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Cracking the Data Code - Advanced Topics 1.1 Introduction

Cracking the Data Code - Advanced Topics

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1. Where Can You Find (Good) Data Workers?

1.1 Introduction

Video - 1:38 mins

1.2 Among university graduates

Video - 2:42 mins

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1.3 Consultants / Freelancers

Video - 2:12 mins

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Video - 3:11 mins

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1.5 In-house (after proper training)

Video - 2:55 mins

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2. What Tools Do You Need for Data Work?

2.1 Introduction

Video - 0:48 mins

Video - 3:19 mins

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2.3 Basic programming stack

Video - 4:36 mins

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2.4 Cloud platforms

Video - 3:13 mins

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2.5 Big Data tools

Video - 5:59 mins

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2.6 Other tools

Video - 2:23 mins

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3. Where Can You Apply Data Projects in an Org?

3.1 Introduction

Video - 1:03 mins

3.2 Strategic decisions

Video - 2:44 mins

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3.3 Customer insights

Video - 4:19 mins

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3.4 Market analysis

Video - 3:08 mins

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3.5 Marketing campaigns

Video - 3:43 mins

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3.6 Specific KPIs based on data-rich processes

Video - 5:41 mins

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3.7 Anywhere else where there is good data

Video - 2:53 mins

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4. When Is a Data Project Successful?

4.1 Introduction

Video - 1:15 mins

4.2 When it meets its goals and adds value through them

Video - 3:19 mins

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4.3 When the integrity of the data is preserved

Video - 2:47 mins

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4.4 When everyone involved learns something new

Video - 4:25 mins

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4.5 When there is the potential of reproducibility

Video - 3:41 mins

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4.6 When a new iteration of the project is a worthy possibility

Video - 4:06 mins

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4.7 When it advances your and your team's know-how

Video - 4:04 mins

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5. When Should You Use A.I.?

5.1 Introduction

Video - 1:27 mins

5.2 When you have ample data

Video - 2:41 mins

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5.3 When other methods fail to deliver adequate value

Video - 4:18 mins

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5.4 When you need to brainstorm solutions, get text summaries, etc.

Video - 4:44 mins

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5.5 When you need to work with text data

Video - 3:14 mins

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5.6 When you need to interact with users or potential clients automatically

Video - 3:50 mins

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5.7 When it's part of your data strategy

Video - 3:50 mins

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6. Who Does What in a Data Project?

6.1 Introduction

Video - 1:05 mins

Video - 3:41 mins

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6.3 Data Scientist

Video - 5:32 mins

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6.4 Data Analyst

Video - 3:24 mins

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6.5 Data Engineer

Video - 4:06 mins

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6.6 Data Visualizer / Storyteller

Video - 3:21 mins

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6.7 Other, as needed

Video - 3:58 mins

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7. Who Else Needs to Be Involved (and When)?

7.1 Introduction

Video - 0:47 mins

7.2 Consultant for Privacy Matters

Video - 4:40 mins

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7.3 Consultant for Data Matters

Video - 3:06 mins

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7.4 Cybersecurity Expert

Video - 2:36 mins

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7.5 Subject Matter Expert (SME)

Video - 3:41 mins

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8. Why Do Some Data Projects Fail?

8.1 Introduction

Video - 1:50 mins

8.2 Poor Planning

Video - 2:19 mins

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8.3 Poor Management

Video - 3:53 mins

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8.4 Poor Implementation

Video - 3:12 mins

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8.5 Insufficient Data

Video - 2:44 mins

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8.6 Stale / Bad Data

Video - 2:54 mins

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8.7 Inadequate Data Workers

Video - 2:18 mins

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8.8 Infrastructure Issues

Video - 2:08 mins

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8.9 Other Reasons

Video - 2:33 mins

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9. Why Data Culture is Key for Any Data-oriented Org?

9.1 Introduction

Video - 1:53 mins

9.2 Better collaboration among stakeholders and other members of the org

Video - 5:33 mins

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9.3 Smoother implementation of data projects

Video - 2:59 mins

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9.4 Better handling of data, esp. sensitive data

Video - 2:58 mins

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9.5 Data-driven decision-making

Video - 4:11 mins

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9.6 Easier identification of new opportunities in data work

Video - 3:08 mins

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9.7 Growth of everyone involved

Video - 2:37 mins

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10. What’s Next in Your Data Journey?

10.1 Introduction

Video - 1:21 mins

10.2 Proof of Concept (POC) project

Video - 2:49 mins

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10.3 Learn more about data work

Video - 2:42 mins

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10.4 Put together a Data Strategy

Video - 2:36 mins

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10.5 Empower data workers in your org and help them learn too, about your side of things

Video - 2:12 mins

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10.6 Ask good questions about data and relevant processes

Video - 4:45 mins

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10.7 Check out my latest book

Video - 1:40 mins

Evaluation Tests

Warm-up test (multiple choice)

Warm-up test (multiple choice)

Warm-up test (multiple choice)

Open-ended questions

Open-ended questions

Open-ended questions

Announcements Reviews Course Info

Let’s start our exploration of this course with an overview of this topic, related to a crucial part of data work: finding (good) data workers. This will help you manage expectations and also create a mental outline of the material to follow.

Learning often happens in classrooms
but it doesn’t have to.

admin@coursevolt.org

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