A course that builds a foundational understanding of Git and version control systems. By the end of the course students will be able to use key Git commands and interact with remote repositories like GitHub, Bitbucket, and GitLab. Students will also get acquainted with the concept of continuous integration and continuous deployment(CI/CD).

An introduction to graph databases and why they are so powerful as well as an overview of what you can use graph databases for.

Builds techniques for web-based data exploration and interactive-app development in Python using open-source Holoviz libraries (that is, HoloViews, HvPlot, Datashader, and Panel). These tools enable constructing rich high-performance, scalable, flexible, and deployable visualizations easily.

A course that introduces infrastructure as code (IaC) explains how to keep up with the industry standards.

Overviews Intake, a lightweight package for building and effectively using data catalogs with Python. Intake helps with finding, investigating, loading and disseminating data. Participants will learn the fundamentals of using Intake to connect with data sources in practical situations.

An introduction to integration testing and how to perform integration testing using Python.

In this course, learners will use R packages to create charts and maps with interactive elements. Tooltips, hover states, and other dynamic elements allow for the encoding of additional layers of data to enrich your data visualizations. Learners will build basic interactive visualizations using the Highcharter package before moving on to more advanced chart types. Learners will also work with more complex data to create maps and network graphs, exportable as HTML widgets.

Plotly is a powerful open-source graphic library for Python that allows users to create interactive visualizations. In this course, learners will discuss the advantages of adding additional layers of data to a visualization through dynamic elements. Learners will then learn how to connect Plotly to the data transformation library Pandas using Cufflinks. Finally, learners will generate interactive bar charts, box plots, scatter plots, and other commonly used formats.

Plotly is a powerful open-source graphic library for Python that allows users to create interactive visualizations. In this course, learners will discuss the advantages of adding additional layers of data to a visualization through dynamic elements. Learners will then learn how to connect Plotly to the data transformation library Pandas using Cufflinks. Finally, learners will generate interactive bar charts, box plots, scatter plots, and other commonly used formats.

This course is designed for students looking to deepen their understanding of creating visualizations and interpreting data in Tableau. Students discuss how Tableau aggregates data and then practice visualizing data using complex calculations. By the end of the course, students will be able to incorporate Tableau’s auto- generated fields in their visualizations, gauge users’ needs when developing Tableau-based products.

In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using Python. The first method, hierarchical clustering, creates tree branch-based clusters in order of increasing specificity. The second, density-based clustering, creates groups based on the concentration of data points within a region. By the end of this course, learners will prepare data for, implement, and optimize these models, and compare their relative advantages.

In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using R. The first method, hierarchical clustering, creates tree branch-based clusters in order of increasing specificity. The second, density-based clustering, creates groups based on the concentration of data points within a region. By the end of this course, learners will prepare data for, implement, and optimize these models, and compare their relative advantages.

This is an intermediate network analytics course. By the end of this course, students will be able to quantitatively measure and visualize network nodes with scores. They will also summarize their findings by testing their network resilience.

Detecting outlier data points are powerful machine learning techniques. This course covers how techniques like Local Outlier Factor and Isolation Forest play a role in anomaly and outlier detection. By the end of the course, students will learn to implement these techniques to identify anomalous data points.

With a basic command of Python variables, learners can begin writing modular code to create and control the flow of a program. In this course, learners will recognize and incorporate conditional statements, for loops, while loops, and list comprehensions into their programs in order to sequence and limit the scope of their programs. Learners will also practice defining functions, blocks of code that interact with data in specific ways.

This course familiarizes learners with key concepts in programming essential for writing code in base R. After loading a dataset into their environment, learners will create variables to represent values that change according to specific conditions. Learners will also construct conditional statements, loops, and functions to practice iterating over these variables with the basic building blocks of a simple program.

This course deepens learners understanding of using SQL to store and retrieve data. Building on their knowledge of basic calls, learners will perform more complex operations by expanding their knowledge of SQL syntax. By the end of this program, students will be able to handle querying multiple data tables at once, join multiple tables, and optimize SQL queries to discover new insights.

This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of intermediate statistics topics like t-values, t-distributions, chi-square distributions, f-statistic, and f-distributions that enable us to compare observed and expected frequencies objectively.

This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of intermediate statistics topics like t-value, t-distribution, chi-square distribution, f-statistic, and f-distribution that enable us to compare observed and expected frequencies objectively.

An introductory course that provides a foundational understanding of how the Internet works, including the key components of web sites, client-server communication protocols, and various web browsers.