0.4 Schedule

is specifically designed for students who has no background of much Algorithms, programming and even computing. The basic requirements are desire to learn, attitude of humble and diligence of working. All that mean you need to get your hand dirty, spent time and do more practices. At the end, it cannot guaranty you become a data scientist but it will help you find the way to wards doing data science and be confident to start doing data science projects. It is certainty that if you persist on this road, you will no doubt becomes a future data scientist. Setup Download files required for the lesson 00:00 1. Analyzing Patient Data How do I read data into R? How do I assign variables? What is a data frame? How do I access subsets of a data frame? How do I calculate simple statistics like mean and median? Where can I get help? How can I plot my data? 00:45 2. Creating Functions How do I make a function? How can I test my functions? How should I document my code? 01:15 3. Analyzing Multiple Data Sets How can I do the same thing to multiple data sets? How do I write a for loop? 01:45 4. Making Choices How do I make choices using if and else statements? How do I compare values? How do I save my plots to a PDF file? 02:15 5. Command-Line Programs How do I write a command-line script? How do I read in arguments from the command-line? 02:45 6. Best Practices for Writing R Code How can I write R code that other people can understand and use? 02:55 7. Dynamic Reports with knitr How can I put my text, code, and results all in one document? How do I use knitr? How do I write in Markdown? 03:15 8. Making Packages in R How do I collect my code together so I can reuse it and share it? How do I make my own packages? 03:45 9. Introduction to RStudio How do I use the RStudio graphical user interface? 04:00 10. Addressing Data What are the different methods for accessing parts of a data frame? 04:20 11. Reading and Writing CSV Files How do I read data from a CSV file into R? How do I write data to a CSV file? 04:50 12. Understanding Factors How is categorical data represented in R? How do I work with factors? 05:10 13. Data Types and Structures What are the different data types in R? What are the different data structures in R? How do I access data within the various data structures? 05:55 14. The Call Stack What is the call stack, and how does R know what order to do things in? How does scope work in R? 06:10 15. Loops in R How can I do the same thing multiple times more efficiently in R? What is vectorization? Should I use a loop or an apply statement? 06:40 Finish