Skills you'll gain: Probability & Statistics, General Statistics, Mathematics, Probability Distribution, Regression, Linear Algebra, Experiment, Bayesian Statistics, Statistical Tests, Econometrics, Machine Learning, Basic Descriptive Statistics, Biostatistics, Calculus, Algebra, Artificial Neural Networks, Dimensionality Reduction, Machine Learning Algorithms, Statistical Machine Learning, Communication, Correlation And Dependence, Data Analysis, Estimation, Exploratory Data Analysis, Statistical Analysis
Advanced · Specialization · 3-6 Months
Skills you'll gain: Computer Programming, Python Programming, Computer Science, Data Structures, Theoretical Computer Science, Computational Thinking, Human Computer Interaction, User Experience, Statistical Programming
Beginner · Course · 1-3 Months
Skills you'll gain: Data Analysis, Business Analysis, Machine Learning, Markov Model, Algorithms, Big Data, Data Analysis Software, Machine Learning Algorithms, Statistical Analysis, Theoretical Computer Science, Exploratory Data Analysis
Mixed · Course · 1-4 Weeks
Skills you'll gain: Human Computer Interaction, Probability & Statistics, User Experience, Big Data, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Design and Product, Dimensionality Reduction, Experiment, General Statistics, Machine Learning, Python Programming, Statistical Programming
Intermediate · Course · 1-3 Months
Skills you'll gain: R Programming, Data Analysis, Data Science, Exploratory Data Analysis, Statistical Programming, Data Visualization Software, Software Visualization, Statistical Visualization, Basic Descriptive Statistics, General Statistics, Big Data, Computer Programming, Computer Programming Tools, Data Structures, Experiment, Linear Algebra, Machine Learning Software, Probability & Statistics, Probability Distribution, Software Engineering Tools, Spreadsheet Software, Statistical Tests, Application Development, Business Analysis, Data Management, Data Visualization, Extract, Transform, Load, Knitr, Plot (Graphics)
Beginner · Specialization · 3-6 Months
Skills you'll gain: Accounting, Business Analysis, Data Analysis, Data Management, Financial Analysis, Probability & Statistics, General Statistics, Correlation And Dependence, Data Visualization, Business Intelligence, Probability Distribution, Statistical Analysis
Mixed · Course · 1-3 Months
Skills you'll gain: General Statistics, Probability & Statistics, Machine Learning, Data Analysis, Regression, Business Analysis, Computer Programming, Correlation And Dependence, Econometrics, Forecasting, Machine Learning Algorithms, Probability Distribution, Python Programming, R Programming, Statistical Analysis, Statistical Programming, Statistical Tests, Data Science
Beginner · Course · 1-3 Months
Skills you'll gain: Machine Learning, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Applied Machine Learning, Computer Programming, Python Programming, Statistical Programming, Theoretical Computer Science, Tensorflow, Algorithms, Data Management, Data Structures, Probability & Statistics, General Statistics, Statistical Machine Learning, Computer Vision, Mathematics, Probability Distribution, Computational Logic, Linear Algebra, Mathematical Theory & Analysis
Beginner · Course · 1-4 Weeks
Skills you'll gain: Data Science, General Statistics, Probability & Statistics, Statistical Tests, Estimation, Basic Descriptive Statistics, Correlation And Dependence, Probability Distribution, Regression, Bayesian Statistics, Data Analysis, Data Visualization, Econometrics, Experiment, Machine Learning, Markov Model, Plot (Graphics), Statistical Analysis, Statistical Visualization
Beginner · Course · 1-3 Months
Advanced statistics are the mathematical tools used to discover and explore complex relationships between different variables in large datasets. In contrast to basic statistics such as average and analysis of variance (ANOVA) that simply describe the characteristics of a dataset, advanced statistical approaches often seek to make predictions about the world. This requires the use of more sophisticated statistical inference tools, such as generalized linear models for regression analysis capable of establishing how multiple interrelated factors may impact projected outcomes.
These advanced statistical methods are increasingly important in the field of data science, which is tasked with uncovering important business insights and developing predictive models from diverse big data-scale datasets. These techniques are also especially important for the proper training and use of machine learning algorithms. As in data science and machine learning more generally, R programming and Python programming skills are typically relied upon to conduct these advanced statistical analyses.
Advanced statistics skills are essential for work in data science, machine learning, and artificial intelligence (AI), as statistical approaches are at the heart of the learning algorithms that make these applications possible. An understanding of statistics is likewise important for professionals in finance, healthcare, and other industries that are increasingly making use of machine learning and AI, as they increasingly need to work closely with data scientists to ensure that these powerful techniques are developed to solve the right business problems.
Those wishing to delve deeper into advanced statistical methods and help develop new mathematical approaches in the field may pursue a master’s or even a PhD in statistics. These experts work in academia, government, or at private sector companies involved in scientific or engineering research. According to the Bureau of Labor Statistics, professional statisticians earn a median annual salary of $91,160, and this specialized career path is expected to be in high demand due to expanding opportunities to use statistics to navigate our data-rich world.
Certainly. Coursera offers a variety of courses in advanced statistics as well as their applications in the context of fields like data science and machine learning. In fact, coursework in statistics is often a prerequisite for data science classes. Regardless of your level of expertise and needs in these areas, Coursera enables you to learn remotely from top-ranked schools like the University of Michigan, Johns Hopkins University, and Duke University. And, since you can view course materials and complete coursework on a flexible schedule, there’s an exceedingly high probability that you can fit online learning about advanced statistics into your existing school or work life.
You need to have strong math skills, especially in basic calculus, linear algebra, and statistics before starting to learn advanced statistics. It's important that you have strong technical skills and are very comfortable on the computer, strong analytical skills, and the ability to carefully examine and question data that is presented to you so that you can organize and draw conclusions from it. For learning some concepts in advanced statistics, you'll need to have experience using the R statistical software package and understand Bayesian estimation, principles of maximum-likelihood estimation, and calculus-based probability.
People who enjoy mathematics are best suited for roles in advanced statistics, especially those who enjoy concepts like probability, linear models, and statistics and how they relate to data science. They can quickly grasp and apply complex technical concepts as well. Those who enjoy testing hypotheses and figuring out uncertain outcomes based on probability are also well suited for roles in advanced statistics. Also, people who have wide-ranging computer skills, the ability to communicate their statistical findings in plain language, problem-solving and analytical skills, and teamwork and collaborative skills are best suited for roles involving advanced statistics.
If you're aspiring to be a biostatistician or data scientist, learning advanced statistics is probably right for you. If you're interested in machine learning and the development of data products, you may also find learning advanced statistics is right for you. People who want to have a career as a statistician, statistical epidemiologist, sports analyst, actuary, market researcher, or investment analyst may also find learning advanced statistics to be the right choice. And if you need to understand how to transform complex sets of data into practical applications, learning advanced statistics is right for you.