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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.


More to explore:

Popular Statistics For Data Science Courses and Certifications


  • Status: Free Trial
    Free Trial
    D

    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, Statistical Machine Learning, Data Science, Statistical Modeling, Exploratory Data Analysis, Statistical Visualization

    4.6
    Rating, 4.6 out of 5 stars
    ·
    670 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Statistics for Data Science with Python

    Skills you'll gain: Descriptive Statistics, Statistical Analysis, Data Analysis, Probability Distribution, Statistics, Data Visualization, Statistical Methods, Statistical Hypothesis Testing, Regression Analysis, Probability & Statistics, Scientific Visualization, Data Science, Matplotlib, Exploratory Data Analysis, Probability, Correlation Analysis, Pandas (Python Package), Jupyter

    4.5
    Rating, 4.5 out of 5 stars
    ·
    451 reviews

    Mixed · Course · 1 - 3 Months

  • Status: Preview
    Preview
    S

    Stanford University

    Introduction to Statistics

    Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Probability Distribution

    4.6
    Rating, 4.6 out of 5 stars
    ·
    4.3K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Advanced Statistics for Data Science

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Regression Analysis, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Probability, Probability Distribution, R Programming, Biostatistics, Data Science, Statistics, Mathematical Modeling, Data Analysis, Data Modeling, Applied Mathematics

    4.4
    Rating, 4.4 out of 5 stars
    ·
    783 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    The Power of Statistics

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

    4.8
    Rating, 4.8 out of 5 stars
    ·
    860 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Statistics with Python

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Methods, Statistical Inference, Bayesian Statistics, Data Visualization, Statistics, Matplotlib, Statistical Visualization, Statistical Software, Probability & Statistics, Model Evaluation, Statistical Analysis, Jupyter, Statistical Programming, Statistical Machine Learning, Regression Analysis, Data Visualization Software, Python Programming

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.3K reviews

    Beginner · Specialization · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Data Science: Statistics and Machine Learning

    Skills you'll gain: Shiny (R Package), Rmarkdown, Model Evaluation, Regression Analysis, Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Interactive Data Visualization, Probability & Statistics, Statistical Machine Learning, Data Presentation, Data Visualization, Statistical Analysis, Statistical Modeling, R Programming, Machine Learning, GitHub

    4.4
    Rating, 4.4 out of 5 stars
    ·
    7.2K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Foundations of Probability and Statistics

    Skills you'll gain: Probability, Statistical Inference, Estimation, Probability & Statistics, Probability Distribution, Statistical Methods, Statistics, Markov Model, Bayesian Statistics, Data Literacy, Statistical Analysis, Sampling (Statistics), Applied Mathematics, Artificial Intelligence, Generative AI, Data Analysis, Data Science, Theoretical Computer Science, Machine Learning Algorithms, Mathematical Theory & Analysis

    Build toward a degree

    4.4
    Rating, 4.4 out of 5 stars
    ·
    328 reviews

    Intermediate · Specialization · 3 - 6 Months

  • C

    Coursera

    Statistics For Data Science

    Skills you'll gain: Correlation Analysis, Probability & Statistics, Statistics, Statistical Analysis, Data Analysis, Data Science, Probability Distribution, Descriptive Statistics, Statistical Inference

    4.1
    Rating, 4.1 out of 5 stars
    ·
    40 reviews

    Beginner · Guided Project · Less Than 2 Hours

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

    IBM

    IBM Data Science

    Skills you'll gain: Exploratory Data Analysis, Dashboard, Data Visualization Software, Data Visualization, Model Evaluation, SQL, Unsupervised Learning, Plotly, Interactive Data Visualization, Peer Review, Data Transformation, Supervised Learning, Jupyter, Data Analysis, Data Cleansing, Data Manipulation, Data Literacy, Generative AI, Professional Networking, Data Import/Export

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    149K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    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, Linear Algebra, Statistical Inference, A/B Testing, Statistical Analysis, Applied Mathematics, NumPy, Probability, Calculus, Dimensionality Reduction, Numerical Analysis, Machine Learning Algorithms, Data Preprocessing, Machine Learning, Machine Learning Methods

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.1K reviews

    Intermediate · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Data Visualization with Python

    Skills you'll gain: Data Visualization Software, Plotly, Data Visualization, Dashboard, Interactive Data Visualization, Matplotlib, Seaborn, Data Presentation, Data Analysis, Geospatial Information and Technology, Pandas (Python Package), Scatter Plots, Histogram, Heat Maps, Box Plots, Jupyter, Python Programming, Data Manipulation

    4.5
    Rating, 4.5 out of 5 stars
    ·
    12K reviews

    Intermediate · Course · 1 - 3 Months

1234…834

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

  • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
  • Statistics for Data Science with Python: IBM
  • Introduction to Statistics: Stanford University
  • Advanced Statistics for Data Science: Johns Hopkins University
  • The Power of Statistics: Google
  • Statistics with Python: University of Michigan
  • Data Science: Statistics and Machine Learning: Johns Hopkins University
  • Foundations of Probability and Statistics: University of Colorado Boulder
  • Statistics For Data Science: Coursera
  • IBM Data Science: 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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