Data analytics with R
In an increasingly data-driven world, data analysis is becoming one of the hottest tech skills to have that will get you hired.
R is a widely used programming language amongst statisticians and data miners and regarded as one of the first data science programming language to learn.
Register for our Data Analytics with R and master the basics of R and be able to conduct data analysis project yourself within 5 sessions.
Through this course, you will:
- Learn basics syntax, most popular functions/libraries in R programming
- how to perform data analysis with R
- create your own visualisation and presentations with R
The track will culminate with a final project where all participants will apply what they have learnt and present their findings in the final 5th session of the Data Analytics with R. Outstanding presentations will be published on CodingGirls official website, slideshare platforms and promoted through various social media channels.
*This course is suitable for beginners. No programming background is required.
Chen Dacheng obtained his PhD in Statistics and Applied Probability from NUS. He uses R for his daily research.
Pang Long graduated from NUS and is currently a research assistant at the NUS School of Public Health. He is a trained computational biologist and biostatistician. Pang Long uses R extensively for his healthcare related research.
Date and Time:
Session 1: March 11th, Sat, 2-5:30 pm
Session 2: March 19th, Sun, 2-5:30 pm (Kindly take note. This session is on Sunday.)
Session 3: March 25th, Sat, 2-5:30 pm
Session 4: April 1st, Sat, 2-5:30 pm
Session 5: April 8th, Sat, 2-5:30 pm
Venue: National Design Centre, IDA Lab #03-04
Note: You are required to bring your own laptop
Session 1 (Mar 11th, Sat, 2-5:30pm)
Part I. R language
· Basic Syntax
· Data Types
· Data Structures
· Arithmetic Operators
· Logical Operators
Part II Data Handling
· Input and Output
· Data Cleaning
· Make Function (Hands-on example)
· Built-in Function (Hands-on example)
Session 2 (Mar 19th, Sun, 2-5:30pm)
Part I. Regression Models
· Simple Linear Regression (Hands-on example)
· Multiple Linear Regression (Hands-on example)
· Logistic Linear Regression (Hands-on example)
Part II. Graphics
· Basic Visualization Functions
· Higher Level Package (eg ggplot2)
Session 3 (Mar 25th, Sat, 2-5:30pm)
Data Engineering -Practical Project A
Exploratory data analysis
Regression analysis with plotting
Session 4 (April 1st, Sat, 2-5:30pm)
Advanced Data Science Project B
Session 5 (April 8th, Sat, 2-5:30pm)
Student project presentation & graduation
Add to Calendar:
Check out our website (www.codingirls.org) and follow our FB for more info: CodingGirls SG. Or you may send email to: firstname.lastname@example.org
We understand that there may be special issues occur, however, we can only refund to you if you cancel the courses 3 days before the starting the track.Seek for your understanding, and kindly check your availability before the registration.