 Visualizing data with python- SkillUp Online

IBM Data Science Professional Certificate

Learn how to present data using key libraries in Python. Explore pie charts, waffle charts, scatter plots, and bubble plots, and discover how to visualize geospatial data. 

Get free access to IBM Watson Studio and develop sought-after skills for a data science career.

Data visualization plays an essential role in the representation of both small and large-scale data; a picture speaks a thousand words, as they say. One of the key skills of a data scientist is the ability to tell a compelling story through the visualization of data. Python is a programming language that is widely used to achieve this.In this course, you will learn how to present data using some of the data visualization libraries in Python, including Matplotlip, Seaborn, and Folium. You will discover how to use basic visualization tools such as pie charts, area plots, histograms, bar charts, box plots, scatter plots, and bubble plots. And you will explore creating waffle charts, word clouds, and regressions plots. You will also be introduced to creating maps and visualizing geospatial data.When you sign up for this course, you will get free access to IBM Watson Studio. In Watson Studio, you will discover how to create your own data science projects and collaborate with other data scientists.

Course Overview

This course comprises six purposely designed modules that take you on a carefully defined learning journey. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, review questions, hands-on labs, final assignment and final exam.Once you have successfully completed the course, you will earn your IBM Certificate.As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

How It Works

You will be able to:<ul><li>Create plots and visuals.</li><li>Do basic plotting with Matplotlib.</li><li>Generate different visualization tools using Matplotlib, such as line plots, area plots, histograms, bar charts, box plots, and pie charts.</li><li>Use Seaborn to create attractive statistical graphics.</li><li>Use Folium to create maps and visualize geospatial data.</li></ul>

Skills You Will Gain

<ul><li>Individuals who want to visualise different types of data</li><li>Individuals who want to gather better insights about their data</li><li>Individuals seeking to visualize data in basic, advanced and specialised forms of data visualisation techniques.</li></ul>

Who Should Enroll On This Program

We recommend that you have completed the following courses:<ul><li>Python for Data Science, AI & Development</li><li>Analyzing Data with Python</li></ul>

Prerequisites

 Visualizing data with python- SkillUp Online - SkillUp Online

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.

Is Python good for data visualization?

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.

Is Python enough to get a job in AI or data science?

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.

When will I be able to access the course materials?

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.

What does it mean to be self-paced?

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.

R or Python: which is better for data science?

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.

Q1 Is Python good for data visualization?

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.

Q2 Is Python enough to get a job in AI or data science?

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.

Q3 When will I be able to access the course materials?

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.

Q4 What does it mean to be self-paced?

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.