Data Literacy & Management: Northern Kentucky University, Greater Cincinnati Region

Data Literacy & Management courses teach the fundamentals of interpreting data, performing analyses, and understanding and communicating actionable insights. Topics of study for beginning and advanced learners include qualitative and quantitative data analysis, tools and methods for data manipulation, and machine learning algorithms.

An Intuitive Introduction to Probability
This course will provide you with a basic, intuitive and practical introduction into Probability Theory.

Build a Database from a Relational Model
In this course you will interpret the components of a relational data model, convert that model into a relational database, and then test the database design.

Business Intelligence Concepts, Tools, and Applications
In this course, you will gain the knowledge and skills for using data warehouses for business intelligence purposes and for working as a business intelligence developer.

Create Interactive Graphs in Tableau
In this project, we will learn how to create an account, create an Interactive Graph in Tableau and share it with others.

Data Science Methodology
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.

Data Science in Real Life
In this one-week course, we contrast the ideal with what happens in real life.

Data Visualization with Python
The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people.

Data – What It Is, What We Can Do With It
Learn to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights.

Exploratory Data Analysis for Machine Learning
Learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

Fitting Statistical Models to Data with Python
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data.

Fundamentals of Big Data
In this course, you will be introduced to many of the core concepts of big data.

Intro to Analytic Thinking, Data Science, and Data Mining
Explore data science with a focus on the skills and ethical considerations required when working with data.

Introduction to Applied Machine Learning
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation.

Introduction to Python
In this tutorial you will create a guessing game application that pits the computer against the user.

Introduction to Relational Database and SQL
In this guided project, you will get hands-on experience working with a relational database using MySQL Workbench from Oracle.

Linear Regression with Python
In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy.

Managing Data Analysis
Discover the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication.

Measurement - Turning Concepts into Data
This course provides a framework for how analysts can create and evaluate quantitative measures.

Multiple Linear Regression with scikit-learn
In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python.

Programming for Everybody (Getting Started with Python)
Learn the basics of how one constructs a program from a series of simple instructions in Python.

Python Basics
This course introduces the basics of Python 3, including conditional execution and iteration as control structures, and strings and lists as data structures.

Python Classes and Inheritance
This course introduces classes, instances, and inheritance. You will learn how to use classes to represent data in concise and natural ways.

What are the Chances? Probability and Uncertainty in Statistics
This course focuses on how analysts can measure and describe the confidence they have in their findings.