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1 FREE Night Included, Click Here to Book Your Room!

Venue Information: Embassy Suites By Hilton 1000 Woodward Pl NE, Albuquerque, NM 87102

Cancellation Policy:

If you cancel your registration at least two weeks prior to the day the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). If you cancel within the 14 days prior to the course you may transfer the full amount towards any future course or receive a full refund (minus a processing fee of $295).

In the unlikely event that Stats Camp Foundation must cancel a course, our team will do our best to inform you as soon as possible with more information. Students then have the option of receiving a full refund of the course fee or a full credit towards another future course. In no event shall Stats Camp Foundation be held responsible for any incidental or consequential damages that you may incur because of the cancellation. Course disclaimer. All courses include 365 days of asynchronous access after event.

*You must register prior to the event start, attendance is NOT mandatory. However; CE credits are only available if you attend live for the duration of the course. (Includes: On-Demand, Livestream and In-Person courses, all Stats Camp Retreats are in-person attendance only.)


what students are saying

The Stats Camp expert instructors were clear, concise, and helpful in addressing questions. Before attending, I was concerned that I would have trouble truly understanding all of the concepts and material in such a short time, but the instruction was fantastic and not overwhelming. I particularly found it advantageous to stay on-site at the Embassy Suites so I could participate in all of the after hours networking events.
Best Statistics Training Course Online

Spiros Tzivelekis, Ph.D., GHE Director - Amgen

Stats Camp was the most useful statistical training I’ve ever had. The instructors are down to earth and practical in their teaching style and the classroom environment was relaxed and non-threatening, which is necessary for such a potentially daunting topic. In particular, the one-on-one private consultation with my own data was invaluable, I highly recommend signing up for the Summer Stats Camp in Albuquerque!
Best Statistics Training Course Online

Kris Carlson, Ph.D., Sandia National Laboratories

The Stats Camp instructors have an unmistakable dedication to research methods and data analysis of the very highest quality-but are remarkably balanced in their very obvious efforts to connect with others on a professional and personal level as very likable and real people. I really enjoyed the networking opportunities that the breakout sessions provided and will be returning for another Summer Camp soon!
Best Statistics Training Course Online

Chen Zhang, Ph.D., Faculty University of Memphis

Thank you to Dr. Todd Little and his team for facilitating such a great statistical methods training workshop in Albuquerque. I absolutely got what I needed and was energized in my work when I returned to Cleveland. This was a wonderful experience that I will be sure to share with my colleagues here at CWRU. Now I am looking forward to mediation this summer in Albuquerque! Who knew I would be excited for more stats!
Best Statistics Training Course Online

Leigh-Ann Sweeney, Ph.D., Lecturer and Health Service Researcher

The Stats Camp instructor’s clear and practical presentation of material that was once intimidating to me has uncovered a powerful analytic tool. I feel comfortable that I’ve learned the correct application of SEM Foundations and Extended Applications from experts in the field. At the same time, I was introduced to cutting edge statistics techniques and I understand the advantages of their practical use and application.
Best Statistics Training Course Online

Jenny Tehan, Ph.D., Department of Psychology University of Akron

Stats Camp is the place for a meeting of the minds and an opportunity to sit down and sort through complex ideas with people who can guide you, side-by-side, through the sticking points so you can get back into the flow. It’s amazing that something like this exists. Dr. Little has given the world a tremendous gift with Stats Camp. I walked in knowing nothing substantial and walked out delighted and better skilled.
Best Statistics Training Course Online

Benjamin Theisen, Ph.D., Business Psychology Consulting Group

The Summer Stats Camp training institute provides a wonderful opportunity for researchers and data analysts to learn the basic foundations of SEM as well as some advanced applications and research opportunities that SEM can facilitate. Dr. Little provides personable “hands-on”instruction in a relaxed and enjoyable environment. I highly recommend this summer institute to faculty and graduate students alike!
Best Statistics Training Course Online

Paul Schrodt, Ph.D., Professor and Director of Graduate Studies

I was impressed with the amount of material Dr. Todd Little and team were able to cover in Summer Camp. The instructors moved at a pace appropriate for the participants, adapted the materials as we went along to accommodate this pace, and still offered individual consultations. I am confident that I can take everything I learned, from the basic to the advanced topics & employ them independently in my own research.
Best Statistics Training Course Online

Katie Paschall, Ph.D., Senior Research Scientist at Child Trends

I can strongly recommend the Stats Camp Summer course in Longitudinal Mixture Modeling. It is as systematic and comprehensive as the very best of the research methods courses I have participated in since I moved from business to academics 12 years ago. It has been an important tool box for pulling apart differing effects in subpopulations for the coming generation of social and behavioral science researchers, starting now!
Best Statistics Training Course Online

Alan R. Johnson, Ph.D., Senior Research Fellow at NORD University, Bodø, Norway

I participated in the SEM: Foundations and Extended Applications course earlier this summer. It was such a wonderful statistics training opportunity that has, already, found lots of applications in various grant proposals and analyses. As I’ve said to several colleagues since, Stats Camp has a special ability to take something that prior to my arrival seemed overwhelming and complicated and make it seem so do-able.
Best Statistics Training Course Online

Amy K. Syvertsen, Ph.D., Applied Developmental Scientist

I took both Foundations of SEM and Longitudinal SEM last summer and I have to say this has been one of the most useful learning experiences of my life. The courses were excellent! Everything was explained in a clear fashion with plenty of time for questions and practice. Learning was fun and the teaching happened at multiple levels, such that everyone would have a lot of knowledge gain regardless of their level of expertise.
Best Statistics Training Course Online

Rodica Damian, Ph.D., Associate Professor University of California, Davis

The highlight of the summer break was attending ‘Stats Camp,’ an educational camp in Albuquerque New Mexico that provides advanced-level training in Statistics. The 5-day class I took (‘SEM with Mplus’) passed by quickly, as an information-packed series of lectures, hands-on examples, personal consultations, and jokes. Classes were small, lectures succinct/direct, and training content based on each individual student requests.
Best Statistics Training Course Online

Anna Yu Lee, PhD, MPH, MA, Counselor/Therapist

I can honestly say that for the first time in years I have been able to focus on myself and my research. I feel physically and mentally healthier than ever and I am excited about the cutting-edge knowledge and resources I am gaining in the rapidly evolving field of latent variable modeling. I am going to make it a goal to prioritize Statscamp for myself and graduate students on a yearly basis. Definitely recommend!
Best Statistics Training Course Online

Sarah D. Lynne-Landsman, Ph.D., Family, Youth & Community Sciences

IN PERSON – 5-day Statistics Short Course

Data Mining and Machine Learning Seminar Overview:

An intermediate 5-day course introducing several popular machine learning approaches such as regression based methods (ridge and lasso regularized regression, regression splines), tree methods (random forests, boosted trees), support vector machines, and Interpretative Machine Learning (ILM) as well as their application to empirical data. The course combines lectures and hands-on practice using R.

Seminar Topics:

  • Review of linear regression and the least squares criterion
  • Regularization methods (ridge regression, lasso, elastic net)
  • Regression splines
  • Prediction error and k-fold cross validation
  • Tree methods to predict categorical or continuous outcomes (CART, random forest, boosting
  • Interpretative Machine Learning (IML)
  • Support vector machines for classification

Seminar Description:

Machine learning refers to leveraging data to build statistical models or algorithms. The objective is usually to gain knowledge about the structure in the data in order to make predictions or decisions.

This short course is based on

  • An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)
  • Hands-on Machine Learning with R (Boemke & Greenwell)
  • Interpretable Machine Learning (Molnar)

The course starts with briefly outlining the key differences and similarities between standard parametric modeling (e.g., linear regression, structural equation modeling) and machine learning (aka statistical learning, aka data mining). The course provides basic insights into a number of popular methods such as regression methods (ridge regression and the lasso, regression splines), tree methods (CART, random forests, boosting), interpretable machine learning (IML), and support vector machines. The emphasis is on a conceptual understanding of these methods and their appropriate application to empirical data. Importantly, these methods are useful not only for large data collections, but also more generally for exploratory analyses when the substantive theory to design and fit parametric models (e.g. SEM) is lacking. Machine learning is used in a wide variety of fields including but not limited to public health, education, biology, and the different social sciences.

Participants are invited to discuss potential machine learning applications to their data during individual consultations with the instructor scheduled at the end of days 2-5.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts (R), relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.


Instructor: Gitta Lubke, Ph.D.

Gitta Lubke is a Professor Emerita in the Department of Psychology/Quantitative Area at the University of Notre Dame. Her research interests included machine learning and general latent variable modeling. Empirical applications were mainly in the field of psychiatric disorders and behavioral genetics. Other areas of expertise include mixture models, twin models, multi-group factor analysis and measurement invariance, longitudinal analyses, and the analysis of categorical data.

APA Continuing Education Credits:

data mining and machine learning

This course offers 29 hours of Continuing Education Credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Understand some of the key differences and similarities between parametric modeling and machine learning methods.
  • Expand the acquired basic knowledge of several popular machine learning methods and apply these methods to empirical data.
  • Implement ridge regression and Lasso.
  • Assess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors.
  • Implement and evaluate regression splines.
  • Implement and evaluate decision trees to categorize data.
  • Utilize CART and bagging techniques.
  • Implement random forests to evaluate data.
  • Implement and evaluate boosted trees.
  • Understand several basic interpretable machine learning methods
  • Understand and utilize support vector machines
  • Utilize R packages for machine learning.
  • Understand and evaluate scientific papers covering basic machine learning applications to empirical data.

Seminar Prerequisites:


  • Advanced proficiency in linear regression, including the estimation of regression coefficients using least squares
  • Intermediate proficiency with R
  • Intermediate knowledge of exploratory data analysis
  • Basic familiarity with iterative optimization (e.g. Newton-Raphson algorithm to find a maximum)

Not required but advantageous:

  • Experience in calculus (e.g., graduate-level course)
  • Understanding the relation between multiple testing and Type I error

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Machine learning methods
  • More advanced mathematical or statistical topics such as constrained estimation using Laplace multipliers

Software and Computer Support:

Participants need to bring a laptop computer with Wi-Fi capabilities.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

All instruction for this course will be based on the freely available software program R. Please make sure to have a recent version installed.

Seminar Audience:

Typically the ideal audience for this course in data mining and machine learning includes:

  1. Students pursuing a degree in computer science, engineering, statistics, or related fields who have a strong background in mathematics and programming.
  2. Researchers and professionals in the fields of data science, data analysis, artificial intelligence, and machine learning who want to learn new techniques and keep up with the latest developments in the field.
  3. Data analysts and data engineers who are interested in learning how to extract insights from large datasets using machine learning algorithms.
  4. Business professionals who are interested in understanding how data mining and machine learning can be applied to solve real-world business problems.
  5. Anyone who wants to gain a deeper understanding of the techniques and algorithms used in data mining and machine learning, and their applications in various fields.

The audience for a course in data mining and machine learning can be quite diverse, but typically consists of individuals with a strong background in quantitative analysis and a desire to apply machine learning techniques to real-world problems.

Course Learning Goals:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with empirical data, participants will be able to:

  • Understand some of the key differences and similarities between parametric modeling and data mining methods
  • Expand the acquired basic knowledge of several popular data mining methods and apply these methods to empirical data
  • Assess and interpret the results of empirical analyses through k-fold cross validation and computation of prediction errors
  • Utilize R packages for data mining
  • Understand and evaluate scientific papers covering data mining applications to empirical data

Seminar Files

Seminar files will be provided by the instructor on the first day of the seminar. You do not need to download anything prior to the event date. All materials will be provided during or after the class.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Monday June 12, 2023
9:00-9:30 Welcome and introductions
9:30-10:45 Simple and Multiple Linear Regression
10:45-11:00 Rest Break
11:00-12:30 Ridge Regression and Lasso
12:30-1:30 Rest Break
1:30-3:00 Prediction Error and Cross Validation
3:00-3:15 Rest Break
3:15-5:00 R lab: Ridge Regression and Lasso
Tuesday June 13, 2023
9:00-10:45 Regression Splines
10:45-11:00 Rest Break
11:00-12:30 R lab: Regression Splines
12:30-1:30 Rest Break
1:30-3:00 Introduction to Tree Methods
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Wednesday June 14, 2023
9:00-10:45 CART, bagging, Random Forests
10:45-11:00 Rest Break
11:00-12:30 R lab: Random Forests
12:30-1:30 Rest Break
1:30-3:00 Boosted Trees
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Thursday June 15, 2023
9:00-10:45 R lab: Boosted Trees
10:45-11:00 Rest Break
11:00-12:30 Interpretable Machine Learning (IML)
12:30-1:30 Rest Break
1:30-3:00 Deductive Data Mining
3:00-3:15 Rest Break
3:15-5:00 Individual consultation with instructor
Friday June 16, 2023
9:00-10:45 Support Vector Machines
10:45-11:00 Rest Break
11:00-12:30 R lab: Support Vector Machines
12:30-1:30 Rest Break
1:30-3:15 Individual consultation with instructor

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