Over the course of ten weeks, you will be exposed to many of the most common techniques used to manipulate and analyze data. At the end of this program, you will be able to work effectively with data science and analytics teams to drive business decisions and successful outcomes for your organization.
Probabilistic Decision Making
This module provides a brief introduction to the foundations behind data science and analytics before exploring the fundamentals of data. In addition, you will review a tutorial on using Jupyter Notebook, an interactive computational environment that will allow you to combine code execution, rich text and data plots and analyses.
Basic Regression Models
Simple regression analyses are at the heart of more elaborate data-driven business decision making. We’ll focus on understanding the ways in which these models are used, the assumptions that make their use valid and how to leverage these models to make better business decisions. The data set for this module focuses on using crime rates to predict housing pricing in Philadelphia.
Creating Sample Data
Explore the science of surveys by way of understanding data samples and sampling variation and quality. This module will describe the methods by which sampling is used to analyze the pros and cons of business decisions through the exploration of sampling, type I and type II errors and control limits.
Advanced Regression Models
Learn about two of the most ubiquitous uses of data science and analytics: forecasting and A/B testing. These will include the analysis of variance, time series regressions and the design and execution of simple and more complex A/B testing procedures. Application is based on the Capital Asset Pricing Model, a tool that describes the relationship between systematic risk and expected return for assets.
Learn about the importance of making business decisions based on conducting statistical tests, comparisons, confidence intervals and margins of error. You will explore these concepts through the lens of a case focused on direct mail advertising, and complete problem sets using the 4M model (Motivation, Method, Mechanics, Message).
Forecasting Machine Learning
Explore some of the more fundamental machine learning methods and how they apply to business decisions. Concepts include supervised learning and ML applications such as spam detection.
Extrapolating Information from Sample Data
Explore how to maximize profits through the extrapolation of information from sample data. You will explore linear and curved patterns, demand, price setting and elasticities.
Building Effective Data Science Teams
Wrap-up the program with a deep dive into the suite of competencies that define effective data science teams and how to build a data-driven culture in your organization. Common pitfalls will be stressed, and strategies to work effectively with data scientists will be laid out.
Note: In order to help you explore some of the hands-on techniques that lead directly to making better data-driven decisions, there will be two week-long learning labs as an opportunity to dig deeper into the data. This makes for a 10-week long program in total.