Statistical Modeling for Data Analysis

Apply statistical modeling for problems where machine learning doesn’t fit. Probability distributions, statistical metrics, sampling, and inference cover PDF/PMF, the central limit theorem,...

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$1,665USD
Duration 2 days
Course Code DS-2202
Available Formats Classroom

Overview

Course Description

Apply statistical modeling for problems where machine learning doesn’t fit. Probability distributions, statistical metrics, sampling, and inference cover PDF/PMF, the central limit theorem, descriptive and inferential statistics, confidence intervals, hypothesis testing, and inference errors. Linear and logistic regression handle estimation, model evaluation, and practical forecasting. Monte Carlo and Bayesian methods quantify uncertainty through simulation, priors, updating, MCMC, and Bayesian regression. Together these techniques fit regulated industries, limited-data teams, and explainability-sensitive analyses where rigorous statistics outperform machine learning. Hands-on labs produce statistical reports, regression models with quantified uncertainty, Monte Carlo simulations, and Bayesian analyses on realistic datasets across forecasting, hypothesis-testing, and uncertainty-quantification scenarios. The course is designed for data scientists, analysts, and developers with Python and DS-1201 experience.

Skills Gained

By the end of this course, participants will be able to:

  • Apply probability distributions for solid statistical analysis
  • Calculate relationships between variables to identify key drivers
  • Build regression models to optimize processes and forecast outcomes
  • Apply Monte Carlo and Bayesian methods to quantify uncertainty

Who Can Benefit

This course is designed for:

  • Data Scientists & Analysts
  • Software Developers

Prerequisites

Participants should enter this course with:

  • Practical Python Experience
  • DS-1201 or equivalent

Organizational Objectives

This course assists organizations to:

  • Lower regulatory risk through statistically defensible analyses in regulated industries
  • Improve forecasting accuracy through deliberate Monte Carlo and Bayesian techniques
  • Build a working statistical-modeling discipline across data-science teams
  • Establish auditable analyses through deliberate hypothesis testing and confidence intervals

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Module 1 - Understanding Probability Distributions

By the end of this module, you will be able to articulate probability distributions and their types, apply PDF and PMF, use the central limit theorem, estimate distributions from data, and apply distributions to data-science problems.

  • Introduction to probability distributions
  • Types and properties of probability distributions
  • PDF vs. PMF
  • Central limit theorem and law of large numbers
  • Estimating probability distributions from data
  • Applications of probability distributions in data science
  • Hands-on Lab: Generate and visualize continuous and discrete probability distributions.

Module 2 - Statistical Metrics and Interpretation

By the end of this module, you will be able to articulate descriptive and inferential statistics, compute statistical metrics in Python, and apply them to data-science problems.

  • Introduction to Statistical Metrics
  • Descriptive Statistics: Summarizing Data
  • Inferential Statistics: Making Predictions from Data
  • Statistical Metrics in Python
  • Practical Applications of Statistical Metrics in Data Science
  • Hands-on Lab: Calculate descriptive statistics and run correlation and regression analysis using Python.

Module 3 - Sampling and Statistical Inference

By the end of this module, you will be able to apply sampling methods, work with sampling distributions and the central limit theorem, build confidence intervals, run hypothesis tests, and recognize errors in statistical inference.

  • Sampling and statistical inference fundamentals
  • Sampling methods and the sampling distribution
  • Central limit theorem in inference
  • Confidence intervals and estimation
  • Hypothesis testing and inference errors
  • Practical applications of sampling and inference
  • Hands-on Lab: Perform Simulating Sampling Distribution and Confidence Interval Calculation # TODO: legacy had 2 labs; consolidate or split module.

Module 4 - Linear and Logistic Regression

By the end of this module, you will be able to apply linear and logistic regression, estimate parameters, evaluate regression models, and apply regression to practical data-science problems.

  • Introduction to Regression Analysis
  • Linear Regression
  • Logistic Regression
  • Estimation in Regression
  • Model Evaluation in Regression
  • Practical Applications of Linear and Logistic Regression
  • Hands-on Lab: Predict response with simple linear regression and run logistic regression for binary classification.

Module 5 - Monte Carlo Methods

By the end of this module, you will be able to apply Monte Carlo methods, walk the steps in a Monte Carlo simulation, recognize their applications, implement Monte Carlo in Python, and reason about advantages and limitations.

  • Introduction to Monte Carlo Methods
  • Core Concepts of Monte Carlo Methods
  • Steps in a Monte Carlo Simulation
  • Applications of Monte Carlo Methods
  • Implementing Monte Carlo Methods in Python
  • Advantages and Limitations of Monte Carlo Methods
  • Hands-on Lab: Estimate Pi via Monte Carlo simulation and run Monte Carlo integration on a hard problem.

Module 6 - Bayesian Statistics and Inference

By the end of this module, you will be able to apply Bayes’ theorem, choose priors, perform Bayesian updating and posterior estimation, run Bayesian hypothesis testing, apply MCMC methods, and connect Bayesian regression to machine learning.

  • Bayes’ theorem and Bayesian inference foundations
  • Components of Bayesian inference and choosing priors
  • Bayesian updating and posterior estimation
  • Bayesian hypothesis testing and model selection
  • Markov Chain Monte Carlo (MCMC) methods
  • Bayesian regression and machine learning
  • Hands-on Lab: Apply Bayesian updating with a Beta-Binomial model and run Bayesian linear regression with PyMC3.

Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

the class/lecture was amazing and very easy to understand and was in detail.

Great class I learned a great deal from the material. There would seem to a large amount that I need to learn about.

I like their training. A lot of material covered. The labs are very good. l learned a lot.

Overall ExitCertified is a great training provider and the remote learning is as effective as in person.

I was very pleased with the course setup by ExitCertified and the instructor.