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    Results for "applied statistics"

    • SAS

      Regression Modeling Fundamentals

      Skills you'll gain: Predictive Modeling, Predictive Analytics, SAS (Software), Statistical Modeling, Regression Analysis, Statistical Analysis, Data Analysis, Statistical Methods, Probability & Statistics, Statistical Hypothesis Testing

      4.7
      Rating, 4.7 out of 5 stars
      ·
      53 reviews

      Intermediate · Course · 1 - 3 Months

    • Coursera Project Network

      Calculating Descriptive Statistics in R

      Skills you'll gain: Descriptive Statistics, Data Analysis, Box Plots, R Programming, Statistical Analysis, Histogram, Probability & Statistics, Data Manipulation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      52 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Johns Hopkins University

      Applied Calculus with Python

      Skills you'll gain: Calculus, Integral Calculus, Applied Mathematics, Graphing, Numerical Analysis, Python Programming, Advanced Mathematics, Mathematical Software, Mathematical Modeling, Derivatives, Programming Principles

      4.9
      Rating, 4.9 out of 5 stars
      ·
      41 reviews

      Intermediate · Course · 1 - 3 Months

    • Northeastern University

      Machine Learning in Healthcare: Fundamentals & Applications

      Skills you'll gain: Data Mining, Health Informatics, Big Data, Applied Machine Learning, Predictive Modeling, Machine Learning, Artificial Intelligence, Machine Learning Algorithms, Random Forest Algorithm, Deep Learning, Health Care, Unsupervised Learning, Healthcare Industry Knowledge, Healthcare Ethics, Algorithms, Data Cleansing, Artificial Neural Networks

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8 reviews

      Beginner · Course · 1 - 4 Weeks

    • Codio

      Getting Started with Generative AI APIs

      Skills you'll gain: OpenAI, Large Language Modeling, Generative AI, ChatGPT, Prompt Engineering, Applied Machine Learning, Application Programming Interface (API), Natural Language Processing, Text Mining, Artificial Intelligence and Machine Learning (AI/ML), Performance Tuning

      3.7
      Rating, 3.7 out of 5 stars
      ·
      23 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New

      Microsoft

      Microsoft Azure for AI and Machine Learning

      Skills you'll gain: MLOps (Machine Learning Operations), Microsoft Azure, Artificial Intelligence and Machine Learning (AI/ML), Application Deployment, Data Pipelines, Network Troubleshooting, Cloud Computing, Software Versioning, CI/CD, Continuous Monitoring, Data Storage, Scalability, Data Quality, Data Transformation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      9 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free

      École Polytechnique Fédérale de Lausanne

      Systèmes d’Information Géographique - Partie 2

      Skills you'll gain: GIS Software, Spatial Analysis, Geographic Information Systems, Geospatial Mapping, Spatial Data Analysis, Augmented Reality, Data Mapping, Color Theory, Data Processing, Database Management

      4.3
      Rating, 4.3 out of 5 stars
      ·
      43 reviews

      Beginner · Course · 1 - 3 Months

    • LearnQuest

      Spring Data Repositories

      Skills you'll gain: Spring Framework, Hibernate (Java), Object-Relational Mapping, Transaction Processing, Databases, Java, Restful API, Data Access, Web Services, Middleware

      3.9
      Rating, 3.9 out of 5 stars
      ·
      47 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free

      Coursera Project Network

      Creating Multi Task Models With Keras

      Skills you'll gain: Keras (Neural Network Library), Tensorflow, Applied Machine Learning, Deep Learning, Artificial Neural Networks

      4.7
      Rating, 4.7 out of 5 stars
      ·
      77 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New

      Microsoft

      Building Intelligent Troubleshooting Agents

      Skills you'll gain: Large Language Modeling, Natural Language Processing, Generative AI Agents, Prompt Engineering, Test Case, Agentic systems, Human Computer Interaction, Artificial Intelligence and Machine Learning (AI/ML), Debugging, Artificial Intelligence, Performance Tuning, Python Programming, Machine Learning

      Intermediate · Course · 1 - 3 Months

    • Universidad Nacional de Colombia

      Más que química

      Skills you'll gain: Chemistry, Systems Of Measurement, Chemical Engineering, Biochemistry, Physical Science, Applied Mathematics, General Science and Research, Scientific Methods, Engineering

      4.9
      Rating, 4.9 out of 5 stars
      ·
      74 reviews

      Beginner · Course · 1 - 3 Months

    • LearnQuest

      Introduction to Data Science and scikit-learn in Python

      Skills you'll gain: Feature Engineering, Exploratory Data Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data Manipulation, NumPy, Data Analysis, Data Processing, Python Programming, Regression Analysis, Predictive Modeling, Machine Learning, Classification And Regression Tree (CART), Statistical Methods, Computer Programming, Data Structures

      3.7
      Rating, 3.7 out of 5 stars
      ·
      49 reviews

      Beginner · Course · 1 - 4 Weeks

    Searches related to applied statistics

    applied statistics for data analytics
    statistics and applied data analysis
    1…104105106…197

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

    • Regression Modeling Fundamentals: SAS
    • Calculating Descriptive Statistics in R: Coursera Project Network
    • Applied Calculus with Python: Johns Hopkins University
    • Machine Learning in Healthcare: Fundamentals & Applications: Northeastern University
    • Getting Started with Generative AI APIs: Codio
    • Microsoft Azure for AI and Machine Learning: Microsoft
    • Systèmes d’Information Géographique - Partie 2: Ã‰cole Polytechnique Fédérale de Lausanne
    • Spring Data Repositories: LearnQuest
    • Creating Multi Task Models With Keras: Coursera Project Network
    • Building Intelligent Troubleshooting Agents: Microsoft

    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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