PhD., Data Scientist
"A picture is worth a thousand words." We are all familiar with this expression. It especially applies when trying to explain the insights 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.
In this course, you will learn how to leverage a software tool to visualize data that will also enable you to extract information, better understand the data, and make more effective decisions.
When you sign up for this course, you get free access to IBM Watson Studio. In Watson Studio, you’ll be able to start creating your own data science projects and collaborating with other data scientists. Start now and take advantage of everything this platform has to offer!
Module 1 -Introduction to Visualization Tools
Module 2 -Basic Visualization Tools
Module 3 -Specialized Visualization Tools
Module 4 -Advanced Visualization Tools
Module 5 -Creating Maps and Visualizing Geospatial Data
Module 6 -Creating Dashboards with Plotly and Dash
PhD., Data Scientist
Earn your certificate
Once you have completed this course, you will earn your certificate.
Python is a widely used programming language for data visualization. It has a useful set of libraries for creating informative and helpful bar charts, scatterplots, line charts, and geographical maps, among other things. These packages make it simple to generate visually appealing displays of data analysis results. Matplotlib, Plotly, Seaborn, GGplot, and Geoplotlib are just a few examples of these libraries, although there are many more.
No, is the short answer. Though knowing Python is essential for pursuing a career in AI or data science, you will also require additional talents.
Data Visualization with Python is a self-paced online course. As a result, you will require internet connectivity in order to use the course materials. When you register for this course, you will immediately have access to the course materials through the course link on your dashboard.
There is no defined schedule for live sessions or webinars in self-paced courses. You can work as swiftly or as slowly as you wish as long as you complete the modules and the course before the deadline.
In the realm of data science, this is a contentious topic. Both languages are valuable for data science, according to IBM, and each has its own benefits and disadvantages. Both languages are widely used in data science because they are suitable for a wide range of activities. These can include everything from data manipulation to big data analysis. Their disparities can thus be best understood by looking at how each one came to be. Python is a general-purpose computer language that was first developed in 1989. R, on the other hand, grew out of statistical analysis and is hence incredibly strong but more difficult to use.