
Explore Matplotlib, Seabourne and Plomley to compare their pros and cons, and see how each enables quick, powerful visualizations with minimal code for storytelling.
Learn to identify and handle NaNs in data frames by applying methods such as filling, dropping, mean imputation, and forward or backward propagation to prepare data for visualization.
Convert data types in a data frame using as type, check dtypes, and ensure compatible formats for visualization with pandas and numpy, preventing errors in Python projects.
Learn to build simple visualizations with Matplotlib in Python, using figures and axes, plotting data on x and y coordinates, including 3D plots and object-oriented and pyplot workflows.
Use matplotlib to create a scatterplot of year, day, and length to reveal patterns in bitcoin ransomware data from the UCI dataset, using pandas to handle first hundred thousand rows.
Learn to use Seaborn's catplot with the tips data set to visualize day versus total bill. See Seaborn's architecture on matplotlib and its built-in data sets.
Plot the closing prices of real stock data using seaborn line plot, fetching data with pandas data reader from Yahoo for Google, Microsoft, Apple, and Tesla.
Learn to clean data and create a Seaborn joint plot of city development index versus training hours, colored by company type, using kde and univariate/bivariate insights.
Build data visualization with python using Plotly express chloroplasts Mapbox to plot May 2020 employment by county with FIPS codes. Learn to check NaNs and duplicates and adjust color scale.
Understanding our data is key to our success, whether it’s for analytical purposes or for our model building in AI/ML/DS or related domains. Moreover, being able to construct a captivating visualization to clearly help explain findings to teams, managers, stakeholders and more is a valuable skill necessary for the world of DS.
And in a world where presenting data is the new big thing, data visualization tools are a must in your data science toolkit. By building visualizations in the most popular visualization libraries, we can gain a deeper level of understanding and create mesmerizing presentations.
Hands-on Data Visualization With Python will help present the core concepts and structure of working with Matplotlib, Seaborn, and Plotly so that you will be able to impress even the toughest managers with your ability to draw insights from data.
And once you're done with the course you'll be able to understand the most important components of data visualization after constructing various types of graphs, and solving practical challenges. This will allow you to stand out in your careers, build tools that can help you obtain new insights, explain data or findings clearly, create interactive visualizations and animations and more.
And if that´s not enough, you'll also become more familiar with some of the most used visualization libraries, and help practice Python programming in the meantime!
So, are you ready to take your career onto the next level? Enroll now!