Data Science – Consulting Approach

The goal of the subject is to familiarize learners with an end-to-end data science project pipeline. It will provide a comprehensive overview of the role of data science in strategy consulting. Subject is designed to discuss and address real life business challenges. It will focus both on technologies (horizontal learning approach) and soft skills (presentation and communication). Intermediate knowledge of Python and SQL, as well as general IT literacy is required from the participants.

1. Understanding consulting business model and the role of data science in this ecosystem.

a. Project types and related data science engagements.

b. Career paths: from management consultants to data roles: data engineer, BI engineer, data scientist, analytics consultant.

c. Technology stack

2. Coding best practices and Git version control

a. How to write good code: classes, functions, documentation

b. How to work with Git

i. Local set up

ii. GitHub repo

a) How to work with it

b) Importance of GitHub repo to build portfolio

a) Pull Requests

b) Preparation to work in groups on one project repo

3. Data Analysis with Python

a. Focus on data processing (pandas), understanding challenges – how to prepare for common data issues.

b. Solving a business problem.

c. Final product – simple web app

4. Intro to cloud

a. Main providers and key considerations

b. Working with Azure

c. Setting up virtual machine

d. Deploying web app to VM

5. Business Intelligence

a. Role of Business Intelligence

b. Power BI

i. Data infrastructure - M language and DAX

ii. Creating a visually appealing dashboard

6. Generative AI

a. Ethical considerations and confidentiality

b. Popular tools (paid vs open source)

i. Chat GPT API (or Azure Open AI Services)

ii. Transfomers (HuggingFace)

7. Mastering Presentation – How to present to non-technical audience

8. Capstone project

a. End-to-end project based on discussed subjects and tools. Raw data will be provided.

i. Build data model

ii. Analyze

iii. Power BI Dashboard

b. Strong emphasis on collaboration (Git)

1. Kleppmann, M. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, 2017.

2. McKinney, W. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition, 2017.

3. Loeliger, J., & McCullough, M. Version Control with Git: Powerful tools and techniques for collaborative software development, 2nd Edition, 2012

4. Crawford, K. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, 2021

5. Densmore, J. Data Pipelines Pocket Reference: Moving and Processing Data for Analytics, 2021

After the course participant should be better prepared to work in data related role in business environment. Essential outcome is understanding that good analytics must be explainable. It is not enough to write a working code. Documentation, storytelling and teamwork are equally important.

• Students to work on capstone projects in groups

• GitHub repository to store project code (repo will be reviewed to ensure students contributed to the project equally – lack of contribution will be reflected in a final score).

• Power BI Dashboard and Power Point Presentation.