Cracking the Data Code – Advanced Topics
About Course
Master the More Nuanced Aspects of Data work
This Advanced Topics module provides a condensed introduction to optimizing your data streams. In this 4-hour video lesson, you’ll learn how to find good data workers an their roles in a data project, the tools needed for a data project, the areas where you can apply data projects in an org, when data projects are successful, when you should use A.I., why some data projects fail, the importance of data culture in an org, and more!
This course is designed as an important installment to the subject and targets people involved in the creation and management of data projects.
Course Content
1. Where Can You Find (Good) Data Workers?
1.1 Introduction
1.2 Among university graduates
1.3 Consultants / Freelancers
1.4 Startups
1.5 In-house (after proper training)
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01:38
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02:42
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1.3 Consultants / Freelancers
02:12 -
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1.4 Startups
03:11 -
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1.5 In-house (after proper training)
02:55 -
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2. What Tools Do You Need for Data Work?
2.1 Introduction
2.2 Hardware
2.3 Basic programming stack
2.4 Cloud platforms
2.5 Big Data tools
2.6 Other tools
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2.1 Introduction
00:48 -
2.2 Hardware
03:19 -
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2.3 Basic programming stack
04:36 -
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2.4 Cloud platforms
03:13 -
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2.5 Big Data tools
05:59 -
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2.6 Other tools
02:23 -
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3. Where Can You Apply Data Projects in an Org?
3.1 Introduction
3.2 Strategic decisions
3.3 Customer insights
3.4 Market analysis
3.5 Marketing campaigns
3.6 Specific KPIs based on data-rich processes
3.7 Anywhere else where there is good data
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3.1 Introduction
01:03 -
3.2 Strategic decisions
02:44 -
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3.3 Customer insights
04:19 -
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3.4 Market analysis
03:08 -
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3.5 Marketing campaigns
03:43 -
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3.6 Specific KPIs based on data-rich processes
05:41 -
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3.7 Anywhere else where there is good data
02:53 -
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4. When Is a Data Project Successful?
4.1 Introduction
4.2 When it meets its goals and adds value through them
4.3 When the integrity of the data is preserved
4.4 When everyone involved learns something new
4.5 When there is the potential of reproducibility
4.6 When a new iteration of the project is a worthy possibility
4.7 When it advances your and your team's know-how
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4.1 Introduction
01:15 -
4.2 When it meets its goals and adds value through them
03:19 -
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4.3 When the integrity of the data is preserved
02:47 -
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4.4 When everyone involved learns something new
04:25 -
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4.5 When there is the potential of reproducibility
03:41 -
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4.6 When a new iteration of the project is a worthy possibility
04:06 -
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4.7 When it advances your and your team’s know-how
04:04 -
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5. When Should You Use A.I.?
5.1 Introduction
5.2 When you have ample data
5.3 When other methods fail to deliver adequate value
5.4 When you need to brainstorm solutions, get text summaries, etc.
5.5 When you need to work with text data
5.6 When you need to interact with users or potential clients automatically
5.7 When it's part of your data strategy
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5.1 Introduction
01:27 -
5.2 When you have ample data
02:41 -
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5.3 When other methods fail to deliver adequate value
04:18 -
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5.4 When you need to brainstorm solutions, get text summaries, etc.
04:44 -
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5.5 When you need to work with text data
03:14 -
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5.6 When you need to interact with users or potential clients automatically
03:50 -
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5.7 When it’s part of your data strategy
03:50 -
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6. Who Does What in a Data Project?
6.1 Introduction
6.2 Data Lead
6.3 Data Scientist
6.4 Data Analyst
6.5 Data Engineer
6.6 Data Visualizer / Storyteller
6.7 Other, as needed
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6.1 Introduction
01:05 -
6.2 Data Lead
03:41 -
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6.3 Data Scientist
05:32 -
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6.4 Data Analyst
03:24 -
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6.5 Data Engineer
04:06 -
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6.6 Data Visualizer / Storyteller
03:21 -
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6.7 Other, as needed
03:58 -
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7. Who Else Needs to Be Involved (and When)?
7.1 Introduction
7.2 Consultant for Privacy Matters
7.3 Consultant for Data Matters
7.4 Cybersecurity Expert
7.5 Subject Matter Expert (SME)
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7.1 Introduction
00:47 -
7.2 Consultant for Privacy Matters
04:40 -
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7.3 Consultant for Data Matters
03:06 -
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7.4 Cybersecurity Expert
02:36 -
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7.5 Subject Matter Expert (SME)
03:41 -
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8. Why Do Some Data Projects Fail?
8.1 Introduction
8.2 Poor Planning
8.3 Poor Management
8.4 Poor Implementation
8.5 Insufficient Data
8.6 Stale / Bad Data
8.7 Inadequate Data Workers
8.8 Infrastructure Issues
8.9 Other Reasons
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8.1 Introduction
01:50 -
8.2 Poor Planning
02:19 -
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8.3 Poor Management
03:53 -
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8.4 Poor Implementation
03:12 -
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8.5 Insufficient Data
02:44 -
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8.6 Stale / Bad Data
02:54 -
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8.7 Inadequate Data Workers
02:18 -
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8.8 Infrastructure Issues
02:08 -
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8.9 Other Reasons
02:33 -
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9. Why Data Culture is Key for Any Data-oriented Org?
9.1 Introduction
9.2 Better collaboration among stakeholders and other members of the org
9.3 Smoother implementation of data projects
9.4 Better handling of data, esp. sensitive data
9.5 Data-driven decision-making
9.6 Easier identification of new opportunities in data work
9.7 Growth of everyone involved
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9.1 Introduction
01:53 -
9.2 Better collaboration among stakeholders and other members of the org
05:33 -
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9.3 Smoother implementation of data projects
02:59 -
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9.4 Better handling of data, esp. sensitive data
02:58 -
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9.5 Data-driven decision-making
04:11 -
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9.6 Easier identification of new opportunities in data work
03:08 -
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9.7 Growth of everyone involved
02:37 -
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10. What’s Next in Your Data Journey?
10.1 Introduction
10.2 Proof of Concept (PoC) project
10.3 Learn more about data work
10.4 Put together a Data Strategy
10.5 Empower data workers in your org & help them learn too, about your side of things
10.6 Ask good questions about data and relevant processes
10.7 Check out my latest book
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10.1 Introduction
01:21 -
10.2 Proof of Concept (POC) project
02:49 -
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10.3 Learn more about data work
02:42 -
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10.4 Put together a Data Strategy
02:36 -
Check what you’ve learned
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10.5 Empower data workers in your org and help them learn too, about your side of things
02:12 -
Check what you’ve learned
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10.6 Ask good questions about data and relevant processes
04:45 -
Check what you’ve learned
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10.7 Check out my latest book
01:40
Evaluation Tests
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Warm-up test (multiple choice)
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Open-ended questions
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