Description:
This 11-video course explores Seaborn, a Python library used in data science to provide a high-level interface for drawing graphs that conveys both a lot of information, and are visually appealing. Seaborn also provides support for other data analysis and statistical libraries, such as SciPy and StatsModels. To take this course, learners should be comfortable programming in Python, have some experience using Seaborn for basic plots and visualizations, and should be familiar with plotting distributions, as well as simple regression plots. You will work with continuous variables to modify plots, and to put it into a context that can be shared. Next, learn how to plot categorical variables by using box plots, violin plots, swarm plots, and FacetGrids (lattice or trellis plotting). You will learn to plot a grid of graphs for each category of your data. Learners will explore Seaborn standard aesthetic configurations, including the color palette, and style elements. Finally, this course teaches learners how to tweak displayed data to convey more information from the graphs.
Target Audience:
Duration: 01:04
Description:
Explore Seaborn, a Python library used in data science to provide a high-level interface for drawing graphs that convey both a lot of information, and are visually appealing, in this 11-video course. To take this course, learners should be comfortable programming in Python and using Jupyter notebooks; familiarity with Pandas for Numpy would be helpful, but is not required. The course explores how Seaborn provides higher-level abstractions over Python's Matplotlib, how it is tightly integrated with the PyData stack, and how it integrates with other data structure libraries such as NumPy and Pandas. You will learn to visualize the distribution of a single column of data in a Pandas DataFrame by using histograms and the kernel density estimation curve, and then slowly begin to customize the aesthetics of the plot. Next, learn to visualize bivariate distributions, which are data with two variables in the same plot, and see the various ways to do it in Seaborn. Finally, you will explore different ways to generate regression plots in Seaborn.
Target Audience:
Duration: 01:07
Description:
This 10-video course explores some of the advanced features of Pandas DataFrames, a Python software library used for data manipulation and analysis. To take this Skillsoft Aspire course, learners should have some previous experience using Pandas, be able to load data into data frames, and be able to perform basic data manipulations in a Jupyter Notebook. You will learn to iterate data in DataFrames. This course covers various ways to export data from a DataFrames to Excel files, JSON (Javascript Object Notation) files, and CSV (comma separated values) files. You will learn to sort the contents in DataFrames, and how to utilize different techniques to manage missing data. The course conducts an in-depth examination of using a multi-index to group data. You will learn to merge data residing in different data frames into a single frame by using join and concatenate operations. Finally, since there are some similarities between relational databases and Pandas, you will learn when and where to integrate data by using structured query language (SQL)-like operations.
Target Audience:
Duration: 00:45
Description:
This Skillsoft Aspire course explores how to use Pandas, a Python software library, to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames. For this 14-video course, learners do not need prior experience working with Pandas, but should be familiar with Python3, and Jupyter Notebooks as a development environment. These data structures simplify various tasks in data analysis. You will learn to define your own index for a Pandas.Series object. Learners will explore Pandas DataFrames, a two-dimensional data structure and containing rows and columns. You will also learn to create a Pandas DataFrames by loading data from a CSV (comma separated values) file. Next, explore how to add and remove data from an existing DataFrames, and how to analyze just a part of the DataFrames. This course examines how to reshape or reorient data, and to create a pivot table. Finally, you will learn to use the concept of multiIndexes, or hierarchical indexes, to represent multidimensional data in a two-dimensional DataFrames.
Target Audience:
Duration: 01:06
Description:
This 13-video course explores advanced operations in NumPy, a Python library, and covers array operations such as image manipulation, fancy indexing, views, and broadcasting. To take this Skillsoft Aspire course, you should already have basic experience with NumPy arrays, and be comfortable with array creation, indexing and slicing, and applying both aggregate and universal functions to your array data. You will learn about the several options available in NumPy to split arrays. You will learn how to use NumPy to work with digital images, which are multidimensional arrays. Next, learn to manipulate a color image, which is a three-dimensional array using NumPy, and to perform slicing operations to view sections of the image, and how to use SciPy package for image manipulation. Learners explore the concepts of both shallow copies and deep copies in NumPy. You will learn how to use masks, an array of index values, to access multiple elements of an array simultaneously, referred to as Sansi indexing. Finally, this course covers broadcasting to perform operations between mismatched arrays.
Target Audience:
Duration: 01:08
Description:
This Skillsoft Aspire course explores NumPy, a Python library used in data science and big data. NumPy provides a framework to express data in the form of arrays; it supplies several array-based operations, and is the fundamental building block for several other Python libraries. For this course, you will need to know basics of programming in Python3, and should also have some familiarity in working with Jupyter notebooks, a browser-based interactive development environment to execute and view results without having to run the entire application. Learners will create an array and explore some of the basic operations, mostly mathematical ones, which can be performed on NumPy arrays. Next, learn to modify NumPy arrays, and then learn more complex operations, such as indexing and slicing, universal functions, and reshaping arrays. You will examine universal functions, which are NumPy library functions, which operate on an element-by-element basis on NumPy arrays. Finally, you will explore various options which are available, to iterate through arrays in NumPy.
Target Audience:
Duration: 01:00