This list of ten free resources for Statisticians consists of high-quality courses, blogs, and podcasts that can be read, listened to, and studied at your leisure. There are ones that help educate those studying to be statisticians, those who have just entered a career, and those who have been working for a while. Even seasoned expert statisticians can benefit from these free resources to help stay current in their desired specialty.
Statisticians are essential to how we function in our daily life and all of society. The more accessible free resources for statisticians, the better since it takes a massive amount of education, knowledge, and skills to succeed as a statistician. With immediate access to read, watch, and learn about most anything on the internet, the greater the opportunity for statisticians to enhance their education.
There are countless free online resources out there, and we have given you a few examples of where you can start in your search. Highly qualified universities offer the courses, the blogs discuss essential topics, and the podcast includes expert insights from experienced data professionals.
Big Data TedTalks
TedTalks is a nonprofit organization that began as a Technology, Entertainment, and Design conference. Its goal is to “make great ideas accessible.” It offers free videos full of short talks on almost every topic. This is an excellent free resource for statisticians to utilize to deepen their knowledge in many different areas.
The Big Data TedTalks offer insight into the enormous amount of data businesses deal with daily. They offer how tos, insights into issues and solutions, and plenty of conversations about big data. There are over a thousand TedTalks about big data that can be beneficial to statisticians at every level, from undergraduate students to seasoned professionals. These videos cover how you can spot a bad statistic, use smart statistics to fight crime, miss out on key human insights when relying on big data, and find the beauty in data visualization. A plethora of big data videos exists for statisticians through this free resource.
Data, Models and Decisions in Business Analytics Course
Columbia University’s advanced free online course on Data, Models & Decisions in Business Analytics takes up eight to ten hours a week for twelve weeks. Before pursuing this course, students must have completed data-related undergraduate coursework and be familiar with Python and basic programming concepts. The free course offers practical applications for topics covered, including statistics, linear, nonlinear & discrete optimization, probability, regression, and stochastic modeling. Statisticians gain much-needed knowledge from these topics in how to utilize historical data, software, concepts, and methods to make decisions that solve business problems.
Columbia University is located in the highly resourceful and high-tech city of New York. It has a rich history, over 250 years, of educating our nation and the world. It carries a prestigious reputation for providing a distinguished education and being an essential research center. The University’s online offerings, ColumbiaX, offers several free data-related courses that could benefit statisticians at any level in their career pursuit.
Fitting Statistical Models to Data with Python Course
The Fitting Statistical Models to Data with Python Course, offered by the University of Michigan through Coursera, is one of three free courses that make up the Statistics with Python Specialization. This free online course offers flexibility in scheduling, is self-paced, and can be completed in 15 hours. Students dive into statistical inference and modeling techniques like generalized linear models, linear regression, hierarchical & mixed effects models, logistic regression, and Bayesian inference techniques. They will learn to identify relationships between variables, join research and data analysis methods, and even make predictions. There are lab-based sessions that utilize real data sets, case studies, and Python libraries like Pandas, Statsmodels, and Seaborn. It would be beneficial for statisticians to pursue this free course full of crucial concepts that can be utilized in many fields. The course offers all of the knowledge and essential skills in Statistical Models, Statistical Regression, Bayesian Statistics, and Python Programming.
FlowingData is a blog by Nathan Yau, who earned his Ph.D. in statistics at UCLA. He focused his studies on visualization, has a passion for helping others understand data, and believes “visualization is the best way to get there.” His blog displays his projects, other people’s work, and visualization guides.
Statisticians can learn a lot from this free resource. Topics include visualization, infographics, statistics, sources, maps, networks, software, and design or data art. There are an immense amount of articles charting all kinds of Covid-19 statistics, along with charts showing unemployment, a bitcoin scam, international students in higher education, climate change, spotting misinformation on the internet, how to make charts, responsible mapping, prison population, mapping chemical plants, and so much more. There is a list of books about data points, data fluency, designing with data graphics, making data graphics, thinking about data, and Nathan’s very own: The FlowingData Guide to Design, Visualization, and Statistics.
Inferential and Predictive Statistics for Business Course
The Coursera course, Inferential and Predictive Statistics for Business, is offered by the University of Illinois at Urbana-Champaign. This free course can be completed as part of earning the Managerial Economics & Business Analytics Specialization within the University’s fully-accredited online MBA program, or entirely by itself. Although it does not provide university credit, this self-paced free course will earn you shareable Course Certificates and is accessible to anyone.
Everything this course has to offer is essential to statisticians, especially those in, or will be in a managerial position. Through the lens of management, students learn how to identify, describe, create a data model, and make decisions regarding different variations. They will study diverse sets of data from a variety of situations and learn to understand statistical concepts, apply methodologies, and properly interpret the findings. In this free course, statisticians will receive analytical management tools that help prepare them to succeed in the challenging world of business.
Machine Learning Course
Our world today is a place where machines can ‘learn for themselves.’ Machines use computer programs to access data, process it, and make predictions or ‘decisions.’ With the rise of artificial intelligence, the need for experts in machine learning is growing. The University of Texas has developed a free online course titled Machine Learning that lasts 12 weeks. Whether a statistician is working in this field directly or not, they can learn valuable knowledge and skills for any statistical career.
In this free course, students learn statistical methods, how to develop and learn algorithms, regression techniques, classification, feature extraction, interpretation tools, and core concepts that deal with machine learning. It has applications throughout the sciences and in data analytics, engineering, service personalization, predictive analysis, computer vision, and speech recognition. Specific topics covered throughout the course include overfitting, PAC learning, gradient descent, pattern recognition, Bayesian methods, maximum likelihood, and neural networks.
SAS Free Resources
SAS, “an analytics powerhouse,” provides analytics software and solutions utilized by organizations all over the world. Fortunately, SAS offers free resources for statisticians and data specialists through SAS E-learning. Among these free resources are 15 free online courses, and a multitude of webinars, access to software, and tutorials. Courses include the Essentials of SAS Programming, SAS Platform Administration, SAS Programming for R Users, and SAS Viya Enablement. Webinars teach about things like data scientists, digital transformation, data-driven supply chain resilience, content marketing, and integrating SAS with Git. Also, video tutorials show how to use SAS Studio, write a SAS program, SAS Windowing Environment, perform conditional logic, and more.
SAS also offers a 100% online, self-paced, introductory course through Coursera entitled: Statistics with SAS. This free course is for those who are familiar with SAS/STAT software for statistical analyses and covers linear regression, t-tests, and ANOVA. All of these concepts benefit statisticians.
Statistical Inference Course
Many statisticians take data from specific populations, analyze it, and determine essential parameters that aid in effective decision-making. This process is statistical inference. Johns Hopkins University, through Coursera, offers a free course entitled Statistical Inference. The excellent free online course provides four weeks of material and can be completed in 54 hours. It is also applicable to a Data Science Specialization.
There is a multitude, and often an overwhelming amount of techniques and ideas that can be utilized to ascertain statistics. This free course focuses on the fundamentals from a practical standpoint. It instructs on probability, variability, expected values, distribution, limits, confidence intervals, testing, P values, asymptotics, bootstrapping, power, and permutation tests. Students obtain an understanding of all of these concepts, can describe them in detail, and enhance their ability to make informed decisions regarding data analysis. Once the course ends, students will have gained essential skills in statistical inference, statistics, and statistical hypothesis testing.
Statistical Modeling, Causal Inference, and Social Science Blog
A blog is a free online resource that offers an incredible amount of insight and expertise into most any topic. The Statistical Modeling, Causal Inference, and Social Science Blog is an exceptional free resource that statisticians can take advantage of to enhance their knowledge in critical areas. These topics deal with the generation of data, statistical assumptions, analysis of causal connections, and human behavior. All of which are important for statisticians to understand to be successful.
Andrew, the writer of the blog, discusses many different issues within these topics that either spark a conversation or lead the reader to a deeper understanding. Some of the posts consider the relationships between substantive theories, social science, and causal inference or negativity versus positivity. Other topics include the Bayesian workflow, using an example of coronavirus and the good and the bad on randomized clinical trials. Multilevel regression, poststratification, and robust statistical procedures are also discussed in different posts.
Super Data Science Podcast
The Super Data Science Podcast is a free resource that can be highly beneficial to statisticians. In general, podcasts are informative, entertaining, and accessible for anyone to listen to at any time. This podcast features experts in data. It focuses on all things related to data, especially data science. It hits on topics like data science careers and journeys, women in STEM, utilizing Oracle Cloud, the power of memory, TensorFlow and AI learning, real data analytics for economics, COVID-19 modeling, and success as an analytics consultant.
T. Scott Clendaniel is a pioneer in artificial intelligence and predictive analysis and shares his insight into data science in one of the podcasts. Some of the content includes imbalanced data sets, fraud detection models, model drift, data science management, and data science in the investment industry. Another podcast features Tony Saldanha, CEO of Transformant, who defines digital transformation and shares how to adapt to it in the current industrial revolution.
Data is evolving. It evolves in the way we view, collect, and analyze information and in the formulas and programs needed. The function and abilities of the internet continue to change. It is a world filled with social media, smart devices, and other user-generated content. There is now the ability to formulate sophisticated algorithms to coincide with increasing computer power. All of the free courses in this list provide an immense amount of knowledge to equip statisticians with the tools to successfully handle all data levels. The topics covered business analytics, big data, data visualization, machine learning, causal inference, modeling, SAS programming, inferential and predictive statistics, and data science.
These free resources for statisticians help to prepare professionals for a multitude of positions. When you can work through all of the content at your own pace, what is keeping you? Yes, it does take time to complete the courses, listen to the podcasts, and read through all of the blogs. But, if you are interested in or already a part of a data-related field, especially as a statistician, the time to continue your education is now. You may not receive a certificate upon completion, but you will gain a wealth of priceless information that will be beneficial to you for the rest of your career.