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    • Statistics For Data Science

    Statistics for Data Science Courses Online

    Master statistics for data science applications. Learn about statistical techniques, data analysis, and machine learning models.

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    Explore the Statistics for Data Science Course Catalog

    • I

      IBM

      IBM Data Analytics with Excel and R

      Skills you'll gain: Data Storytelling, Interactive Data Visualization, Shiny (R Package), Data Wrangling, Exploratory Data Analysis, Relational Databases, Big Data, Data Visualization Software, Ggplot2, Database Design, Data Analysis, IBM Cognos Analytics, Statistical Analysis, Data Presentation, 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

    • Status: Free
      Free
      C

      Coursera Instructor Network

      Data Science for Professionals

      Skills you'll gain: Predictive Modeling, Statistical Analysis, Data Manipulation, Data Analysis, Data Science, Pandas (Python Package), Data Visualization Software, Statistics, Data Cleansing, Data Processing, Data-Driven Decision-Making, Business Analytics, Exploratory Data Analysis, Machine Learning

      4.5
      Rating, 4.5 out of 5 stars
      ·
      14 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Amsterdam

      Methods and Statistics in Social Sciences

      Skills you'll gain: Qualitative Research, Statistical Hypothesis Testing, Scientific Methods, Descriptive Statistics, Statistical Inference, Statistical Analysis, Statistical Software, Probability Distribution, Research, Correlation Analysis, Research Design, Sampling (Statistics), Research Reports, Science and Research, Probability, Interviewing Skills, Data Analysis, Sample Size Determination, Statistical Methods, Social Sciences

      4.6
      Rating, 4.6 out of 5 stars
      ·
      7.6K reviews

      Beginner · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      R Programming

      Skills you'll gain: Statistical Analysis, R Programming, Statistical Programming, Data Analysis, Debugging, Simulations, Computer Programming Tools, Program Development, Data Structures, Performance Tuning, Data Import/Export

      4.5
      Rating, 4.5 out of 5 stars
      ·
      22K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      E

      Eindhoven University of Technology

      Process Mining: Data science in Action

      Skills you'll gain: Process Analysis, Process Improvement, Business Process Management, Data Mining, Business Process Modeling, Process Optimization, Data Processing, Performance Analysis, Big Data, Real Time Data, Data Science, Verification And Validation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.2K reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of California, Irvine

      Data Science Fundamentals

      Skills you'll gain: Data Ethics, Predictive Modeling, Unsupervised Learning, Natural Language Processing, Predictive Analytics, Data Analysis, Classification And Regression Tree (CART), Regression Analysis, Data Mining, Statistical Analysis, Data Science, Social Media, Text Mining, Anomaly Detection, Business Analytics, Decision Tree Learning, Statistical Modeling, Big Data, Analytics, Cloud Computing

      4.2
      Rating, 4.2 out of 5 stars
      ·
      238 reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: New
      New
      I

      IBM

      IBM Data Architecture

      Skills you'll gain: NoSQL, Data Warehousing, SQL, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Data Security, Linux Commands, Data Migration, Database Design, Data Governance, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Shell Script, Database Architecture and Administration, Data Store

      4.6
      Rating, 4.6 out of 5 stars
      ·
      6.5K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      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, Descriptive Statistics

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

      Beginner · Course · 1 - 3 Months

    • I

      IIMA - IIM Ahmedabad

      Pre-MBA Statistics

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

      4.6
      Rating, 4.6 out of 5 stars
      ·
      278 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      Skills you'll gain: Bayesian Statistics, Statistical Inference, Data Analysis, Probability, Statistical Modeling, Statistical Analysis, Microsoft Excel, Probability Distribution, R Programming, Regression Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      The Data Scientist’s Toolbox

      Skills you'll gain: Rmarkdown, Version Control, Data Science, Big Data, Git (Version Control System), GitHub, Data Analysis, R Programming, Statistical Programming, Development Environment, Integrated Development Environments, Software Installation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      34K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free
      Free
      T

      The University of Edinburgh

      Data Science in Stratified Healthcare and Precision Medicine

      Skills you'll gain: Precision Medicine, Bioinformatics, Image Analysis, Medical Imaging, Big Data, Health Informatics, Statistical Methods, Data Science, Machine Learning, Natural Language Processing, Probability & Statistics, Data Analysis, Molecular Biology, Network Analysis, Graph Theory, Healthcare Ethics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      334 reviews

      Intermediate · Course · 1 - 3 Months

    Statistics For Data Science learners also search

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    In summary, here are 10 of our most popular statistics for data science courses

    • IBM Data Analytics with Excel and R: IBM
    • Data Science for Professionals: Coursera Instructor Network
    • Methods and Statistics in Social Sciences: University of Amsterdam
    • R Programming: Johns Hopkins University
    • Process Mining: Data science in Action: Eindhoven University of Technology
    • Data Science Fundamentals: University of California, Irvine
    • IBM Data Architecture: IBM
    • Introduction to Probability and Data with R: Duke University
    • Pre-MBA Statistics: IIMA - IIM Ahmedabad
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz

    Frequently Asked Questions about Statistics For Data Science

    Statistics for data science refers to the mathematical analysis used to sort, analyze, interpret, and present data. It includes concepts like probability distribution, regression, and over or under-sampling. Descriptive statistics organizes data based on characteristics of the data set, such as normal distribution, central tendency, variability, and standard deviation. Inferential statistics incorporates the use of probability theory to infer characteristics of the data set.‎

    Learning statistics for data science can lead to career opportunities in data science and related fields. As organizations increasingly rely on data to make decisions, they tend to seek out analysts who understand how to work with data and present it to stakeholders. Learning statistics for data science can also provide a good salary. As of 2020, the median pay for computer and information research scientists in the US is $122,840 and the job market remains positive, according to the Bureau of Labor Statistics. Mathematicians and statisticians have a similar job outlook and a median salary of $92,030 per year.‎

    Data analysis, data architects, data scientists, and information officers typically use statistics for data science in their regular work. Data science is a broad field, and statistics can be useful in other roles that require analyzing and presenting data. This includes data warehouse analysts, data visualization developers, database managers, and machine learning engineers. Additional related fields include financial analysts, teachers, and researchers working for universities and corporate settings.‎

    Through online courses, you can learn the fundamentals of statistics for data science, including the theories and techniques statisticians use in their work. Some courses explore fundamental concepts like Bayes’ Theorem and probability theory. Others present methods for calculating and evaluating data sets. You can brush up on your knowledge of programs statisticians use, like Excel and Python, or examine the application of statistics specific fields.‎

    Online Statistics for Data Science courses offer a convenient and flexible way to enhance your knowledge or learn new Statistics for Data Science skills. Choose from a wide range of Statistics for Data Science courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Statistics for Data Science, 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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