Channel Title: Data Talks Channel Description: The channel goal is to teach people how to make data driven decisions, aka good decisions. I started by doing videos explaining tools that will help make your decisions including: seaborn for data visualization, patsy for feature engineering, stats models for causal inference (I might build better tools for this in the future), sklearn for machine learning and keras for deep learning. I'm currently on stage two where I explain the basic concepts (you only need middle school algebra to follow along) involved in data decision making including: statistical inference for talking about data you have never seen, machine learning for teaching computers how to find patterns in data for you and causal inference for learning what causes what. Stage three is a combination of practical lessons (data driven tips for making decisions) and much later a framework for making decisions. Throughout I will sprinkle conversations with my friends and other data scientists. <video> As If Random Description: We cover an important topic for IV analysis - or the last topic of the course </video> <video> IV Proof Description: We prove why IV analysis works </video> <video> Real Life IV Examples Description: We talk about three times when IV analysis was used in real studies </video> <video> Send Me Your Notebooks! Description: Comment down below with a github link to notebooks you'd like me to review! </video> <video> Parametric G Formula Description: We describe my favorite causal inference technique: the parametric G formula, my go-to for any standard observational causal inference problems </video> <video> Using GPT to learn Data Science Description: In this video I show how I use GPT to help with data science by going through one of my viewer's notebooks The notebook I'm going through: https://github.com/skeem1/From-Cafffeine-to-Cocaine/blob/main/coffee-bean-production.csv seaborn tutorials: https://www.youtube.com/watch?v=fWuPIGVPo7o&list=PLgJhDSE2ZLxYlhQx0UfVlnF1F7OWF-9rp&pp=gAQBiAQB Causal inference videos: https://www.youtube.com/watch?v=kE-agokfsHE&list=PLgJhDSE2ZLxaIAU_C1j0Cw70f2bQ2Sa6D&pp=gAQBiAQB </video> <video> Modeling Means Description: We do a quick primer of Linear Regression (a ML technique) to prepare us for our next ML base causal inference tool! </video> <video> 4 Types Of Patients Description: We talk about 4 actions patients can take when given a treatment. This servers as some framework for IV analysis. </video> <video> Other Causal Inference Tools Description: I talk about the most common tools in Causal Inf that I won't be covering: * IP Weighting * Outcome Regresssion * Propensity Scores * G-estimation </video> <video> Future Topics Description: Thanks so much for watching! Please comment below on what topics you'd like to see covered next! </video> <video> Model Misspecification Description: We talk about an added assumption of the parametric G formula </video> <video> IV Example Description: We go over an example of IV analysis </video> <video> Exchangeability In Observational Studies Description: Exchangeability in observational studies is possible without confounding or selection bias </video> <video> Instrumental Variables Description: We introduce Instrumental Variables </video> <video> 2 Problems With Standardization Description: Today we talk about two problems with standardization: combinatorial explosions and continuous variables. This sets us up nices for the next lecture where we talk about the solution to these problems. </video>

Contacts and Links

Description

The channel goal is to teach people how to make data driven decisions, aka good decisions.

I started by doing videos explaining tools that will help make your decisions including: seaborn for data visualization, patsy for feature engineering, stats models for causal inference (I might build better tools for this in the future), sklearn for machine learning and keras for deep learning.

I'm currently on stage two where I explain the basic concepts (you only need middle school algebra to follow along) involved in data decision making including: statistical inference for talking about data you have never seen, machine learning for teaching computers how to find patterns in data for you and causal inference for learning what causes what.

Stage three is a combination of practical lessons (data driven tips for making decisions) and much later a framework for making decisions.

Throughout I will sprinkle conversations with my friends and other data scientists.

Videos from channel