The Complete Data Science Project Management Course

Name: The Complete Data Science Project Management Course
Rating: 3.8 (146 reviews)

Learn step by step Data Science Project Management through CRISP-DM industry standard data mining methodology

Created byHiTech Squad

Last updated 9/2018

English

What you'll learn

Plan end to end data science projects including activities involved, dependencies, external/internal resource needs and skills requirements
Manage stakeholder expectations on the delivery of data science projects
Manage data science team and ensure alignment to larger project/program objectives
Plan communications on status reporting of data science projects with details of all activities

Course content

9 sections • 39 lectures • 1h 20m total length

Course Introduction3:42
Purpose, target learners and learning outcomes

Business Understanding Phase Introduction0:53
Introduction to Business Understanding phase of CRISP-DM
Determine Business Objectives3:04
Determine Business Objective Task with its outputs and activities
Assess Situation5:08
Assess Situation Task with its output and activities
Determine Data Mining Goals3:02
Determine Data Mining Goals task with its outputs and activities
Produce Project Plan2:29
Produce Project Plan task with its outputs and activities
Business Understanding Phase Conclusion1:11
Conclusion of Business Understanding Phase of CRISP-DM
Business Understanding Quiz

Data Understanding Phase Introduction0:50
Introduction to Data Understanding phase of CRISP-DM
Collect Initial Data2:03
Collect Initial Data task with its outputs and activities
Describe Data2:07
Describe Data task with its outputs and activities
Explore Data2:16
Explore Data task with its outputs and activities
Verify Data Quality2:03
Verify Data Quality task with its outputs and activities
Data Understanding Phase Conclusion1:05
Conclusion of Data Understanding Phase of CRISP-DM
Data Understanding Quiz

Data Preparation Phase Introduction1:40
Introduction to Data Preparation phase of CRISP-DM
Select Data2:54
Select Data task with its outputs and activities
Clean Data2:06
Clean Data task with its outputs and activities
Construct Data3:22
Construct Data task with its outputs and activities
Integrate Data1:58
Integrate Data task with its outputs and activities
Format Data2:03
Format Data task with its outputs and activities
Data Preparation Phase Conclusion0:54
Conclusion of Data Preparation Phase of CRISP-DM
Data Preparation Quiz

Modeling Phase Introduction1:04
Introduction to Modeling phase of CRISP-DM
Select Modeling Technique2:51
Select Modeling Technique task with its outputs and activities
Generate Test Design2:35
Generate Test Design task with its outputs and activities
Build Model2:38
Build Model task with its outputs and activities
Assess Model2:36
Assess Model task with its outputs and activities
Modeling Phase Conclusion0:52
Conclusion of Data Preparation Phase of CRISP-DM
Modeling Quiz

Deployment Phase Introduction1:02
Introduction to Deployment phase of CRISP-DM
Plan Deployment1:58
Plan Deployment task with its outputs and activities
Plan Monitoring and Maintenance2:00
Plan Monitoring and Maintenance task with its outputs and activities
Produce Final Report2:00
Produce Final Report task with its outputs and activities
Review Project1:58
Review Project task with its outputs and activities
Deployment Phase Conclusion0:39
Conclusion of Deployment Phase of CRISP-DM
Deployment Phase Quiz

Requirements

None

Description

This course is to enable learners to successfully manage a data science project. It is process oriented and explains CRISP-DM methodology. CRISP-DM, stands for Cross Industry Standard Process for Data Mining, and it is the most widely used, holistic framework for data science projects.

This course takes you through the data mining activities in the context of Project Management. The project explains inputs and outputs of all activities helping effective project management of a data science project. As per the Project Management best practices it guides you to engage the right stakeholders to help setting Data Mining Success criteria to achieve the business goals.

Machine Learning and Model building activities using Python or R are an important activities in any data science project. However, there are several other activities that are part of any Data Science project. The data needs to be prepared for application of machine learning techniques. There are lot of steps involved in preparing a data-set which would be suitable for achieving the business goal of the data science project. In this course, we are going to take a broader look and identify how each activity of CRISP-DM fits together towards achieving business outcomes of a data science project.

The course lists the monitoring, reporting and user training needs during the execution of data science project. The data science project needs to conclude with deployment of data mining results and review of lessons learned. All the above activities are sequenced in this course along with their purpose and elaborate details for end to end execution of a data science project.

Who this course is for:

Project/Program Managers who want to manage data science, Artificial Intelligence and Machine Learning projects
Business Analysts/ Domain Specialists/ Business sponsors who need to drive business improvements through data science projects
Data Science team members (developers/technologists/data analysts) who want to get the big picture of data science projects
Aspiring Data science professionals, who want to learn complete methodology without having to learn coding or technical topics

The Complete Data Science Project Management Course

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

CRISP-DM Overview1 lecture • 5min

Business Understanding6 lectures • 16min

Data Understanding6 lectures • 10min

Data Preparation7 lectures • 15min

Modeling6 lectures • 13min

Evaluation5 lectures • 9min

Deployment6 lectures • 10min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: