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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

    • U

      University of California, Santa Cruz

      Bayesian Statistics

      Skills you'll gain: Time Series Analysis and Forecasting, Bayesian Statistics, R Programming, Forecasting, Statistical Inference, Statistical Modeling, Technical Communication, Data Analysis, Probability, Statistical Machine Learning, Statistical Methods, Statistical Analysis, Advanced Analytics, Mathematical Modeling, Microsoft Excel, Markov Model, Probability Distribution, Probability & Statistics, Unsupervised Learning, Regression Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • G

      Google

      Google Advanced Data Analytics

      Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Statistical Hypothesis Testing, Data Ethics, Data Visualization Software, Sampling (Statistics), Data Presentation, Regression Analysis, Feature Engineering, Data Transformation, Descriptive Statistics, Professional Networking, Data Visualization, Tableau Software, Data Manipulation, Statistical Analysis, Advanced Analytics, Object Oriented Programming (OOP), Data Analysis, Interviewing Skills

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.1K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Mathematics for Machine Learning and Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Linear Algebra, Statistical Inference, Applied Mathematics, NumPy, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Python Programming, Jupyter, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.7K reviews

      Intermediate · Specialization · 1 - 3 Months

    • I

      IBM

      IBM Generative AI Engineering

      Skills you'll gain: Prompt Engineering, Data Wrangling, Large Language Modeling, Unit Testing, Supervised Learning, Feature Engineering, Generative AI, Keras (Neural Network Library), Deep Learning, Artificial Intelligence, ChatGPT, Natural Language Processing, Data Cleansing, Jupyter, Data Analysis, Unsupervised Learning, Data Manipulation, PyTorch (Machine Learning Library), Data Import/Export, Exploratory Data Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      86K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IIMA - IIM Ahmedabad

      Pre-MBA Statistics

      Skills you'll gain: Sampling (Statistics), Probability, Probability & Statistics, Statistical Hypothesis Testing, Statistics, Data Literacy, Statistical Inference, Estimation, Statistical Analysis, Descriptive Statistics, Data Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      280 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Michigan

      Applied Machine Learning in Python

      Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Dimensionality Reduction, Random Forest Algorithm

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Science

      Skills you'll gain: Dashboard, Data Visualization Software, Data Wrangling, Data Visualization, SQL, Supervised Learning, Feature Engineering, Plotly, Interactive Data Visualization, Jupyter, Data Literacy, Exploratory Data Analysis, Data Mining, Data Cleansing, Matplotlib, Data Analysis, Unsupervised Learning, Generative AI, Pandas (Python Package), Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      143K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Analyst

      Skills you'll gain: Data Storytelling, Dashboard, Data Visualization Software, Plotly, Data Wrangling, Data Visualization, Generative AI, SQL, Interactive Data Visualization, Exploratory Data Analysis, Data Cleansing, Big Data, Jupyter, Matplotlib, Data Analysis, Statistical Analysis, Pandas (Python Package), Data Manipulation, Excel Formulas, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      92K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      Duke University

      Data Analysis with R

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Exploratory Data Analysis, Regression Analysis, Statistical Reporting, Probability Distribution, Statistical Methods, Data Analysis Software, R Programming, Bayesian Statistics, Statistical Analysis, Data Analysis, Statistical Software, Statistical Modeling, Probability & Statistics, Probability, Statistics, Correlation Analysis, Data Literacy

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: New
      New
      D

      DeepLearning.AI

      Applied Statistics for Data Analytics

      Skills you'll gain: Probability & Statistics, Statistical Analysis, Statistics, Statistical Modeling, Statistical Hypothesis Testing, Statistical Visualization, Descriptive Statistics, Data Analysis, Histogram, Probability, Probability Distribution, Correlation Analysis, Statistical Inference, Estimation, Simulation and Simulation Software, Sampling (Statistics), Analytical Skills, Spreadsheet Software, Generative AI

      4.9
      Rating, 4.9 out of 5 stars
      ·
      20 reviews

      Beginner · Course · 1 - 4 Weeks

    • D
      S

      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), Artificial Intelligence, NumPy, Predictive Modeling, Deep Learning, Reinforcement Learning, Random Forest Algorithm, Feature Engineering

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      34K reviews

      Beginner · Specialization · 1 - 3 Months

    • I

      IBM

      Applied Data Science with R

      Skills you'll gain: Data Storytelling, Interactive Data Visualization, Data Visualization Software, Shiny (R Package), Data Wrangling, Dashboard, Exploratory Data Analysis, Relational Databases, Data Analysis, Ggplot2, Database Design, Data Presentation, SQL, Plot (Graphics), Leaflet (Software), Data Transformation, Database Management, Data Manipulation, Web Scraping, R Programming

      4.5
      Rating, 4.5 out of 5 stars
      ·
      1.2K reviews

      Beginner · Specialization · 3 - 6 Months

    Applied Statistics learners also search

    R Statistics
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    Statistics Projects
    Advanced Statistics
    Statistics
    Basic Statistics
    Statistics for Data Science
    Statistics With R
    1234…198

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

    • Bayesian Statistics: University of California, Santa Cruz
    • Google Advanced Data Analytics: Google
    • Mathematics for Machine Learning and Data Science: DeepLearning.AI
    • IBM Generative AI Engineering: IBM
    • Pre-MBA Statistics: IIMA - IIM Ahmedabad
    • Applied Machine Learning in Python: University of Michigan
    • IBM Data Science: IBM
    • IBM Data Analyst: IBM
    • Data Analysis with R: Duke University
    • Applied Statistics for Data Analytics: 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.‎

    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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