Course Outline
Day One: Language Basics
- Course introduction
-
About data science
- Defining data science
- The process of conducting data science
- Introducing the R language
- Variables and data types
- Control structures (loops and conditionals)
-
R scalars, vectors, and matrices
- Defining R vectors
- Matrices
-
String and text manipulation
- Character data type
- File I/O
- Lists
-
Functions
- Introducing functions
- Closures
- lapply/sapply functions
- DataFrames
- Practical labs for all sections
Day Two: Intermediate R Programming
- DataFrames and file I/O
- Reading data from files
- Data preparation
- Built-in datasets
-
Visualisation
- Graphics package
- plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat maps
- ggplot2 package (qplot(), ggplot())
- Exploration with dplyr
- Practical labs for all sections
Day Three: Advanced Programming With R
-
Statistical modelling with R
- Statistical functions
- Handling missing values (NA)
- Distributions (Binomial, Poisson, Normal)
-
Regression
- Introduction to linear regression
- Recommendations
- Text processing (tm package and word clouds)
-
Clustering
- Introduction to clustering
- K-Means
-
Classification
- Introduction to classification
- Naive Bayes
- Decision trees
- Training using the caret package
- Evaluating algorithms
-
R and big data
- Connecting R to databases
- The big data ecosystem
- Practical labs for all sections
Requirements
- A basic programming background is preferred
Setup
- A modern laptop
- Latest RStudio and R environment installed
Testimonials (7)
The real life applications using Statcan and CER as examples.
Matthew - Natural Resources Canada
Course - Data Analytics With R
His knowledge, and the codes were already written in the files so I could study after the classes and practice on my own.
GLORIA ADANNE - Natural Resources Canada
Course - Data Analytics With R
Lots of R coding provided and good examples
Kasia - Natural Resources Canada
Course - Data Analytics With R
Extensive language and well-developed. Also a wealth of supporting information available online.
Michel - Natural Resources Canada
Course - Data Analytics With R
I liked that the trainer made sure we all understood and were following the lectures. if we had a problem, he stopped and helped us fix it.
Cesar - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
- Teleperformance
Course - Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
