With Python Workshop
Conducting proper analysis and interpretation is vital in the endeavor to extract useful insights from data. In this course, we will extend beyond the methods of classical statistics and gain familiarity with contemporary methods for statistical inference driven by computational power. Students will gain experience with the general concepts behind Monte Carlo sampling, Bootstrapping, and Bayesian inference so that these methods may be applied to new problems.
What You Will Learn
Throughout the course, we will address example problems in hypothesis testing, regression, unsupervised clustering, topic modeling and time series analysis. In addition to implementing these concepts from scratch, students will gain familiarity with popular Python libraries for easily applying these methods on future problems.
The workshop will be an approximately 3 hour online webinar focused on demonstration-led topics and techniques.
District Data Labs online workshops are recorded for the benefit of the participants, and all code will be made available after the workshop.
The course will cover the following topics:
- Discussion of Classical Statistical Methods
- Monte Carlo Methods and Frequentist Inference
- Hypothesis Testing Using Monte Carlo Sampling
- Bootstrap Resampling
- Bayesian Methods and Posterior Inference
- Markov Chain Monte Carlo
- Gibbs Sampling
- Metropolis-Hastings Algorithm
- Unsupervised Clustering
After this course, students will have a conceptual understanding of contemporary statistical methods, as well as hands on experience implementing these methods from scratch on example problems. With this knowledge, students will be prepared to apply these methods to their own problems.
This advanced course is aimed at students who are already fully comfortable with common quantitative techniques.
- Comfort with Python (numpy, matplotlib, etc)
- Comfort with probability theory
- Comfort with classical statistical methods (hypothesis tests, p-values, regression, etc)
Instructor: Keegan Hines
Keegan Hines is a Data Scientist with IronNet Cybersecurity, focusing on large-scale machine learning applications in cyber defense.
He received a PhD from the University of Texas with a focus on computational statistics and neuroscience during which time he taught multiple seminars on statistical methods and R. He is interested in challenging problems in machine learning and distributed computing.
A RECORDING OF THIS ONLINE WORKSHOP IS AVAILABLE FOR PURCHASE.
CLICK THE BUTTON BELOW TO GET THE VIDEO.
REGULAR PRICE: $35