BCGX Data Science

The simulation job of BCG X from The Forage https://www.theforage.com/simulations/bcg/data-science-ccdz

Introduction

BCG X is transforming today's businesses through data science and analytics initiatives. Our goal is to help companies gain a competitive advantage through these insights. This program is designed to give you a sense of what it's like to work at BCG X as we assist our clients using data science. In this job simulation, the participant will gain valuable insight into what it's like to solve meaningful challenges with our diverse and forward-thinking team at BCG X. The program will show the types of problems that are solved at BCG X and will attempt to simulate the challenges their will be facing, such as new terminology, ambiguity about the client's goals, and challenging data analysis.

The client of this project is PowerCo - a major gas and electricity utility that is concerned about their customers leaving for better offers from other energy providers, and the participant will work on data to get the answer. To achieve this, we typically follow a five-step methodology:

1. Business understanding & problem framing: What is the context of this problem, and why are they trying to solve it?

2. Exploratory data analysis & data cleaning: What data are we working with, what does it look like, and how can we improve it?

3. Feature engineering: Can we enrich this dataset using our own expertise or third-party information?

4. Modeling and evaluation: Can we use this dataset to make accurate predictions? If so, are these predictions reliable?

5. Insights & recommendations: How can we communicate the value of these predictions by explaining them in a way that matters to the business?

Task 1: Business Understanding & Hypothesis Framing

• Determine the client data needed for analysis
• Outline the techniques you'll use to investigate your client's problem
• Write an email to your Associate Director summarizing your approach

Task 2: Exploratory Data Analysis

• Use python to analyze client data
• Create data visualizations to help you interpret key trends

Task 3: Feature Engineering & Modelling

• Use Python to build a new feature for your analysis

Task 4: Findings & Recommendations

• Build a predictive model for churn using a random forest technique
• Write an executive summary with your findings