Enroll in Data Visualization with Python course and learn how to make data more meaningful and present that data in a form that makes sense to people using several data visualization libraries.

Data Visualization with Python Course

Data Visualization with Python - SkillUp Online

Explore how to present data using Python libraries. Learn how to create meaningful pie charts, waffle charts, scatter plots, and bubble plots, and discover how to visualize geospatial data.
 
Enjoy free access to IBM Watson Studio and develop valuable skills for a rewarding career in data science.

Visualizing data effectively is essential for communicating the insights discovered in both small and large-scale data sets. Data scientists need to be able to tell a compelling story through the visualization of data to engage business stakeholders. Python is a programming language that is often used to achieve this. In this course, you will explore how to present data using some of the data visualization libraries in Python. These include Matplotlip, Seaborn, and Folium. You will learn how to use basic visualization tools such as pie charts, area plots, histograms, bar charts, box plots, scatter plots, and bubble plots. Plus, you will create waffle charts, word clouds, and regressions plots, and be introduced to creating maps and visualizing geospatial data. When you enroll 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

Data Visualization with Python comprises five 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 Applied Data Science with Python 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 2-3 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 don't worry, you are 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, a final assignment and a 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

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

Who Should Enroll On This Course

After completing this course, 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

We recommend that you have completed the following courses: <ul> <li>Python 101</li> <li>Data Analysis with Python</li> </ul>

Prerequisites

Data Visualization with Python Course - SkillUp Online

Python is a very popular language for creating data visualizations. It offers a fantastic suite of libraries for building insightful and useful bar charts, scatterplots, line charts, and geographical maps etc. These libraries make it really easy to create engaging visual representations of the results of data analysis. Good examples of these libraries include Matplotlib, Plotly, Seaborn, GGplot, and Geoplotlib, although there are many more.

Is Python good for data visualization?

The short answer is no. Though learning Python is critical for developing a career in AI or data science, you will still need other skills if you wish to get a job in AI or data science. To give you a little background, for example, there is a constant buzzing debate about whether R or Python is best in data science and AI. IBM believes both languages are important, for they each have their strengths and weaknesses. However interestingly, they also have their similarities. Where Python is considered a general-purpose programming language, R has grown directly from statistical analysis. The question that experienced data scientists and those who work in AI are asking, therefore, is not which is best for data science or AI, but when is it best to use each language. We recommend you read this article written by IBM if you like to find out more: 'https: //www.ibm.com/cloud/blog/python-vs-r'

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

This is a hot subject for debate in the world of data science. IBM considers the topic very well in a page dedicated to the subject (see link below). From IBM's perspective, both languages are valuable for data science, and each brings its own strengths and weaknesses. Both languages are popular in data science because they each work well for many data science tasks. These can range from data manipulation to big data exploration. Their differences can be best understood, therefore, through consideration of how each one has come into existence. Python came into being around 1989 and is considered a general-purpose programming language. However, R has grown directly from statistical analysis, and is therefore extremely powerful but more complex to use. For more information on this, we recommend you read this article written by IBM

Which is better for data science, R or Python?

Data Visualization with Python is a course that runs online. In order to be able to use the course materials, therefore, you will need access to the internet. When you enroll for this course, you will be able to access the course materials via the course link in your dashboard immediately.

When will I have access to the course materials?

Self-paced courses are not run to a set schedule of live sessions or webinars. As long as you work through the modules and complete the course before the end date, you can work as quickly or as slowly as you want; you can set your own pace.

What does self-paced mean?

Yes, Data Visualization with Python is a mentored course. 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.

Is Data Visualization with Python a mentored course?

Python is a very popular language for creating data visualizations. It offers a fantastic suite of libraries for building insightful and useful bar charts, scatterplots, line charts, and geographical maps etc. These libraries make it really easy to create engaging visual representations of the results of data analysis. Good examples of these libraries include Matplotlib, Plotly, Seaborn, GGplot, and Geoplotlib, although there are many more.

Q1 Is Python good for data visualization?

The short answer is no. Though learning Python is critical for developing a career in AI or data science, you will still need other skills if you wish to get a job in AI or data science. To give you a little background, for example, there is a constant buzzing debate about whether R or Python is best in data science and AI. IBM believes both languages are important, for they each have their strengths and weaknesses. However interestingly, they also have their similarities. Where Python is considered a general-purpose programming language, R has grown directly from statistical analysis. The question that experienced data scientists and those who work in AI are asking, therefore, is not which is best for data science or AI, but when is it best to use each language. We recommend you read this article written by IBM if you like to find out more: 'https: //www.ibm.com/cloud/blog/python-vs-r' 

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

This is a hot subject for debate in the world of data science. IBM considers the topic very well in a page dedicated to the subject (see link below). From IBM's perspective, both languages are valuable for data science, and each brings its own strengths and weaknesses. Both languages are popular in data science because they each work well for many data science tasks. These can range from data manipulation to big data exploration. Their differences can be best understood, therefore, through consideration of how each one has come into existence. Python came into being around 1989 and is considered a general-purpose programming language. However, R has grown directly from statistical analysis, and is therefore extremely powerful but more complex to use. For more information on this, we recommend you read this article written by IBM 

Q3 Which is better for data science, R or Python?

Data Visualization with Python is a course that runs online. In order to be able to use the course materials, therefore, you will need access to the internet. When you enroll for this course, you will be able to access the course materials via the course link in your dashboard immediately.

Q4 When will I have access to the course materials?

Self-paced courses are not run to a set schedule of live sessions or webinars. As long as you work through the modules and complete the course before the end date, you can work as quickly or as slowly as you want; you can set your own pace. 

Q5 What does self-paced mean?

Yes, Data Visualization with Python is a mentored course. 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. 