Causal Data Science with Directed Acyclic Graphs course gives an introduction to causal data science with directed acyclic graphs (DAG). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal reasoning. In addition, they open the possibility to completely automatize the causal inference task with the help of special identification algorithms.
The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. The courses is usually available at INR 2,299 on Udemy but you can click now to get 72% off and get Causal Data Science with Directed Acyclic Graphs for INR 449.
Who all can opt for this course?
- Candidates who are working as data analysts
- Candidates who are Economists
- Candidates working as IT professionals
- Individuals with an interest in machine learning
|Registration Link||Apply Now!|
|Price||INR 449 (|
|Student Enrollment||2,172 students|
|Instructor||Paul Hünermund https://www.linkedin.com/in/paulhünermund|
|Topics Covered||Causal Discovery, Structural Causal models, Causal Data Science process|
|Total Student Reviews||380|
- Data science and machine learning causal inference
- How directed acylic graphs are used (DAG)
- Most recent advancements in causal AI
|1.||Introduction (15 minutes)||Welcome|
|2.||Structural Causal Models, Interventions, and Graphs (01 hour 00 minutes)||Directed Acyclic Graphs|
|Structural Causal Models|
|3.||Causal Discovery (34 minutes)||Testable Implications of DAGs|
|The PC Algorithm|
|4.||Confounding Bias and Surrogate Experiments (01 hour 34 minutes)||Confounding Bias|
|R Examples 1|
|R Examples 2|
|5.||Recovering from Selection Bias (29 minutes)||Selection Bias|
|Recovering from Selelection Bias|
|6.||Transportability of Causal Knowledge Across Domains (57 minutes)||The Transportability Task|
|S-Admissibility and Do-Calculus|
|7.||Outro (04 minutes)||The Causal Data Science Process|
- Basic understanding of statistics and probability
- Basic programming knowledge would be helpful
Kai-Chuan Y. (5/5) : The motivating example is really appealing to makes the point. The Causal Discovery with non-parametric cases would be useful to included in the R example. I know the theories are important, but I would like to see how to deal with data driven methods in real examples we might face in practice.
- Tarashankar Bandyopadhyay (5/5) : Very good foundational course on Causal Data Science, with examples in R.
- Niklas Jahnsson (5/5) : Great course that explores an area that is not so well known at this point yet.
- Ariel Menezes de Almeida Júnior (5/5) : Great course! It teaches what I was expecting it to, but it’s way more understandable than what I thought it would be.
- Anonymized User (4/5) : The good thing is that there are quite a lot of references in order to be explored after the course.
- Banu Selin T. (2.5/5) : The double speed and 1.75 speed audio is very low quality. The regular speed is very slow. The instructors method is very old-school style academic and abstract, I think he can use some more real life examples to turn to more understable fashion.
- Mark M. (2/5) : Very boring and slow start. Example could have been done in 1/3 of the time. Instructor’s voice is droning on. Will probably not continue.
About the Author
The instructor of this course is Paul Hünermund who is a Professor for Business Economics. With 4.4 Instructor Rating and 380 Reviews on Udemy, he/she offers 1 Course and has taught 2,172 Students so far.
- At Copenhagen Business School, Paul Hünermund teaches strategy and innovation as an assistant professor
- Hünermund’s study examines how businesses might use cutting-edge machine learning and artificial intelligence technology to gain a competitive advantage
- Instructor’s research examines how managers might mitigate biases that may exist in corporate decision-making
- As a result, it illuminates the roots of successful business tactics in industries with intense technical rivalry and their implications for economic expansion and environmental sustainability
- Instructor’s research draws on theories from a variety of fields, including economics, corporate strategy, game theory, and psychology, to examine the factors that influence firm innovation activities and performance
- Additionally, it makes use of a range of techniques from the fields of causal inference, machine learning, and econometrics
- Dr. Hünermund’s research offers policymakers advice on how to create public R&D support programmes that are most effective
- Instructor has widely disseminated this information through consulting projects and keynote addresses to the OECD, the European Commission, and the German Federal Ministry of Research and Education
- Instructor is one of the co-founders of causalscience[dot]org, a website that promotes information exchange between the business world and academics on issues pertaining to causal data science
|Parameters||Causal Data Science with Directed Acyclic Graphs||Data Science: Deep Learning and Neural Networks in Python||Data Science: Natural Language Processing (NLP) in Python|
|Offers||INR 455 (||INR 455 (||INR 455 (|
|Duration||5 hours||12 hours||12 hours|
|Rating||4.4 /5||4.7 /5||4.5 /5|
|Instructors||Paul Hünermund||Lazy Programmer Inc.||Lazy Programmer Inc.|
|Register Here||Apply Now!||Apply Now!||Apply Now!|