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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
      U
      I

      Multiple educators

      Data Science Foundations

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

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

      Beginner · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Statistical Inference

      Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      4.4K reviews

      Mixed · Course · 1 - 4 Weeks

    • D

      DeepLearning.AI

      AI For Everyone

      Skills you'll gain: Data Ethics, Market Opportunities, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Team Building, Machine Learning, Strategic Thinking, Data Science, Needs Assessment, Deep Learning, Business Ethics, Artificial Neural Networks, Engineering Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      47K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      U

      University of Leeds

      Programming for Data Science

      Skills you'll gain: Jupyter, Programming Principles, Computer Programming, Python Programming, Development Environment, Software Installation, Data Structures, Data Science, Software Documentation, Debugging

      4.3
      Rating, 4.3 out of 5 stars
      ·
      25 reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Colorado Boulder

      Statistics and Data Analysis with Excel, Part 1

      Skills you'll gain: Descriptive Statistics, Statistical Visualization, Data Transformation, Data Cleansing, Probability, Box Plots, Histogram, Probability Distribution, Probability & Statistics, Scatter Plots, Statistics, Microsoft Excel, Excel Formulas, Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      27 reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Pennsylvania

      Data Analysis Using Python

      Skills you'll gain: Matplotlib, Data Analysis, Pandas (Python Package), Data Cleansing, Pivot Tables And Charts, Data Visualization Software, Data Manipulation, Scatter Plots, NumPy, Data Transformation, Jupyter, Data Validation, Data Import/Export, Histogram, Data Structures, Programming Principles, Scripting Languages

      4.5
      Rating, 4.5 out of 5 stars
      ·
      416 reviews

      Beginner · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      A Crash Course in Data Science

      Skills you'll gain: Data Science, Data Management, Data-Driven Decision-Making, Project Design, Performance Metric, Software Engineering, Machine Learning, Predictive Modeling, Statistical Inference

      4.5
      Rating, 4.5 out of 5 stars
      ·
      8.2K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      E

      Eindhoven University of Technology

      Improving your statistical inferences

      Skills you'll gain: Statistical Inference, Scientific Methods, Statistical Hypothesis Testing, Quantitative Research, Bayesian Statistics, Statistical Analysis, Statistical Methods, Probability & Statistics, Sample Size Determination, Research, Data Collection, R Programming, Data Sharing, Probability Distribution

      4.9
      Rating, 4.9 out of 5 stars
      ·
      793 reviews

      Intermediate · Course · 1 - 3 Months

    • F

      Fractal Analytics

      Python for Data Science

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Visualization, Statistical Analysis, Probability & Statistics, Statistics, Data Wrangling, Pandas (Python Package), Data Analysis, Jupyter, Data Processing, Data Manipulation, Data Science, Descriptive Statistics, Data Transformation, Applied Machine Learning, Data Cleansing, Python Programming

      4
      Rating, 4 out of 5 stars
      ·
      45 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free
      Free
      U

      University of London

      Foundations of Data Science: K-Means Clustering in Python

      Skills you'll gain: Data Visualization, Matplotlib, Probability & Statistics, Data Science, Unsupervised Learning, Statistics, NumPy, Python Programming, Pandas (Python Package), Data Analysis, Machine Learning Algorithms, Descriptive Statistics, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      716 reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free
      Free
      U

      University of London

      Probability and Statistics: To p or not to p?

      Skills you'll gain: Descriptive Statistics, Statistics, Probability & Statistics, Statistical Hypothesis Testing, Data Visualization, Data-Driven Decision-Making, Statistical Modeling, Data Analysis, Probability, Probability Distribution, Sampling (Statistics), Risk Modeling, Statistical Inference, Mathematical Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.5K reviews

      Beginner · Course · 1 - 3 Months

    • Status: AI skills
      AI skills
      M

      Microsoft

      Microsoft Power BI Data Analyst

      Skills you'll gain: Data Storytelling, Dashboard, Excel Formulas, Extract, Transform, Load, Power BI, Data Analysis Expressions (DAX), Microsoft Excel, Microsoft Copilot, Data Modeling, Data-Driven Decision-Making, Star Schema, Data Analysis, Data Presentation, Data Visualization Software, Spreadsheet Software, Data Validation, Interactive Data Visualization, Data Transformation, Data Cleansing, Data Storage

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      7.1K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    Statistics For Data Science learners also search

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    1234…834

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

    • Data Science Foundations: IBM
    • Statistical Inference: Johns Hopkins University
    • AI For Everyone: DeepLearning.AI
    • Programming for Data Science: University of Leeds
    • Statistics and Data Analysis with Excel, Part 1: University of Colorado Boulder
    • Data Analysis Using Python: University of Pennsylvania
    • A Crash Course in Data Science: Johns Hopkins University
    • Improving your statistical inferences: Eindhoven University of Technology
    • Python for Data Science: Fractal Analytics
    • Foundations of Data Science: K-Means Clustering in Python: University of London

    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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