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.

DeepLearning.AI
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
Beginner · Course · 1 - 4 Weeks

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
Beginner · Course · 1 - 3 Months

University of Michigan
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
Beginner · Specialization · 1 - 3 Months

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
Advanced · Course · 1 - 3 Months

University of Colorado Boulder
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
Beginner · Specialization · 3 - 6 Months

Stanford University
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
Beginner · Course · 1 - 3 Months

University of Colorado Boulder
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
Intermediate · Specialization · 3 - 6 Months
University of Michigan
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
Intermediate · Course · 1 - 4 Weeks

University of Amsterdam
Skills you'll gain: Statistical Hypothesis Testing, Statistics, Scientific Methods, Quantitative Research, Data Analysis Software
Beginner · Course · 1 - 3 Months

University of Pennsylvania
Skills you'll gain: Probability, Probability & Statistics, Sampling (Statistics), Probability Distribution, Statistics, Data Science, Statistical Inference, Descriptive Statistics, Statistical Analysis, General Mathematics
Intermediate · Course · 1 - 4 Weeks

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

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
Intermediate · Course · 1 - 3 Months
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:
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.‎