5 Best Statistics Courses & Classes Online To Take In 2023
Discover the ultimate guide to mastering statistics in 2023, as we unveil the top 5 online courses and classes, handpicked from over 1,129 popular options, ensuring you receive the best learning experience to elevate your data analysis skills and propel your career forward.
We independently evaluate all recommended online courses. If you click on
links we provide, we may receive compensation. Learn more.
Are you ready to take your statistical knowledge to the next level? We know the overwhelming feeling of sifting through countless course options. To save you time and ensure you get the best learning experience, we have meticulously researched 1,129 popular statistics courses from various providers that have attracted over 20,646,146 enrolled students who left 1,029,018 ratings and reviews.
Our comprehensive selection process involved evaluating and picking the best courses based on a multitude of factors, such as rating, reviews, enrollments, learners' feedback, valuable and engaging content, comprehensive curriculum, release date, and affordability. All this whilst combining our experiences and expertise to bring you the definitive list of the top 5 statistics courses and classes available online. Embark on your journey to statistical mastery with complete confidence, knowing that your chosen course is among the best of the best.
The "Introduction to Statistics" course offered by Stanford University equips learners with the essential statistical thinking concepts required for understanding and interpreting data. Throughout the course, you will acquire the ability to perform exploratory data analysis, gain a deep understanding of sampling principles, and identify appropriate significance tests suitable for various contexts. With a strong foundation in these skills, you will be well-prepared to delve into more advanced topics in statistical thinking and machine learning.
Throughout the course, learners will explore key topics such as Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, and Multiple Comparisons. These comprehensive subjects will enable you to build a strong knowledge base in statistics, which is increasingly important in today's data-driven world, and empower you to effectively communicate data-driven insights to others.
User review:
Due to this course I learn more about statictics from the very fundamental concept. I never understand in detail about how a statistical test work but this course teach me the basic idea for every test in statistics.
I highly appreciate the opportunity the team give to us to learn in free. I hope I can purchase my certificate latter. Thank you very much Prof. Guenther Walther, your explanation and description really help me reevaluate my comprehention about statistics. The feedback in every assignment is a big help to bridge me to the right answer. I would reread and rewatch this course to deepen the part that still hard for me to understand such as conditional probability and the bayesian rule.
Thank you to provide accesible knowledge to every learner around the world. Best regard... [1]... Read More
Mar'attus S
Best for:
Ideal for those looking to gain a strong foundation in statistical thinking concepts, enhance their data analysis skills, and improve decision-making abilities through statistical analysis.
Delving into the world of statistics is crucial for comprehending research within the social and behavioral sciences. This course serves as an introduction to the fundamental concepts of statistics and sets the foundation for more advanced topics in the specialization, such as inferential statistics. The curriculum covers methods of descriptive statistics, including defining cases and variables, calculating measures of central tendency (mean, median, and mode), dispersion (standard deviation and variance), as well as understanding relationships between variables through correlation and regression analyses.
The course also covers the basics of probability, tackling topics like calculating probabilities, understanding probability distributions, and working with sampling distributions—all of which are necessary to grasp the inner workings of inferential statistics. Additionally, students are introduced to methods of inferential statistics that aid in assessing the validity of patterns observed in the data for making conclusions about the underlying population of interest. The course delves into confidence intervals and significance tests, while also training students to compute and generate these statistics themselves using freely available statistical software.
Best for:
This course is best for achieving a foundational understanding of statistics and learning how to apply them to data science, analytics, and decision-making. It focuses on developing skills in descriptive statistics, probability, inferential statistics, and using statistical software.
This comprehensive course provides an in-depth understanding of how statistics play a central role in the data science approach. Students will gain valuable knowledge in designing data collection, deriving insights from visualizing data, obtaining supporting evidence for data-based decisions, and constructing models for predicting future trends from data through the application of statistical thinking methods and analytical tools.
In the realm of data science and analytics, this course lays a strong foundation in statistics, equipping learners with the skills to navigate various stages of data analysis efficiently. From data collection to prediction, the course's well-structured content ensures its relevance to both beginners and individuals seeking to boost their expertise in the fast-growing field of data science. By the end of the course, students will have developed their statistical thinking acumen, learning to apply it effectively within real-world scenarios.
Best for:
This course is ideal for those seeking to develop their statistical thinking acumen and effectively apply it to real-world scenarios, particularly within the realm of data science and analytics.
In the Inferential Statistics course, learners will be exposed to various statistical inference techniques applicable to numerical and categorical data types. The course teaches how to establish and execute hypothesis tests, draw meaning from p-values, and present the findings in a digestible manner for clients and the general audience. Throughout the course, participants will be given numerous data examples to learn how to communicate estimates effectively, which includes expressing uncertainties surrounding the focal quantities.
Additionally, learners will be guided through the installation and utilization of R and RStudio, which are free statistical software tools. These tools will be employed in lab exercises and final projects that help solidify the knowledge obtained in the course. Crucial to understanding and conveying statistical information, the course offers practical methods for data analysis, delving into the basic principles of interpreting and accurately reporting the results for categorical and numerical data types. By the end of the course, participants will be well-equipped to handle real-world data and draw meaningful conclusions utilizing statistics.
User review:
Overall a great course. Very rich in material. I do not have a strong math or statistical background and i struggled a bit with the range and quantity of material presented. Hard work is surely involved, but it is ultimately rewarding. A word of caution : if you are taking this course standalone (or as part of Coursera's Data Science Learning Path like me) without taking the first introductory part, you will have to compensate a bit on the programming parts if you are new to R (luckily a lot of freely available instructional material is found on the web, and the professor herself offers a free statistics textbook with online R labs). Not a downside for me, as this course has made me discover this fantastic language which has taken a strong position besides my budding Python skills. Cheers! [2]... Read More
Emanuele M
Best for:
This course is the best for understanding various statistical inference techniques applicable to numerical and categorical data types, drawing meaningful conclusions, and accurately communicating results obtained from data analysis.
This comprehensive course on Statistics and Probability using Excel covers crucial concepts and techniques, making it perfect for beginners who want to gain the ability to apply these skills to various business applications. By teaching students important probability distributions such as Normal distribution, Poisson distribution, Exponential distribution, and Binomial distribution, the course equips them with the necessary tools to make informed business decisions using statistical data analysis.
Designed to be engaging and interactive, this course combines theoretical concepts with step-by-step instructions on implementation in MS Excel, including downloadable Excel files containing data and solutions used in the program. Students also benefit from class notes and assignments to revise and practice the concepts in MS Excel. With a focus on real-world problem-solving, the course demonstrates how to create models for various strategies and emphasizes the value of a strong foundation in probability and statistics for success in the field of business analytics.
User review:
Its really help clear concept of Probability, Interval Estimation & Hypothesis testing. the best part is less jargon used. It really helped me as i am shifting my career from sales to data analysis field. They are also helping to obtain jobs.[3]... Read More
Lipsa Patro
Best for:
This course excels in teaching students how to apply crucial statistical concepts and techniques using Excel, enabling them to make informed business decisions through data analysis. It also emphasizes real-world problem-solving and strengthens their foundation in probability and statistics for success in business analytics.
Selecting the best statistics course requires careful consideration to match your unique learning goals and intended application of the acquired knowledge. Below is a learner guide that outlines key elements to look out for when choosing the ideal course:
Alignment with your desired learning outcomes: Ensure the course content addresses your user intents, such as gaining a basic understanding of statistics, improving analytical skills or preparing for an exam.
Course complexity: Select a course that corresponds to your current knowledge level, from introductory to advanced courses.
Course format and duration: Consider online or offline course options, as well as the course's length and schedule flexibility.
Practical applications: A course that demonstrates statistical concepts in real-world situations or the use of statistical software, such as Excel, can enhance skill development.
Instructor's credentials: Assess the expertise and experience of the course instructor in both statistics and teaching.
Course reviews and outcomes: Evaluate previous learners' feedback on the course content, delivery and learning achievements.
By comparing the courses based on the above factors, you'll be well-equipped to select the best statistics course that accelerates your learning journey and opens up opportunities in data-driven fields.
Conclusion
In conclusion, embarking on a journey to learn and master statistics not only boosts your professional skills but also enhances your ability to make data-driven decisions in various fields. With a multitude of top-notch courses available, you can be assured that you will find one that perfectly matches your needs, whether you are a beginner seeking a fundamental understanding of statistical concepts or an experienced professional aiming to sharpen your data analysis and modeling abilities.
Don't hesitate to invest your time and effort into learning statistics, as it has the potential to open new avenues in your career and empower you to excel in today's increasingly data-driven world. The knowledge and perspective gained will prove invaluable in your personal and professional growth, enabling you to make a significant positive impact on your chosen industry. Take the first step today and usher in a future filled with remarkable possibilities and accomplishments.
How much does a statistics course cost?
The cost of a statistics course varies depending on the platform and course content. Generally, the options can include free trials, monthly or annual subscriptions, or one-time fees ranging from free to $99. Discounts or free access without a certificate may also be available on some platforms.
How long do statistics courses take?
The duration of statistics courses varies greatly depending on the course content and the platform. Course durations can range from around 5 hours to over 60 hours. Keep in mind that individual pacing and commitment may affect the actual time it takes to complete a course.
What factors should be considered when choosing an online statistics course?
When choosing an online statistics course, consider factors such as course content, level of difficulty, duration, platform reputation, instructor expertise, cost, and the availability of course materials and support. You should also take into account your learning preferences, current skill level, and specific learning goals.
