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Statistics For Data Science Courses

Statistics for Data Science courses can help you learn data visualization, hypothesis testing, regression analysis, and probability theory. You can build skills in interpreting data trends, making predictions, and conducting A/B testing. Many courses introduce tools like R, Python, and SQL, that support analyzing datasets and implementing statistical models. By engaging with these tools, you can effectively apply statistical methods to real-world data challenges, enhancing your ability to draw meaningful insights.

Popular Statistics For Data Science Courses and Certifications


  • Status: Free Trial
    Free Trial
    I

    IBM

    Statistics for Data Science with Python

    Skills you'll gain: Descriptive Statistics, Data Visualization, Statistical Analysis, Data Presentation, Data Analysis, Probability Distribution, Statistics, Statistical Methods, Statistical Hypothesis Testing, Data Science, Statistical Programming, Data Visualization Software, Probability & Statistics, Jupyter, Regression Analysis, Statistical Modeling, Descriptive Analytics, Statistical Inference, Correlation Analysis, Probability

    4.5
    Rating, 4.5 out of 5 stars
    ·
    463 reviews

    Mixed · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    The Power of Statistics

    Skills you'll gain: A/B Testing, Sampling (Statistics), Data Analysis, Analytics, Statistics, Descriptive Statistics, Statistical Analysis, Statistical Hypothesis Testing, Probability & Statistics, Advanced Analytics, Probability Distribution, Data Science, Statistical Inference, Statistical Programming, Statistical Methods, Probability, Python Programming

    4.8
    Rating, 4.8 out of 5 stars
    ·
    898 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Pennsylvania

    Statistics for Data Science Essentials

    Skills you'll gain: Probability, Probability & Statistics, Sampling (Statistics), Probability Distribution, Statistics, Data Science, Statistical Inference, Statistical Methods, Descriptive Statistics, Statistical Analysis, General Mathematics, Algebra

    4.5
    Rating, 4.5 out of 5 stars
    ·
    13 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Amsterdam

    Basic Statistics

    Skills you'll gain: Statistical Hypothesis Testing, Probability & Statistics, Statistical Methods, Statistics, Statistical Analysis, Quantitative Research, Data Analysis Software

    4.6
    Rating, 4.6 out of 5 stars
    ·
    4.7K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    What is Data Science?

    Skills you'll gain: Data Literacy, Data Mining, Data Processing, Big Data, Cloud Computing, Data Analysis, Data Science, Digital Transformation, Data-Driven Decision-Making, Data Storage, Deep Learning, Machine Learning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    78K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Data Science Foundations: Statistical Inference

    Skills you'll gain: Probability, Statistical Hypothesis Testing, Statistical Inference, Probability & Statistics, Statistical Methods, Probability Distribution, Statistics, Bayesian Statistics, Statistical Analysis, Sampling (Statistics), Applied Mathematics, Data Ethics, Data Analysis, Correlation Analysis, Data Science, Sample Size Determination, Artificial Intelligence

    Build toward a degree

    4.4
    Rating, 4.4 out of 5 stars
    ·
    362 reviews

    Intermediate · Specialization · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    Excel Basics for Data Analysis

    Skills you'll gain: Excel Formulas, Data Cleansing, Pivot Tables And Charts, Spreadsheet Software, Data Wrangling, Microsoft Excel, Data Analysis, Data Quality, Google Sheets, Data Manipulation, Data Integrity, Data Entry, Data Import/Export, Data Science, Information Privacy

    4.7
    Rating, 4.7 out of 5 stars
    ·
    11K reviews

    Beginner · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    J

    John Wiley & Sons

    Foundations of Data Science and Statistical Methods

    Skills you'll gain: Statistical Methods, Exploratory Data Analysis, Data Quality, Statistics, Data Analysis, Data Science, Statistical Analysis, Probability & Statistics, Data Storage, Data Collection, Data Management, Data Pipelines, Statistical Machine Learning, Data-Driven Decision-Making, Applied Mathematics, Probability Distribution, Machine Learning, Linear Algebra

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    R

    Rice University

    Business Statistics and Analysis

    Skills you'll gain: Statistical Hypothesis Testing, Microsoft Excel, Statistical Methods, Pivot Tables And Charts, Regression Analysis, Data Literacy, Statistics, Descriptive Statistics, Probability & Statistics, Graphing, Spreadsheet Software, Probability Distribution, Business Analytics, Statistical Modeling, Statistical Analysis, Statistical Inference, Excel Formulas, Data Analysis, Presentations, Sample Size Determination

    4.7
    Rating, 4.7 out of 5 stars
    ·
    13K reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    I

    IBM

    IBM Data Analyst

    Skills you'll gain: Data Storytelling, Dashboard Creation, Data Presentation, Data Wrangling, Generative AI, Plotly, Data Visualization Software, Web Scraping, Data Visualization, Exploratory Data Analysis, SQL, Plot (Graphics), Dashboard, Data Analysis, Professional Networking, IBM Cognos Analytics, Excel Formulas, Data Import/Export, Python Programming, Microsoft Excel

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    99K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    E

    EDUCBA

    Statistics for Data Science with Python

    Skills you'll gain: Descriptive Statistics, Probability & Statistics, Statistical Hypothesis Testing, Regression Analysis, Statistics, Predictive Modeling, Statistical Programming, Statistical Analysis, Statistical Methods, Data Science, Data Analysis, Statistical Modeling, Histogram, Statistical Visualization, Pandas (Python Package), NumPy, Statistical Inference, Predictive Analytics, Probability, Model Evaluation

    Mixed · Course · 1 - 4 Weeks

  • Status: Preview
    Preview
    D

    Duke University

    Data Science Math Skills

    Skills you'll gain: Probability, Graphing, Algebra, Bayesian Statistics, Data Science, Calculus, General Mathematics, Applied Mathematics, Derivatives

    4.5
    Rating, 4.5 out of 5 stars
    ·
    13K reviews

    Beginner · Course · 1 - 4 Weeks

1234…834

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

  • Statistics for Data Science with Python: IBM
  • The Power of Statistics: Google
  • Statistics for Data Science Essentials: University of Pennsylvania
  • Basic Statistics: University of Amsterdam
  • What is Data Science? : IBM
  • Data Science Foundations: Statistical Inference: University of Colorado Boulder
  • Excel Basics for Data Analysis: IBM
  • Foundations of Data Science and Statistical Methods: John Wiley & Sons
  • Business Statistics and Analysis: Rice University
  • IBM Data Analyst: IBM

Frequently Asked Questions about Statistics For Data Science

Statistics for data science is a branch of mathematics that focuses on collecting, analyzing, interpreting, presenting, and organizing data. It plays a crucial role in data science as it provides the tools and methodologies needed to make sense of complex data sets. Understanding statistics allows data scientists to draw meaningful conclusions from data, make predictions, and inform decision-making processes. In a world increasingly driven by data, the ability to analyze and interpret statistical information is essential for businesses and organizations to thrive.‎

A variety of job opportunities exist for individuals skilled in statistics for data science. Common roles include data analyst, data scientist, statistician, business intelligence analyst, and quantitative analyst. These positions often require a strong foundation in statistical methods and the ability to apply these techniques to real-world problems. Additionally, industries such as finance, healthcare, marketing, and technology are actively seeking professionals who can leverage statistical insights to drive business strategies and improve outcomes.‎

To succeed in statistics for data science, you should focus on developing several key skills. These include a solid understanding of descriptive and inferential statistics, proficiency in statistical software (such as R or Python), and the ability to visualize data effectively. Familiarity with probability theory, hypothesis testing, regression analysis, and machine learning concepts is also beneficial. Building these skills will empower you to analyze data confidently and derive actionable insights.‎

There are numerous online courses available to help you learn statistics for data science. Some highly recommended options include the Statistics for Data Science Essentials course, which covers fundamental concepts, and the Probability & Statistics for Machine Learning & Data Science course, which focuses on applying statistical methods in machine learning contexts. Additionally, the Advanced Statistics for Data Science Specialization offers a deeper dive into advanced topics.‎

Yes. You can start learning statistics for data science on Coursera for free in two ways:

  1. Preview the first module of many statistics for data science courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in statistics for data science, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn statistics for data science effectively, start by identifying your current skill level and the specific areas you want to improve. Enroll in online courses that match your interests, such as those focusing on statistical methods or programming languages like R and Python. Practice regularly by working on real-world data sets and projects. Engage with online communities or study groups to discuss concepts and share insights. This hands-on approach will help reinforce your learning and build confidence in applying statistical techniques.‎

Typical topics covered in statistics for data science courses include descriptive statistics, probability distributions, hypothesis testing, regression analysis, and data visualization techniques. Courses may also explore advanced topics such as Bayesian statistics, machine learning algorithms, and statistical modeling. By covering these subjects, learners gain a comprehensive understanding of how to analyze and interpret data effectively, which is essential for making informed decisions in various fields.‎

For training and upskilling employees in statistics for data science, consider courses like the Data Science: Statistics and Machine Learning Specialization and the Statistics & Mathematics for Data Science & Data Analytics course. These programs provide a structured approach to learning essential statistical concepts and their applications in data science, making them suitable for workforce development.‎

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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