Programming for Data Analytics (using R and Python)

All chapters

• DANL100: syllabus

------R for the first half semester ----------------------

•   Chapter #1: R installation,basics, value assignment
•   Chapter #2: Simple function, Input data
•   Chapter #3: Simple data manipulation
•   Chapter #4: R loops, if else, if else if
•   Chapter #5: Output data to an external file
•   Chapter #6: Data frame and list
•   Chapter #7: subset, combine data sets, and merge
•   Chapter #8: date variable
•   Chapter #9: Simple plots and graphs
•   Chapter #10: Matrix manipulation
•   Chapter #11: String manipulation
•   Chapter #12: Introduction to R packages
•   Chapter #13: Excel and R, reading SAS data sets
•   Chapter #14: Reading zip and binary files

------Python for the second half semester ----------------------

•   Chapter #15: Sources of data
•   Chapter #16: Various distributions and hypothesis tests
•   Chapter #17: Hypothesis,Durbin-Watson,Normality,Granger causality tests
•   Chapter #18: Linear models
•   Chapter #19: Monte Carlo Simulation
•   Chapter #20: SEM (Structural Equation Model)
•   Chapter #21: Supervisor learning
•   Chapter #22: Unsupervisor learning
•   Chapter #23: R package: rattle
•   Chapter #24: Predictive Analytics
•   Chapter #25: Term projects

Download and Install R

• Go to r-project.org =>Click Download under CRAN =>[Choose a mirror] =>[PC or Mac]=>Click "base"
• YouTube: Windows (6m39s) | Mac (13m21s)

My videos for this course

• chapter 15     : Effective rate | Stock | Bond's duration
• chap 18 & 19: Linear reg #1 | #2 | Multivariate
• chap 20 & 21: Durbin-Watson | Benford | Granger causality
• chap 22 & 23: Option theory (basics) | Black-Scholes-Merton
• chapter 30     : Event Study

Links

• Click here, click 'package', then choose.
• semTools : MASS, foreign, parallel, boot, Amelia, mice, GPArotation,mnormt, blavaan
• Random price movement