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    • Applied Statistics

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    1086 results for "applied statistics"

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      Indian Statistical Institute

      Postgraduate Diploma in Applied Statistics

      Skills you'll gain: Basic Descriptive Statistics, Bayesian Statistics, Correlation And Dependence, Data Analysis, Forecasting, General Statistics, Probability & Statistics, Regression

      Postgraduate Diploma · 6-12 Months

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      IBM Skills Network

      IBM Data Science

      Skills you'll gain: Python Programming, Data Science, Data Analysis, Data Structures, Statistical Programming, Machine Learning, Data Mining, Regression, Machine Learning Algorithms, Data Visualization, General Statistics, Basic Descriptive Statistics, SQL, Applied Machine Learning, Statistical Analysis, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Databases, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Data Management, Database Theory, Data Visualization Software, R Programming, Statistical Machine Learning, Statistical Tests, Deep Learning, Probability & Statistics, Extract, Transform, Load, Plot (Graphics), Devops Tools, SPSS, Estimation, Interactive Data Visualization, Algorithms, Database Application, Geovisualization, Reinforcement Learning, Theoretical Computer Science, Big Data, Business Analysis, Computational Logic, Correlation And Dependence, Database Administration, Econometrics, Entrepreneurship, Marketing, Mathematical Theory & Analysis, Mathematics, Spreadsheet Software, Storytelling, Supply Chain Systems, Supply Chain and Logistics, Writing

      4.6

      (108.5k reviews)

      Beginner · Professional Certificate · 3-6 Months

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      IBM Skills Network

      IBM Data Analyst

      Skills you'll gain: Data Analysis, Python Programming, Data Visualization, Exploratory Data Analysis, Basic Descriptive Statistics, Data Structures, Statistical Programming, Data Science, Statistical Visualization, Plot (Graphics), Data Management, General Statistics, SQL, Databases, Business Analysis, Microsoft Excel, Spreadsheet Software, Data Mining, Statistical Analysis, Programming Principles, Probability & Statistics, Regression, Machine Learning, Algebra, Computer Programming, Data Analysis Software, Database Theory, Probability Distribution, Applied Machine Learning, Statistical Tests, Big Data, Correlation And Dependence, Data Visualization Software, Estimation, NoSQL, Geovisualization, ArcGIS, Cloud Computing, Data Warehousing, Database Application, Extract, Transform, Load, HTML and CSS, Knitr, Mathematics, Minitab, PostgreSQL, R Programming, SAS (Software), SPSS, Apache, Computational Logic, Computer Programming Tools, Database Administration, Econometrics, Interactive Data Visualization, Leadership and Management, Mathematical Theory & Analysis, Operating Systems, Professional Development, Statistical Machine Learning, System Programming, Theoretical Computer Science

      4.6

      (62.6k reviews)

      Beginner · Professional Certificate · 3-6 Months

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      DeepLearning.AI, Stanford University

      Machine Learning

      Skills you'll gain: Machine Learning, Probability & Statistics, Machine Learning Algorithms, General Statistics, Theoretical Computer Science, Applied Machine Learning, Algorithms, Artificial Neural Networks, Regression, Econometrics, Computer Programming, Deep Learning, Python Programming, Statistical Programming, Mathematics, Tensorflow, Data Management, Data Structures, Statistical Machine Learning, Reinforcement Learning, Probability Distribution, Mathematical Theory & Analysis, Data Analysis, Data Mining, Linear Algebra, Computer Vision, Calculus, Feature Engineering, Bayesian Statistics, Operations Research, Research and Design, Strategy and Operations, Computational Logic, Accounting, Communication

      4.9

      (9.6k reviews)

      Beginner · Specialization · 1-3 Months

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      IBM Skills Network

      Introduction to Data Science

      Skills you'll gain: Data Science, Data Structures, SQL, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Statistical Programming, Databases, Python Programming, Database Theory, Data Visualization Software, R Programming, Data Management, Data Mining, Regression, Devops Tools, Machine Learning Algorithms, SPSS, Basic Descriptive Statistics, Data Analysis, Database Application, Big Data, Computer Programming, Database Administration, Deep Learning, General Statistics, Machine Learning, Marketing, Probability & Statistics, Storytelling, Writing

      4.6

      (78.6k reviews)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      Key Technologies for Business

      Skills you'll gain: Data Science, Cloud Computing, Applied Machine Learning, Cloud Engineering, Cloud Infrastructure, Data Mining, Regression, Cloud Applications, Cloud Management, Cloud Platforms, Cloud Storage, DevOps, IBM Cloud, Network Security, Software As A Service, Software Engineering, Basic Descriptive Statistics, Data Analysis, Big Data, BlockChain, Computer Architecture, Computer Graphics, Computer Programming, Computer Vision, Deep Learning, Finance, General Statistics, Human Computer Interaction, Interactive Design, Machine Learning, Machine Learning Algorithms, Operating Systems, Probability & Statistics, Security Engineering, Software Architecture, Software Framework, Storytelling, System Programming, Theoretical Computer Science, Writing

      4.7

      (74.9k reviews)

      Beginner · Specialization · 1-3 Months

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      IBM Skills Network

      IBM Data Engineering

      Skills you'll gain: Data Management, Databases, Data Architecture, Data Structures, Big Data, Database Theory, SQL, Apache, Database Administration, Extract, Transform, Load, Python Programming, Data Model, Database Application, Data Warehousing, Data Analysis, NoSQL, Data Engineering, Distributed Computing Architecture, Database Design, Operating Systems, System Programming, System Software, Programming Principles, Statistical Programming, Algebra, Computer Architecture, PostgreSQL, 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, Data Visualization, Data Visualization Software, Basic Descriptive Statistics, Exploratory Data Analysis, Cloud Applications, Cloud Computing, Data Science, DevOps, Kubernetes, Leadership and Management, Network Architecture, Network Security, Other Programming Languages, Professional Development, Security Engineering, Algorithms, Computational Logic, Computational Thinking, Computer Networking, Computer Programming, Computer Programming Tools, IBM Cloud, Linux, Mathematical Theory & Analysis, Mathematics, Microarchitecture, Project Management, Security Strategy, Software Architecture, Software Engineering, Strategy and Operations, Theoretical Computer Science

      4.6

      (41.5k reviews)

      Beginner · Professional Certificate · 3-6 Months

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      IBM Skills Network

      IBM Data Analytics with Excel and R

      Skills you'll gain: Data Analysis, R Programming, Data Visualization, Plot (Graphics), Data Management, SQL, Exploratory Data Analysis, Data Mining, Databases, Basic Descriptive Statistics, General Statistics, Data Visualization Software, Statistical Programming, Data Analysis Software, Interactive Data Visualization, Statistical Visualization, Statistical Analysis, Big Data, Business Analysis, Microsoft Excel, Probability & Statistics, Spreadsheet Software, Database Theory, Regression, Statistical Tests, Data Science, Data Structures, Software Visualization, Machine Learning, User Experience, Probability Distribution, NoSQL, Python Programming, Applied Machine Learning, Deep Learning, Estimation, Geovisualization, Linear Algebra, Machine Learning Algorithms, Machine Learning Software, SAS (Software), Spatial Data Analysis, Statistical Machine Learning, Cloud Computing, Data Architecture, Data Model, Data Warehousing, Database Administration, Database Application, Database Design, Mathematics, Visualization (Computer Graphics), Accounting, Advertising, Apache, Communication, Computational Logic, Computer Programming, Extract, Transform, Load, Leadership and Management, Marketing, Operating Systems, Professional Development, Programming Principles, System Programming, Theoretical Computer Science

      4.7

      (15.7k reviews)

      Beginner · Professional Certificate · 3-6 Months

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      IBM Skills Network

      IBM & Darden Digital Strategy

      Skills you'll gain: Entrepreneurship, Marketing, Strategy and Operations, Business Analysis, Business Transformation, Leadership and Management, Sales, Strategy, Data Science, Cloud Computing, Data Analysis, Data Management, Research and Design, Applied Machine Learning, Business Design, Big Data, Market Analysis, Cloud Engineering, Cloud Infrastructure, Databases, NoSQL, Python Programming, SQL, Statistical Programming, Innovation, Basic Descriptive Statistics, Cloud Applications, Cloud Management, Cloud Platforms, Cloud Storage, Data Mining, Data Structures, Data Visualization, Data Warehousing, DevOps, General Statistics, IBM Cloud, Machine Learning, Mathematics, Network Security, Probability & Statistics, Software As A Service, Software Engineering, Apache, BlockChain, Business Psychology, Change Management, Computer Architecture, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Data Visualization Software, Deep Learning, Design and Product, Digital Marketing, Extract, Transform, Load, Finance, Human Computer Interaction, Interactive Design, Internet Of Things, Machine Learning Algorithms, Microsoft Excel, Operating Systems, Organizational Development, Product Strategy, Professional Development, Security Engineering, Software Architecture, Software Framework, System Programming, Theoretical Computer Science

      4.7

      (24.5k reviews)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      IBM AI Foundations for Business

      Skills you'll gain: Data Science, Applied Machine Learning, Data Management, Leadership and Management, Data Mining, Machine Learning, Regression, Artificial Neural Networks, Basic Descriptive Statistics, Cloud Computing, Cloud Platforms, Data Analysis, Data Architecture, Database Administration, Databases, Deep Learning, Big Data, Computer Vision, General Statistics, Innovation, Machine Learning Algorithms, Probability & Statistics, Storytelling, Writing

      4.7

      (71.9k reviews)

      Beginner · Specialization · 1-3 Months

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      IBM Skills Network

      Applied Data Science

      Skills you'll gain: Python Programming, Data Analysis, Data Science, Statistical Programming, Data Structures, Data Visualization, Basic Descriptive Statistics, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Machine Learning, Applied Machine Learning, Data Mining, General Statistics, Regression, Statistical Analysis, Statistical Tests, Data Management, Extract, Transform, Load, Plot (Graphics), Interactive Data Visualization, Machine Learning Algorithms, SQL, Geovisualization, Algorithms, Business Analysis, Computational Logic, Computer Programming Tools, Correlation And Dependence, Data Analysis Software, Databases, Econometrics, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Spreadsheet Software, Statistical Machine Learning, Theoretical Computer Science

      4.6

      (44.1k reviews)

      Beginner · Specialization · 3-6 Months

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      DeepLearning.AI

      Deep Learning

      Skills you'll gain: Deep Learning, Machine Learning, Artificial Neural Networks, Python Programming, Statistical Programming, Machine Learning Algorithms, Linear Algebra, Applied Machine Learning, Statistical Machine Learning, Dimensionality Reduction, Feature Engineering, Probability & Statistics, Business Psychology, Entrepreneurship, Machine Learning Software, Computer Vision, Marketing, General Statistics, Natural Language Processing, Computer Programming, Leadership and Management, Project Management, Regression, Sales, Strategy, Strategy and Operations, Tensorflow, Differential Equations, Mathematics, Applied Mathematics, Decision Making, Supply Chain Systems, Supply Chain and Logistics, Advertising, Communication, Estimation, Forecasting, Mathematical Theory & Analysis, Statistical Visualization, Algorithms, Theoretical Computer Science, Bayesian Statistics, Calculus, Probability Distribution, Statistical Tests, Big Data, Computer Architecture, Computer Networking, Data Management, Human Computer Interaction, Network Architecture, User Experience, Algebra, Computational Logic, Computer Graphic Techniques, Computer Graphics, Data Structures, Data Visualization, Hardware Design, Interactive Design, Markov Model, Network Model

      4.8

      (138.4k reviews)

      Intermediate · Specialization · 3-6 Months

    1234…84

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


      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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