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  • Applied Statistics

Applied Statistics Courses

Applied statistics courses can help you learn data analysis, hypothesis testing, regression techniques, and experimental design. You can build skills in interpreting data sets, making predictions, and communicating statistical findings effectively. Many courses introduce tools like R, Python, and Excel, that support performing statistical analyses and visualizing data.


More to explore:

Popular Applied Statistics Courses and Certifications


  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Applied Statistics for Data Analytics

    Skills you'll gain: Probability & Statistics, Statistical Analysis, Statistics, Statistical Hypothesis Testing, Statistical Visualization, Descriptive Statistics, Data Analysis, Histogram, Probability Distribution, Correlation Analysis, Statistical Inference, Estimation, Simulation and Simulation Software, Sampling (Statistics), Large Language Modeling, Spreadsheet Software

    4.9
    Rating, 4.9 out of 5 stars
    ·
    51 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    M

    Meta

    Statistics Foundations

    Skills you'll gain: Bayesian Statistics, Descriptive Statistics, Statistical Hypothesis Testing, Statistical Inference, Sampling (Statistics), Data Modeling, Statistics, Probability & Statistics, Statistical Analysis, Statistical Methods, Statistical Modeling, Marketing Analytics, Tableau Software, Data Analysis, Spreadsheet Software, Analytics, Time Series Analysis and Forecasting, Regression Analysis

    4.8
    Rating, 4.8 out of 5 stars
    ·
    380 reviews

    Beginner · 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

  • 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
    ·
    862 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Statistics and Applied Data Analysis

    Skills you'll gain: Statistical Hypothesis Testing, Descriptive Statistics, Statistical Visualization, Data Transformation, Data Cleansing, Statistical Analysis, Regression Analysis, Probability, Probability Distribution, Sampling (Statistics), Box Plots, Histogram, R Programming, Statistical Methods, Scatter Plots, Microsoft Excel, Probability & Statistics, Statistics, Data Import/Export, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    46 reviews

    Beginner · Specialization · 3 - 6 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

What brings you to Coursera today?

  • 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
    ·
    329 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Applied Machine Learning in Python

    Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Random Forest Algorithm, Regression Analysis, Classification Algorithms, Artificial Neural Networks

    4.6
    Rating, 4.6 out of 5 stars
    ·
    8.7K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Amsterdam

    Basic Statistics

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

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

    Beginner · 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, Descriptive Statistics, Statistical Analysis, General Mathematics

    4.4
    Rating, 4.4 out of 5 stars
    ·
    10 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    M

    Microsoft

    Statistical Analysis and Advanced Techniques

    Skills you'll gain: Regression Analysis, Statistical Analysis, Time Series Analysis and Forecasting, Logistic Regression, R Programming, Statistical Hypothesis Testing, Probability & Statistics, Statistical Modeling, R (Software), Statistics, Predictive Modeling, Generative AI, Forecasting, Descriptive Statistics, Power BI

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    P

    Packt

    Statistics & Mathematics for Data Science & Data Analytics

    Skills you'll gain: Statistics, Data Analysis, Statistical Analysis, Regression Analysis, Analytics, Statistical Methods, Probability, Data Science, Statistical Modeling, Data-Driven Decision-Making, Statistical Inference, Probability Distribution, Applied Machine Learning, Predictive Modeling, Model Evaluation

    4.8
    Rating, 4.8 out of 5 stars
    ·
    10 reviews

    Intermediate · Course · 1 - 3 Months

1234…225

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

  • Applied Statistics for Data Analytics: DeepLearning.AI
  • Statistics Foundations: Meta
  • Statistics with Python: University of Michigan
  • The Power of Statistics: Google
  • Statistics and Applied Data Analysis: University of Colorado Boulder
  • Introduction to Statistics: Stanford University
  • Foundations of Probability and Statistics: University of Colorado Boulder
  • Applied Machine Learning in Python: University of Michigan
  • Basic Statistics: University of Amsterdam
  • Statistics for Data Science Essentials: University of Pennsylvania

Frequently Asked Questions about Applied Statistics

Applied statistics is the branch of statistics that focuses on practical applications of statistical techniques to real-world problems. It plays a crucial role in various fields, including business, healthcare, social sciences, and engineering. By utilizing data to inform decisions, applied statistics helps organizations and individuals make evidence-based choices, identify trends, and improve outcomes. Understanding applied statistics is essential for interpreting data accurately and for making informed decisions based on that data.‎

A background in applied statistics opens the door to a variety of career opportunities. You might consider roles such as data analyst, statistician, market researcher, or quality control analyst. Additionally, positions in fields like healthcare, finance, and social research often require expertise in applied statistics. These roles typically involve analyzing data sets, interpreting results, and communicating findings to stakeholders, making applied statistics a valuable skill in today's data-driven job market.‎

To succeed in applied statistics, you should develop a strong foundation in several key skills. First, proficiency in statistical software (such as R, Python, or SAS) is essential for data analysis. Additionally, understanding probability theory, hypothesis testing, and regression analysis will enhance your analytical capabilities. Strong problem-solving skills and the ability to communicate complex statistical concepts clearly are also important. These skills will empower you to apply statistical methods effectively in various contexts.‎

There are numerous online courses available for learning applied statistics. Some highly recommended options include the Applied Statistics for Data Analytics course, which provides practical insights into data analysis techniques. Additionally, the Statistics and Applied Data Analysis Specialization offers a comprehensive overview of statistical methods and their applications. These courses cater to different skill levels and can help you build a solid understanding of applied statistics.‎

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

  1. Preview the first module of many applied statistics 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 applied statistics, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn applied statistics effectively, start by identifying your learning goals and the specific areas of statistics that interest you. Enroll in online courses that match your skill level and desired outcomes. Engage with course materials actively by completing assignments and participating in discussions. Additionally, practice applying statistical techniques to real-world data sets to reinforce your understanding. Joining study groups or online forums can also provide support and enhance your learning experience.‎

Typical topics covered in applied statistics courses include descriptive statistics, inferential statistics, probability distributions, hypothesis testing, regression analysis, and data visualization. Courses may also explore specialized areas such as Bayesian statistics or statistical methods for specific fields like business or social sciences. This comprehensive curriculum equips learners with the tools needed to analyze data and draw meaningful conclusions.‎

For training and upskilling employees in applied statistics, consider courses like the Business Statistics and Analysis Specialization, which focuses on statistical methods relevant to business contexts. Additionally, the Statistics and Applied Data Analysis Specialization provides a broader understanding of statistical techniques applicable across various industries. These courses can enhance employees' analytical skills and improve decision-making within organizations.‎

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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