|Course Title||R Programming Language – Level 2|
|Gouvernement du Québec fee (taxes incl.)||$60.00|
|General public fee (taxes incl.)||$492.55|
|Prerequisites||A college level understanding of Math and Linear Algebra. Basic concepts on algorithm design. Familiarity with R syntax, data structures and functions – R Programming level 1.|
|Target audience||Software developers, data analysts, professionals in any area|
|Instructor||Diego Perea PhD|
|NB: This is a non-credit course. Certificate provided for all participants who complete 80% of course hours.|
Please note that this is a non-credit course.
| After the introductory course, we dive into the use of R for data analysis to solve real data problems. Data normally resides in large data bases or repositories, in the first part of the course, we learn how to connect R to databases and apply R data analysis algorithms. We make use of R modular functionality and packages to produce analysis and reports. Then, these reports are exported to Tableau to produce interactive dashboards.
In the second part of the course, we move from descriptive data analytics to predictive data analytics, where we make use of r regression and classification algorithms to infer new data. Then, we produce Tableau dashboards to showcase the predicted results. At the end of the course, participants will be comfortable using R for their descriptive and predictive data analytics projects in their companies.
|Topics to be covered include:|
Please note that the instructor reserves the right to modify this schedule.
|Week 1||Topic 1 and 2
Advance data analysis methods in R and connecting databases and repositories to R
|Week 2||Topic 3
Descriptive data analytics reports
|Week 3||Topic 4
Data regression and classifications in R. Predictive data analytics
|Week 4||Topic 5
Predictive data analytics dashboards
|Week 5||Topic 6
Predictive analytics in R
SOFTWARE TO BE USED
For the course, we will mainly use R, which is the industry standard for statistical learning and provides functions for most of the methods. We will complement R processing capabilities with Tableau visual reporting capabilities. Tableau is a popular and friendly BI and reporting tool widely used in the industry.
LABS and DATASETS
In the labs, the participant will apply the concepts and methods seen in class using practical datasets. Among others, we will use the following datasets:
- Uber trip data: trip information including Uber service type, source, destination, distance, duration and paid fare. Continuous regression methods are applied to estimate the trip fare based on trip distance, time an service demand.
- Advertisement data: dataset containing the budget spent on advertisement by a company on different markets. Continuous variable regression methods help design the best advertisement plan to maximize profit.
- Automobile features data: dataset containing car characteristics to develop a model that predicts whether a car gets high or low gas mileage.
In addition to the previous datasets, participants are encourage to bring in their own data. The following are some website with good data sources.