02 Chapter: What is Data Science?
In this unit, you’ll learn best practices to help you be a better engineer and to work more effectively with and engineering team. As a data scientist, no matter how many algorithms you design, how much data you crunch or charts you create, ultimately, you’ll be writing software. Some companies expect their data scientists to contribute directly to the code base, others have engineers who are around to help translate prototype code to production. No matter which kind of team you’re working with, it’s critical to learn how to be a good citizen of the code base, so that you can make life easier for yourself and the rest of your team. The better your code is, the easier it is to deploy, and the greater the likelihood that you'll see your projects having an impact on the company!
- Become familiar with the field of Data Science, its applications, larger impact, and limitations
- List different ways Data Science is being applied
- Identify the broad skill sets that data scientists need to possess
- List three ideas for your first Capstone Project
- Big data: A collective term used for technology to analyze large amounts of data to unearth insights, typically into human behavior and patterns.
- Data set: A collection of data to be analyzed.
- Analytics: A collective term for techniques used to analyze data, mostly to draw business insights.
- Algorithm: A well-defined set of steps to solve a specific problem.
- Hypothesis: An educated guess that needs to be validated (or disproved) by experiment and data.
We offer two specialized learning tracks focused on Natural Language Processing and Deep Learning. You can choose either one of these specializations or remain a generalist and dive deeper into advanced machine learning techniques. As you progress through the curriculum and talk with your mentor, you'll get a better sense of what track you should choose. Meanwhile, here are a few resources to give you a sense of the specialization fields.
This article provides a brief explanation of natural language processing, discusses its uses, and highlights a few innovative startups that use NLP.
Deep learning networks produce actionable results for a variety of commercial enterprises. This Forbes article profiles a few companies that use deep learning solutions to help customers in exciting and innovative ways.
