At a Glance

Grab your lab coat, beakers, and pocket calculator ... wait what? Wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed.

About This Course

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.

Learning Objectives

in this course, you will learn:

The major steps involved in tackling a data science problem.
The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
How data scientists think!

Course Syllabus

Module 1: From Problem to Approach

Business Understanding
Analytic Approach

Module 2: From Requirements to Collection

Data Requirements
Data Collection

Module 3: From Understanding to Preparation

Data Understanding
Data Preparation

Module 4: From Modeling to Evaluation

Modeling
Evaluation

Module 5: From Deployment to Feedback

Deployment
Feedback

General Information

This course is self-paced.
It can be taken at any time.
It can be audited as many times as you wish.

Requirements

Data Science Hands-on with Open Source Tools(Tools for Datascience) (DS0105EN)

Recommended skills prior to taking this course

Passion for Data Science

IBM