TECHNOLOGY APPLICATION MODULES

The Excel and SQL course is a complete Excel and SQL training course. Excel Lessons start at the basics including charting and cover advanced topics such as modeling business processes, and PivotTables. Each lesson includes instructional readings, lecture videos, practice problems, and an assignment to assess student learning.

This module walks you through the basics of spreadsheet automation through the Visual Basic for Applications programming language that underlies most Microsoft Office applications. The content starts from an assumption of zero programming experience and takes you all the way through recording basic macros and then writing dynamic and complex macros from scratch. What you learn in this module has the potential to save you and your company hundreds of hours and hundreds of thousands of dollars by automating repetitive tasks.

Web development is fundamental to every industry. Knowing how a website works will help you speak more intelligently with your tech team, as well as give you the skills to understand how your website and web apps work. In this module, we assume you have zero web development experience and we take you through the basics of HTML and CSS. We will wrap up with how to implement and customize professional templates and how to make your website live online.

This module will guide students with no prior background in coding through the basic building blocks of programming in the Python language using the Jupyter Notebook format. This format is ideal for students who need to learn Python for business programming tasks in data science, statistics, healthcare research, finance, accounting, marketing, supply chain, human resources, operations and others.

This module will guide students through a no-code solution for developing machine learning pipelines using Microsoft Azure Machine Learning Studio Designer. This end-to-end solution is ideal for learning the overall process in a single tool, including data collection, data cleaning, data modeling (including algorithm testing and feature selection), and model deployment.

This module will introduce students to the first step in the data mining process after data is acquired—exploratory data analysis. This includes univariate statistics (e.g., mean, median, mode, quartiles, skewness, kurtosis) and visualizations (histograms and box plots) as well as bivariate statistics (correlation, t-tests, one-way ANOVAs, chi-squared) and visualizations (scatterplots, barplots, heatmaps, and crosstabs). This module is ideal to prepare students for data cleaning and predictive modeling.

This module will teach students how to collect data from the web using Python. This includes both web scraping using the BeautifulSoup package and making requests from web services using the Requests package. In addition, students get a detailed dive into the Twitter API and learn to automate the collection of social media text, images, and videos.

This module will help students understand the primary data cleaning steps necessary to prepare for predictive modeling. This includes outlier detection and management, handling missing data of all types (random, completely at random, and not at random), and making mathematical transformations. In addition, several basic and common modifications of Pandas DataFrames are covered, including iterating over records, recoding values, and converting dates to integers.

Tableau is a popular and powerful tool for data exploration, analytics, and visualization. This content starts from an assumption of zero experience and then takes you through most of the basic functions and features. By the end of this module, you’ll be able to create interactive, dynamic dashboards and embed them into websites.