Professor(s)
Notes
In 2006, Clive Humby, a British mathematician, coined the sentence “Data is the new oil.” Lots of data is gathered every second, and we are all adapting to the amount becoming available every day. In different fields, people still struggle to find the link between the overwhelming data and our capacity to integrate it into the decision-making process. This course focus on how to properly explore, analyse, and present data to the world.
A quick online search shows that the 5 top skills requested by employers in general are communication, problem solving, critical thinking, creativity and analytical skills. We will incorporate them all in this class. In data analytics, the top skills requested are: databasing, business intelligence tools, Excel, and programming in Python/R.
This course is an introduction to data visualization and will help students develop the numerical skills demanded by today’s employment market:
• Data cleaning and mining,
• Advanced Microsoft Excel (without macros),
• Business intelligence (BI) tools: the popular TABLEAU,
• Visual and oral data communication practice.
This not a course on statistics, although statistical practices will be performed to transform data into a useful form. Nor is this a data science class, since you are not expected to produce inference on your data. Rather, this is an analytics class in which we will explore, synthesize, and present data in a form legible to the general public.
Learning Outcomes
- Students will appreciate the use of mathematics in modeling the world.
- Students will be able to clearly communicate quantitative information in words, in numbers and with graphs.
- Students will develop a positive approach to mathematics.
- Students will develop strategies for solving problems.
- Students will have familiarity and fluency in using a statistical software package.
- Students will be able to reason with quantitative information - in words, numbers and graphs and charts.
- Students will be able to take into account uncertainty and ethics applied to statistics.
- Students will use descriptive statistics to describe samples, populations, and relationships between variables (independence, regression).
- Students will use techniques of inferential statistics appropriately (confidence intervals, hypothesis tests for proportions, means, chi-squared tests and linear regression).
Syllabus
Book List
Title | Author | Publisher | ISBN Number |
---|---|---|---|
Visualization Analysis and Design | Tamara Munzner | HACHETTE | 9781466508910 |
Storytelling with Data: Let's Practice! | Cole Nussbaumer Knaflic | Wiley | 9781119621492 |
Schedule
Day | Start Time | End Time | Room |
---|---|---|---|
Monday | 10:35 | 11:55 | PL-5 |
Wednesday | 10:35 | 11:55 | PL-5 |
Thursday | 10:35 | 11:55 | PL-5 |