Data Analysis and Decision Making

Price: $299
Modality: Self-Paced, Online
Time Required: Approximately 40 hours

Overview

With the growing need to be knowledgeable and skilled in the area of data in the modern business environment, it is no surprise that you would choose this course. Many businesses are focusing on machine learning and big data. It is ideal to develop a good foundation in data analysis in order to weigh in intelligently on these discussions. By the end of this class, you will be prepared to operate successfully in this context. The first area we will tackle is probability, which involves measuring uncertainty and determining the best business decision. This can be achieved with descriptive statistics and data visualizations. This will be followed with an introduction to using Palisade StatTools which will be a recurring option throughout the course.

Next, you will learn how to form a confidence interval of estimation and how to perform common statistical tests to compare means and proportions. You will get the chance to practice these tests on different problem sets. We then dig into the foundations of linear regression. In the business context, many use this as a predictive model for decision-making. No matter what field of business you are involved in, you will find this course to be very relevant to your business ventures, decisions, and data challenges.

Learning Objectives

Analyze the role of uncertainty and risk in the decision-making process
Analyze available data to understand relationships among variables and to create predictions
Use available computing technology (e.g., spreadsheets) interpret make inferences about underlying populations
Turn raw data into insight and actionable information

Visual representation of the modules titles as bars on a graph This is an asynchronous self-paced course. That means that you can work at your own speed. There are review questions after most videos and reading. These questions are ungraded and designed for you to check your understanding of the content before moving on.

As this course is quantitative in nature, our goal is to give you as much practice as possible. We typically start by introducing you to concepts that you need to understand, followed by the equations. We then demonstrate very concrete examples of how to solve case-based problems before asking you to try some problems on your own. You will also have access to bonus content demonstrating how to solve some of the practice problems you complete.

Modules

Module 1: Introduction to Statistics and Probability Theory

In this module, we're going to be talking about probability, which is fundamentally important in all aspects of business. You may think that you are new to the concept of probability, but you actually make decisions based on probability every day. Any time you think to yourself, "What are the odds..." you are considering the probability that something will occur. In this module we're going to talk about how to calculate probability more precisely so you can make better decisions based on those calculations.

Module 2: Descriptive Statistics and Visualization

In this module, we will discuss the concept of descriptive statistics and what they are used for. We will also talk about data visualizations and how they can be used as tools for communicating the meaning behind data.

Module 3: Confidence Interval & Hypothesis Testing

In this module, we will discuss the concept of sampling distribution and construct confidence interval estimation for the means and proportions. Then we will examine how to use hypothesis testing to make statistical inferences on a population parameter.

Module 4: Regression I

In this module, we're going to be talking about linear regression. Linear regression is one of the most widely used prediction methods.

Module 5: Regression II

In this module, we're going to take a deeper dive into linear regression. We will expand the regression analysis we started in the previous module by looking at the purpose of our regression modeling. Is it explanation or prediction and how that may affect our modeling decisions? We will discuss multiple other topics including p-values, outliers, residual plots, multicollinearity, and lurking variables.