      Virtual Reality

      Skills you'll gain: Computer Graphics, Virtual Reality, Human Computer Interaction, Design and Product, Product Design, Computer Graphic Techniques, Computer Science, Product Development, Applied Machine Learning, Graphic Design, Interactive Design, Machine Learning, Technical Product Management, User Experience

      4.7

      (2k reviews)

      Beginner · Specialization · 3-6 Months

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      University of Colorado System

      Agile Leadership

      Skills you'll gain: Leadership and Management, Business Psychology, Entrepreneurship, Communication, Strategy and Operations, Adaptability, Collaboration, Emotional Intelligence, Human Resources, Leadership Development, Resilience, Organizational Development, Change Management, Project Management, Business Communication, Conflict Management, Culture, Advertising, Agile Software Development, Feature Engineering, Machine Learning, Marketing, Scrum (Software Development), Software Engineering, Software Engineering Tools

      4.7

      (1.1k reviews)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      Natural Language Processing in TensorFlow

      Skills you'll gain: Machine Learning, Natural Language Processing, Deep Learning, Tensorflow, Artificial Neural Networks, Data Science, Machine Learning Algorithms, Statistical Machine Learning, Applied Machine Learning, Computer Programming, Python Programming, Statistical Programming

      4.6

      (6.1k reviews)

      Intermediate · Course · 1-4 Weeks

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      University of London

      Machine Learning for All

      Skills you'll gain: Machine Learning, Applied Machine Learning, Artificial Neural Networks, Feature Engineering, Algorithms, Machine Learning Algorithms, Theoretical Computer Science

      4.7

      (3.1k reviews)

      Beginner · Course · 1-4 Weeks

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      DeepLearning.AI

      Machine Learning Modeling Pipelines in Production

      Skills you'll gain: Applied Machine Learning, Cloud Computing, Computer Programming, Deep Learning, Machine Learning, Python Programming, Statistical Programming, Tensorflow

      4.4

      (301 reviews)

      Advanced · Course · 1-3 Months

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      Parsons School of Design, The New School

      Creativity and AI

      Skills you'll gain: Entrepreneurship, Research and Design, Creativity, Machine Learning, Innovation, Artificial Neural Networks, Market Research

      4.5

      (38 reviews)

      Intermediate · Specialization · 3-6 Months

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

    • Power Electronics: University of Colorado Boulder
    • IBM Mainframe Developer: IBM
    • Digital Transformation Using AI/ML with Google Cloud: Google Cloud
    • Build Basic Generative Adversarial Networks (GANs): DeepLearning.AI
    • Mathematics for Machine Learning: Linear Algebra: Imperial College London
    • Computational Thinking for Problem Solving: University of Pennsylvania
    • Virtual Reality: University of London
    • Agile Leadership: University of Colorado System
    • Natural Language Processing in TensorFlow: DeepLearning.AI
    • Machine Learning for All: University of London

    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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