The Complete Data Science Project Management Course

The Complete Data Science Project Management Course, available at $34.99, has an average rating of 2.95, with 39 lectures, 7 quizzes, based on 141 reviews, and has 407 subscribers.

You will learn about 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 This course is ideal for individuals who are Project/Program Managers who want to manage data science, Artificial Intelligence and Machine Learning projects or Business Analysts/ Domain Specialists/ Business sponsors who need to drive business improvements through data science projects or Data Science team members (developers/technologists/data analysts) who want to get the big picture of data science projects or Aspiring Data science professionals, who want to learn complete methodology without having to learn coding or technical topics It is particularly useful for Project/Program Managers who want to manage data science, Artificial Intelligence and Machine Learning projects or Business Analysts/ Domain Specialists/ Business sponsors who need to drive business improvements through data science projects or Data Science team members (developers/technologists/data analysts) who want to get the big picture of data science projects or Aspiring Data science professionals, who want to learn complete methodology without having to learn coding or technical topics.

Enroll now: The Complete Data Science Project Management Course

Summary

Title: The Complete Data Science Project Management Course

Price: $34.99

Average Rating: 2.95

Number of Lectures: 39

Number of Quizzes: 7

Number of Published Lectures: 39

Number of Published Quizzes: 7

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: ₹1,199

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Chapter 2: CRISP-DM Overview

Lecture 1: Data Science Methodology – CRISP-DM

Chapter 3: Business Understanding

Lecture 1: Business Understanding Phase Introduction

Lecture 2: Determine Business Objectives

Lecture 3: Assess Situation

Lecture 4: Determine Data Mining Goals

Lecture 5: Produce Project Plan

Lecture 6: Business Understanding Phase Conclusion

Chapter 4: Data Understanding

Lecture 1: Data Understanding Phase Introduction

Lecture 2: Collect Initial Data

Lecture 3: Describe Data

Lecture 4: Explore Data

Lecture 5: Verify Data Quality

Lecture 6: Data Understanding Phase Conclusion

Chapter 5: Data Preparation

Lecture 1: Data Preparation Phase Introduction

Lecture 2: Select Data

Lecture 3: Clean Data

Lecture 4: Construct Data

Lecture 5: Integrate Data

Lecture 6: Format Data

Lecture 7: Data Preparation Phase Conclusion

Chapter 6: Modeling

Lecture 1: Modeling Phase Introduction

Lecture 2: Select Modeling Technique

Lecture 3: Generate Test Design

Lecture 4: Build Model

Lecture 5: Assess Model

Lecture 6: Modeling Phase Conclusion

Chapter 7: Evaluation

Lecture 1: Evaluation Phase Introduction

Lecture 2: Evaluate Results

Lecture 3: Review Process

Lecture 4: Determine Next Steps

Lecture 5: Evaluation Phase Conclusion

Chapter 8: Deployment

Lecture 1: Deployment Phase Introduction

Lecture 2: Plan Deployment

Lecture 3: Plan Monitoring and Maintenance

Lecture 4: Produce Final Report

Lecture 5: Review Project

Lecture 6: Deployment Phase Conclusion

Chapter 9: Conclusion

Lecture 1: Course Conclusion

Instructors

HiTech Squad
Group of Hi Tech Professionals

Rating Distribution

1 stars: 7 votes
2 stars: 20 votes
3 stars: 41 votes
4 stars: 50 votes
5 stars: 23 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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