DATA SCIENCE
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DATA SCIENCE

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About this course

Duration : 42 Days
Course Overview

The 42-day Enterprise Data Science course is a fast-paced practical introduction to the interdisciplinary field of data science, which is the study of how to use computer science, statistics, and a scientific mindset to extract knowledge from data.

The goal of the course is to teach participants how to think in a data-scientific way, conduct or approximate experiments on business activity, analyze data rigorously and communicate results well. The course is aimed at people with no or little experience in computer science, but with a strong, undergraduate-level math background.

Outside of the classroom, participants are expected to spend about 6 hours a week on homework and their final project.

Intro to Computer Science and Python Programming

Programming languages bridge the gap between how humans like to express themselves and how computers actually work. This module teaches participants the basic workings of modern computers, and how to write effective Python code to communicate with them.

Intro to Data Science

‘Science’ is the most important word in data science. This module teaches the foundations of a scientific mindset: how to systematically question ideas, design experiments, and interpret results with the end goal of creating knowledge. It also teaches core data science skills – how to efficiently acquire, clean, manipulate and visualize data.

Intro to Applied Probability and Statistics

This module is designed to provide the participants with a grounding in probability and statistics. It covers the basics of probability creation to hypothesis testing.

Machine Learning

Machine Learning is a way of teaching computers to recognize the underlying patterns or structure to data.

This module teaches the statistics that make up common machine learning algorithms, and how to implement them.

Topics in Data Science

This module covers a range of topics. It first teaches the basics of deep learning and neural networks, the differences between the R and Python languages, how to store and access big data online, and how to use the cloud to run expensive computation.

Final Project

For the final project, the participants will study a data-related problem in their professional field or in a related field that they are interested in. The participants will acquire a real-world data set, form a hypothesis, clean, parse, and apply modeling techniques and data analysis principles to ultimately create a predictive model.

The instructional team will help participants scope out their project so that they choose a project that is feasible and that can display the skills that they have acquired from the course.