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    • Applied Statistics

    Applied Statistics Courses Online

    Understand applied statistics for data analysis and interpretation. Learn statistical methods and tools for various industries.

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    Explore the Applied Statistics Course Catalog

    • I

      IBM

      AI Foundations for Everyone

      Skills you'll gain: Prompt Engineering, Large Language Modeling, Generative AI, Artificial Intelligence, ChatGPT, Data Ethics, Artificial Intelligence and Machine Learning (AI/ML), OpenAI, IBM Cloud, Private Cloud, Data Loss Prevention, Deep Learning, Machine Learning, Artificial Neural Networks, WordPress, Governance, Automation, Generative AI Agents, Business Transformation, Applied Machine Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      27K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      Deep Learning

      Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Tensorflow, Supervised Learning, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, Debugging, Performance Tuning, Keras (Neural Network Library), Python Programming, Machine Learning Algorithms, Analysis, Data Processing

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      146K reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      Skills you'll gain: Tensorflow, Computer Vision, Image Analysis, Keras (Neural Network Library), Natural Language Processing, Time Series Analysis and Forecasting, Deep Learning, Artificial Neural Networks, Generative AI, Applied Machine Learning, Predictive Modeling, Artificial Intelligence and Machine Learning (AI/ML), Text Mining, Forecasting, Data Processing, Supervised Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      25K reviews

      Intermediate · Professional Certificate · 3 - 6 Months

    • Status: Free
      Free
      T

      The Hong Kong University of Science and Technology

      Python and Statistics for Financial Analysis

      Skills you'll gain: Statistical Inference, Statistical Methods, Pandas (Python Package), Probability & Statistics, Risk Analysis, Financial Trading, Financial Data, Data Manipulation, Statistical Analysis, Regression Analysis, Financial Analysis, Jupyter, Financial Modeling

      4.4
      Rating, 4.4 out of 5 stars
      ·
      4.5K reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      BI Foundations with SQL, ETL and Data Warehousing

      Skills you'll gain: Data Warehousing, Extract, Transform, Load, Apache Airflow, Linux Commands, SQL, IBM Cognos Analytics, Data Pipelines, Apache Kafka, Bash (Scripting Language), Shell Script, Dashboard, File Management, Star Schema, IBM DB2, Business Intelligence, Interactive Data Visualization, Linux, Relational Databases, Stored Procedure, Databases

      4.6
      Rating, 4.6 out of 5 stars
      ·
      24K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Minnesota

      Human Resource Management: HR for People Managers

      Skills you'll gain: Performance Management, Performance Appraisal, Employee Performance Management, Compensation Management, Compensation Strategy, Compensation and Benefits, Constructive Feedback, Workforce Planning, Human Resource Strategy, Human Resources, Employee Onboarding, Recruitment, Recruitment Strategies, Human Capital, Compensation Analysis, Talent Acquisition, Human Resources Management and Planning, People Management, Job Analysis, Payroll

      4.8
      Rating, 4.8 out of 5 stars
      ·
      15K reviews

      Beginner · Specialization · 3 - 6 Months

    • G

      Google

      Agile Project Management

      Skills you'll gain: Agile Project Management, Backlogs, Agile Software Development, Agile Methodology, User Story, Sprint Planning, Scaled Agile Framework, Product Roadmaps, Sprint Retrospectives, Employee Coaching, Kanban Principles, Team Management, Waterfall Methodology, Prioritization

      4.8
      Rating, 4.8 out of 5 stars
      ·
      14K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Cape Town

      Understanding Clinical Research: Behind the Statistics

      Skills you'll gain: Biostatistics, Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Clinical Research, Statistical Analysis, Quantitative Research, Descriptive Statistics, Statistical Inference, Data Collection, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.6K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Michigan

      Python 3 Programming

      Skills you'll gain: Unified Modeling Language, JSON, Object Oriented Programming (OOP), Software Design, Debugging, Object Oriented Design, Data Processing, Web Scraping, Unit Testing, Programming Principles, Data Import/Export, Restful API, Python Programming, Image Analysis, Data Manipulation, Jupyter, Maintainability, Data Structures, Software Engineering, File Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      23K reviews

      Beginner · Specialization · 3 - 6 Months

    • I

      IBM

      IBM IT Scrum Master

      Skills you'll gain: Software Development Life Cycle, Software Architecture, Agile Software Development, User Story, Sprint Retrospectives, Agile Methodology, DevOps, Software Design, Kanban Principles, Scrum (Software Development), Information Technology, Software Engineering, Computer Hardware, Agile Project Management, Sprint Planning, Cloud Computing, Backlogs, Cloud-Native Computing, Software Development Methodologies, Network Troubleshooting

      4.8
      Rating, 4.8 out of 5 stars
      ·
      9.3K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Generative AI for Data Scientists

      Skills you'll gain: Prompt Engineering, Generative AI, ChatGPT, Exploratory Data Analysis, Data Ethics, OpenAI, Feature Engineering, Predictive Modeling, Large Language Modeling, Artificial Intelligence, Data Storytelling, Program Development, Data Modeling, Data Presentation, Predictive Analytics, Data Synthesis, Data Analysis, Data Cleansing, Data Visualization Software, Image Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.7K reviews

      Intermediate · Specialization · 1 - 3 Months

    • U

      University of California, Davis

      Learn SQL Basics for Data Science

      Skills you'll gain: Data Governance, Presentations, SQL, Apache Spark, Distributed Computing, Data Quality, Descriptive Statistics, Data Lakes, A/B Testing, Data Storytelling, Data Analysis, Peer Review, Exploratory Data Analysis, Data Pipelines, Databricks, JSON, Statistical Analysis, Database Design, Query Languages, Complex Problem Solving

      4.6
      Rating, 4.6 out of 5 stars
      ·
      17K reviews

      Beginner · Specialization · 3 - 6 Months

    Applied Statistics learners also search

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    Statistics for Data Science
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    1…567…198

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

    • AI Foundations for Everyone: IBM
    • Deep Learning: DeepLearning.AI
    • DeepLearning.AI TensorFlow Developer: DeepLearning.AI
    • Python and Statistics for Financial Analysis: The Hong Kong University of Science and Technology
    • BI Foundations with SQL, ETL and Data Warehousing: IBM
    • Human Resource Management: HR for People Managers: University of Minnesota
    • Agile Project Management: Google
    • Understanding Clinical Research: Behind the Statistics: University of Cape Town
    • Python 3 Programming: University of Michigan
    • IBM IT Scrum Master: IBM

    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.‎

    Online Applied Statistics courses offer a convenient and flexible way to enhance your existing knowledge or learn new Applied Statistics skills. With a wide range of Applied Statistics classes, you can conveniently learn at your own pace to advance your Applied Statistics career skills.‎

    When looking to enhance your workforce's skills in Applied Statistics, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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