Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

• 1. Migrating to R 2. Input and Output 3. Data Structures 4. Merging and Reshaping Datasets 5. Working with Dates and Strings 6. Working with Tabular Data 7. Working with Numerical Data 8. Graphics and Data Visualization 9. Probability Distributions 10. Tests of Differences 11. Tests of Relationships 12. Modern Robust Statistics 13. Writing Functions 14. Working with Financial Data 15. Dealing with Big Data 16. Introduction to Text and Data Mining

Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

• 1. Migrating to R 2. Input and Output 3. Data Structures 4. Merging and Reshaping Datasets 5. Working with Dates and Strings 6. Working with Tabular Data 7. Working with Numerical Data 8. Graphics and Data Visualization 9. Probability Distributions 10. Tests of Differences 11. Tests of Relationships 12. Modern Robust Statistics 13. Writing Functions 14. Working with Financial Data 15. Dealing with Big Data 16. Introduction to Text and Data Mining

Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

• 1. Migrating to R 2. Input and Output 3. Data Structures 4. Merging and Reshaping Datasets 5. Working with Dates and Strings 6. Working with Tabular Data 7. Working with Numerical Data 8. Graphics and Data Visualization 9. Probability Distributions 10. Tests of Differences 11. Tests of Relationships 12. Modern Robust Statistics 13. Writing Functions 14. Working with Financial Data 15. Dealing with Big Data 16. Introduction to Text and Data Mining