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    Results for "applied statistics"

    • IBM

      BI Foundations with SQL, ETL and Data Warehousing

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

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

      Beginner · Specialization · 3 - 6 Months

    • Multiple educators

      Data Science Foundations

      Skills you'll gain: Dashboard, Pseudocode, Jupyter, Algorithms, Data Mining, Pandas (Python Package), Correlation Analysis, Web Scraping, NumPy, Probability & Statistics, Predictive Modeling, Big Data, Automation, Data Visualization Software, Data Collection, Data Science, GitHub, Python Programming, Machine Learning Algorithms, Unsupervised Learning

      4.6
      Rating, 4.6 out of 5 stars
      ·
      112K reviews

      Beginner · Specialization · 3 - 6 Months

    • IBM

      IBM IT Scrum Master

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

      4.8
      Rating, 4.8 out of 5 stars
      ·
      9.1K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • 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, Statistical Visualization, Relational Databases, Data Analysis, Ggplot2, Database Design, Data Presentation, SQL, Plot (Graphics), Leaflet (Software), Database Management, Data Manipulation, R Programming, Web Scraping

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

      Beginner · Specialization · 3 - 6 Months

    • DeepLearning.AI

      Probability & Statistics for Machine Learning & Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Data Science, Exploratory Data Analysis, Data Analysis, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      554 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Rice University

      Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions

      Skills you'll gain: Descriptive Statistics, Probability & Statistics, Probability Distribution, Business Analytics, Microsoft Excel, Data Analysis, Statistical Analysis, Box Plots, Sampling (Statistics), Correlation Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.6K reviews

      Mixed · Course · 1 - 4 Weeks

    • Duke University

      Introduction to Probability and Data with R

      Skills you'll gain: Sampling (Statistics), Exploratory Data Analysis, Statistical Inference, Probability Distribution, Bayesian Statistics, R Programming, Data Analysis, Probability, Statistics, Statistical Analysis, Statistical Software, Data Science, Descriptive Statistics

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.7K reviews

      Beginner · Course · 1 - 3 Months

    • IBM

      IBM Data Analytics with Excel and R

      Skills you'll gain: Data Storytelling, Data Presentation, Interactive Data Visualization, Data Visualization Software, Shiny (R Package), Data Wrangling, Exploratory Data Analysis, Statistical Visualization, Relational Databases, Big Data, Ggplot2, Database Design, Data Analysis, IBM Cognos Analytics, Data Mining, Dashboard, Excel Formulas, Data Manipulation, Web Scraping, Microsoft Excel

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      28K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • University of California, Davis

      Learn SQL Basics for Data Science

      Skills you'll gain: Data Governance, Presentations, Data Cleansing, Feature Engineering, SQL, Apache Spark, A/B Testing, Distributed Computing, Descriptive Statistics, Data Lakes, Data Quality, Data Storytelling, Data Analysis, Peer Review, Exploratory Data Analysis, Data Pipelines, Databricks, JSON, Query Languages, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      18K reviews

      Beginner · Specialization · 3 - 6 Months

    • University of Michigan

      Fitting Statistical Models to Data with Python

      Skills you'll gain: Statistical Modeling, Statistical Methods, Bayesian Statistics, Statistical Inference, Statistical Software, Statistical Analysis, Statistical Programming, Regression Analysis, Predictive Modeling, Data Analysis, Exploratory Data Analysis, Data Manipulation

      4.4
      Rating, 4.4 out of 5 stars
      ·
      699 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free

      Yale University

      Understanding Medical Research: Your Facebook Friend is Wrong

      Skills you'll gain: Probability & Statistics, Research Design, Statistical Methods, Descriptive Statistics, Research Methodologies, Statistical Analysis, Scientific Methods, Clinical Research, Data Analysis, Statistical Hypothesis Testing, Data Validation

      4.9
      Rating, 4.9 out of 5 stars
      ·
      2.1K reviews

      Beginner · Course · 1 - 3 Months

    • Johns Hopkins University

      Mathematical Biostatistics Boot Camp 1

      Skills you'll gain: Sampling (Statistics), Probability & Statistics, Statistical Inference, Statistical Methods, Probability, Probability Distribution, Data Analysis, Statistical Analysis, Biostatistics

      4.4
      Rating, 4.4 out of 5 stars
      ·
      514 reviews

      Mixed · Course · 1 - 4 Weeks

    Searches related to applied statistics

    applied statistics for data analytics
    applied statistics and probability
    statistics and applied data analysis
    1…8910…206

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

    • BI Foundations with SQL, ETL and Data Warehousing: IBM
    • Data Science Foundations: University of London
    • IBM IT Scrum Master: IBM
    • Applied Data Science with R: IBM
    • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
    • Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions: Rice University
    • Introduction to Probability and Data with R: Duke University
    • IBM Data Analytics with Excel and R: IBM
    • Learn SQL Basics for Data Science: University of California, Davis
    • Fitting Statistical Models to Data with Python: University of Michigan

    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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