LSB Data Camp 2021-22

Interested in learning new data analytics skills or honing your existing ones?

If you answered yes, then join the Love School of Business and Center for Organizational Analytics for the new LSB Data Camp series. Throughout the year, we are hosting workshops on a variety of topics to help better prepare you for your future career. A light dinner will be provided for participants.

Upcoming Sessions

R Programming 101 with Prof. Adam Aiken

Feb. 8, 5-8 p.m.

Register

Learn Text Mining with Prof. Long Xia

March 15, 5-8 p.m.

Register

Learn Robotic Process Automation with Prof. Kevin Agnew

April 12, 5-8 p.m.

Register

 

Session Descriptions

Tableau 101

In this session, we will examine concepts of data visualization and learn how Tableau can help you make sense of your large dataset. You will also learn how to prepare your data for compelling storytelling. Prof. Ajjan will guide you step-by-step to build a story and convey data insights clearly and directly.

By the end of the module, learners will:

  • Build a solid foundation with core concepts and techniques for working with data to create visualizations in Tableau
  • Develop an understanding of visual best practices
  • Learn how to create a digital story to effectively share your information and insights

Instructor: Dr. Haya Ajjan

Intro to AI & Machine Learning Using Python

In this session, you will learn the basic concepts behind data mining and machine learning. We will go hands-on with Python and Jupyter Notebooks to experience the machine learning process, first-hand. You will be guided through the process of loading data, analyzing it and using it to make accurate predictions.

By the end of the module, learners will:

  • Understand the key ideas and terminology in machine learning
  • Become familiar with popular machine learning tools
  • Learn how to use machine learning to analyze and predict information

Instructor: Dr. Steven Dinger

Learn Web-Based Analytics

In this session, we will examine concepts of data analytics with a special focus on web-based data. Specifically, you will build a solid foundation with core concepts in web-based analytics. You will also apply popular industry-standard analytics tools to analyze web-scale data and gain meaningful insights from them. A mixed teaching approach—a combination of short lectures and hands-on practices—will be adopted to guide you step-by-step to build effective web-based analytics systems to support real-world business decisions.

By the end of the module, learners will be able to:

  • Understand the key concepts in web-based analytics, including web development basics, definition and categories of web analytics
  • Apply practical analytics techniques for working with web-scale data to support decision-making across multiple business domains following the analytics processes (e.g., data collection, preprocessing, analytical modeling, evaluations, deployment, etc.)
  • Evaluate the innovative applications of web analytics in modern businesses and organizations and potentially create new business opportunities

Instructor: Dr. Long Xia

R Programming 101

In this module, we’ll learn about R, the free, open source, and powerful statistical software. We’ll use the RStudio developer environment, also free to download, and the R language to import, clean, visualize, and summarize our data. Instructions for set-up (Mac or PC) will be provided.

By the end of the module, we will:

  • Learn how to install R, R Studio, and navigate our developer environment.
  • Use “Tidyverse” packages and syntax to explore our data.
  • Discuss the basics of moving from “point and click” statistical analysis to a more reproducible and automated workflow.

Instructor: Dr. Adam Aiken

Learn Text Mining

In this learning module, we will examine concepts of data mining with a special focus on textual data, which has become a major data format in the business world. Specifically, you will build a solid foundation with core concepts in text mining and analytics. You will also apply popular text mining tools to analyze text data from multiple perspectives (e.g., topic modeling, sentiment analysis, text classification, etc.) and gain meaningful insights. This module will guide you step-by-step to build effective text analytics systems to support real-world business decisions.

By the end of the module, learners will be able to:

  • Understand the key concepts in text analytics, including various types of text analytics and how each of them would support practical business operations and decision-making;
  • Apply different levels of text analytics algorithms, including basic lexicon-based methods, traditional machine learning, and state-of-the-art deep learning;
  • Evaluate the innovative applications of text analytics in modern businesses and potentially create new business opportunities.

Instructor: Dr. Long Xia

Learn Robotic Process Automation

Description TBA

Instructor: Dr. Kevin Agnew