About this Data Visualization Course
"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.IBM Watson Studio. When you sign up, you get free access to Watson Studio. Start now and take advantage of this platform.
You can start creating your own data science projects and collaborating with other data scientists using
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- This course is free.
- It is self-paced.
- It can be taken at any time.
- It can be audited as many times as you wish.
Alex Aklson, Ph.D., is a data scientist in the Digital Business Group at IBM Canada. Alex has been intensively involved in many exciting data science projects such as designing a smart system that could detect the onset of dementia in older adults using longitudinal trajectories of walking speed and home activity. Before joining IBM, Alex worked as a data scientist at Datascope Analytics, a data science consulting firm in Chicago, IL, where he designed solutions and products using a human-centred, data-driven approach. Alex received his Ph.D. in Biomedical Engineering from the University of Toronto.
Jay Rajasekharan started his career as a project engineer at Honeywell Aerospace, where he managed a portfolio of projects that focused on quality improvement, cost reduction, and process improvement for Boeing, Airbus, and Lockheed Martin. Subsequently, Jay made a switch from engineering to analytics and joined IBM as a business analyst under the Infrastructure Services division. Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost. Aside from his career, Jay is very passionate about teaching – he volunteers as a tutor at his community library and as an Alumni mentor at the University of Toronto.
Polong Lin is a Data Scientist and Lead Data Science Advocate at IBM in Canada. Polong co-organizes the largest data science meetup group in Canada, and regularly speaks at conferences about data science. Polong holds a M.Sc. in Cognitive Psychology.
Special thanks to the following engineers and analysts for their valuable and significant contribution to the hands-on component of this course: Ehsan M. Kermani, Slobodan Markovic, Susan Li, and Madeleine Shang.