DA 101: Introduction to Data Analytics

Textbook Information

Optional Textbook: A General Introduction to Data Analytics,1st Edition by João Moreira, Andre Carvalho, Tomás Horvath, ISBN-13: 978-1119296249, ISBN-10: 1119296242

E-Book Option: An online version of one or more of your texts is available at no cost as a Penn State Library E-Book. Some E-Books will only be available online, while others will be available to download in full or in part. This book will be available for free through the Penn State Libraries.

Published Remarks

None

Hardware Requirements

None

Software Requirements

None need to be purchased. All software used in the course is available free to students through Penn State.

Proctored Exams

None

Course Description

This course is designed around storytelling with data. It is designed to introduce students to foundational concepts in data analytics. Students will learn key concepts used in the data analytics industry to understand and frame projects. The core dimensions of analytics prescribed by current data analytics professional guidelines will be introduced and demonstrated through case studies. Students will be exposed to spreadsheets, scripting languages for analytics, and current statistical software packages. The importance of communicating findings to different constituents will be emphasized throughout this class. 

Course Pre-requisites: None

Course Objectives

Upon completion of this course, the students will be able to: 

  • Identify the different types of analytics approaches 
  • Frame problems in various contexts in a data analytics solution 
  • Identify the appropriate analyses required to address a problem  
  • Effectively communicate findings to different stakeholders 
  • Gather information about the data analytics career field

Course Requirements and Grading

This course will use a variety of methods to assess and evaluate student learning, which include the following:

  • Discussion boards – There is an initial introductory discussion board at the beginning of the course and reflective type discussion boards in most of the remaining modules. Your substantive posts and replies to peers each week will allow me to gauge your progress and ability to articulate key concepts.
  • Module quizzes – Most modules will have a low stakes quiz (usually worth less than 10 points) and allow you to practice your mastery of the concepts in the lessons.
  • Mid-term Exam – This exam will use content from modules 1 – 4 to gauge your understanding of course concepts to that point in the course.
  • Case Studies – Most modules will have a real-world case study to help you apply the module content to a specific data problem.
  • Final Project – The final project is a culminating case study that will allow you to showcase your learning of all course concepts, from data analysis, to data visualization and storytelling, to creating a final data report.
  • Other assignments – The course also has several other assignments, such as a homework that asks you to conduct a job search and another that asks you to create a data story.

You will earn a grade that reflects the extent to which you achieve the course learning objectives listed above. Grades are assigned by the points earned in each lesson’s activities and then weighted based on the categories below. Below is a breakdown of each assignment type and it’s value as a percentage of the total course grade.

Course Assignment Weighting
Assignment Value
Discussion Boards 10%
Module Quizzes 10%
Mid-term Exam 15%
Case Studies 35%
Other Assignments 15%
Final Project 15%