Data Visualization with Python: The Complete Guide

Data is becoming a force to reckon with. With the amount of data that is being generated every minute, dealing with data has become more important. The importance of data lies in the fact that it allows us to look at our history and predict the future. Data science is the field that deals with collecting, sorting, organizing and analyzing huge amounts of data. This data is then used to understand the current and future trends. This field borrows techniques and theories from across multiple fields such as mathematics, statistics, computer science, information science, etc. It also aids other domains such as machine learning, data mining, databases and visualization.  

Data scientists are gaining importance and are also earning higher salaries, which means this is the right time to become a data scientist. While, it might seem easy, sorting data, these scientists are responsible for writing important algorithms and programs to help sort and analyze the data – and this isn’t an easy task.  

The course will cover a number of different concepts such as an introduction to data science including concepts such as linear algebra, probability and statistics, Matplotlib, charts and graphs, data analysis, visualization of non-uniform data, hypothesis and gradient descent, data clustering and so much more. That’s not all, we’ll also include projects to help you show exactly how to build visuals using Python. You can learn all this and tons of interesting stuff in this unique data science course. Enroll now and start building next generation interfaces for your data.  

Target Audience 

Beginner python developers who want to learn Data science will find this course very useful.  

Business Outcomes 

• This course will help you understand the importance of data science, along with becoming familiar with Matplotlib, Python’s very own visualization library.  
• Learn about the linear general statistics and data analysis.  
• We’ll also go over important concepts such as data clustering, hypothesis gradient descent, and advanced data visualizations.