4.6
stars
34 ratings

Elaine Hanley

1,971 already enrolled

Offered By

DataOps Methodology

IBM Skills Network

About this Course

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DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.”
The DataOps Methodology is designed to enable an organization to utilize a repeatable process to build and deploy analytics and data pipelines. By following data governance and model management practices they can deliver high-quality enterprise data to enable AI. Successful implementation of this methodology allows an organization to know, trust and use data to drive value.

In the DataOps Methodology course you will learn about best practices for defining a repeatable and business-oriented framework to provide delivery of trusted data. This course is part of the Data Engineering Specialization which provides learners with the foundational skills required to be a Data Engineer.

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Beginner Level

None

Approx. 10 hours to complete

English

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What you will learn

Understand the process to establish a repeatable process that delivers rigor and repeatability
Articulate the business value of any data sprint by capturing the KPI's the sprint will deliver
Understand how to enable the organization's business, development and operations to continuously design, deliver and validate new data demands

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Beginner Level

None

Approx. 10 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.58/5 (14 Ratings)

Elaine Hanley

1,971 Learners

1 Course

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Establish DataOps - Prepare for operation

2 hours to complete

11 videos (Total 59 min)

11 videos

Introducing the DataOps Methodology4mDataOps Methodology Phases6mCourse Structure and Learning Objectives2mDataOps Case Study2mIntroduction to AI Ladder and DataOps Capabilities7mData Strategy Overview4mData Strategy Considerations10mData Strategy Case Study2mEstablish Team Introduction4mTeam Roles and Responsibility7mTeam Organizational Structure6m

2 practice exercises

Review Questions30mReview Questions30m

Week2

Week 2

2 hours to complete

Establish DataOps – Optimize for operation

2 hours to complete

10 videos (Total 56 min)

Week3

Week 3

1 hour to complete

Iterate DataOps - Know your data

In this lesson you will learn about the capabilities that you will need to use to understand the data in repositories across an organization. Data discovery is most appropriately employed when the scale of available data is too vast to devise a manual approach or where there has been institutional loss of data cataloging. It utilizes various techniques to programmatically recognize semantics and patterns in data. It is a key aspect of identifying and locating sensitive or regulated data to adequately protect it, although in general, knowing what stored data means unlocks its potential for use in analytics. Data Classification provides a higher level of semantic enrichment, enabling the organization to raise data understanding from technical metadata to a business understanding, further helping to discover the overlap between multiple sources of data according to the information that they contain:

1 hour to complete

2 videos (Total 14 min)

Week4

Week 4

1 hour to complete

Iterate DataOps – Trust your data

In this lesson you will learn that understanding data semantics helps data consumers to know what is available for consumption, but it does not provide any guidance on how good that data is. This module is all about trust, how reliable a data source can be in providing high fidelity data that can be used to drive key strategic decisions, and whether that data should be accessible to those who want to use it; whether the data consumer is permitted to see and use it. This module will address the common dimensions of data quality, how to both detect and remediate poor data quality. And it will look at enforcing the many policies that are needed around data quality, not least the need to respect an individual’s wishes and rights around how their data is used:

1 hour to complete

3 videos (Total 17 min), 1 reading, 2 quizzes

Week5

Week 5

2 hours to complete

Iterate DataOps – Use your data

In this lesson you will learn that providing useful data in a catalog can often necessitate some transformation of that data. Modifying original data can optimize data ingestion in various use-cases, such as combining multiple data sets, consolidating multiple transaction summaries, or manipulating non-standard data to conform to international standards. This module will examine the choices for data preparation, how visualization can be used to facilitate the human understanding of the data and what needs to be changed, and the various options for single use, optimization of data workflows and ensuring the regular production of transformations for operational use. Furthermore, this module will show you how to plan and implement the data movement and integration tasks that are required to support a business use case. The module is based on a real-world data movement and integration project required to support implementation of an AI-based SaaS analytical system for supply chain management running in the Google cloud. The module will cover the major topics that need to be addressed to complete a data movement and integration project successfully:

2 hours to complete

4 videos (Total 21 min), 1 reading, 3 quizzes

Week6

Week 6

1 hour to complete

Improve DataOps

1 hour to complete

1 video (Total 10 min)

Week7

Week 7

1 hour to complete

Summary & Final Exam

1 hour to complete

1 video (Total 5 min)

Reviews

4.6

11 reviews

5 stars
67.64%
4 stars
29.41%
3 stars
2.94%

TOP REVIEWS FROM DATAOPS METHODOLOGY

by LHJul 31, 2022

Great over view and good breakdown of the concepts

by DTOct 21, 2021

Really enjoyed this, explains all the proccesses really well

View all reviews

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DataOps Methodology

DataOps Methodology

About this Course

What you will learn