Programming for Data Analytics (using R and Python)

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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

My videos for this course

Links

Economics & Finance Dept | add later | add later