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Description

Are you interested in learning how data can help a business thrive and prosper?
Do you want to be able to leverage the value of your business data?
If so, then this is the perfect course for you!
The hype around AI, data science, analytics and business intelligence is at its peak. Almost all companies are aware that data can help them improve their performance in some way, shape, or form. However, the majority of business executives commit the same crucial mistake:
“Tactics without strategy is the noise before defeat’’
Sun Tzu, Chinese military strategist
Collecting and analysing data for the sake of working with numbers is far from optimal.
Data is only as valuable as the insights you will obtain from it.
So, to position your business for success in today’s AI and data-driven world, you have to start by reflecting on several key questions.
What are the key decisions your company will make that can be improved with the right data?
How is data going to help your firm improve and automate business processes?
In what way can data make your products or services better?
To what extent is your business’s data valuable to external parties who might be willing to pay for it?
It is much better to try and answer such fundamental questions first, rather than focusing extensively on data analysis techniques and data storage infrastructure requirements before you have defined a roadmap of how data will help your business in the long run.
A smart business executive focuses on data strategy first.
In this course, we will cover several important topics that will prove to be invaluable if you are:
- a business owner,
- a business executive
- an aspiring data practitioner.
We will provide context and help you understand why data is one of the most important for any business today. We’ll talk about hundreds of ways companies have benefited from a well-structured data strategy in real life. By the end of the course you will be able to recognize data-related opportunities in your own organization.
The course starts by focusing on the main ways in which data can help a business:
- use data to improve business decisions.
- use data to understand your customers and markets
- use data to provide more intelligent products and services
- use data to improve your business processes.
- use data to generate a meaningful revenue stream
We’ll discuss how companies have benefited from data in each of these scenarios and the practical implications you need to bear in mind before embarking on your data projects.
Then, in the next section of the course, we will do one of my favorite exercises that I do when working with and consulting for my clients. I will show you how to define your data use cases. We will brainstorm the data opportunities for your business and identify possible data use cases, ensuring a clear link to your strategic business goals. We will take this process as an opportunity to review your existing strategy to ensure it is still relevant in today's business world. We will then make sure you don't fall into the trap of identifying too many use cases - it is not about finding as many as you can, rather than the most important ones.
Then the course continues by focusing on sourcing and collecting data. An important topic that involves several key considerations. We will distinguish between structured and unstructured data, internal and external data, and so on. By the end of this section, you will have an idea how a company should approach data collection, and understand the different sources of data that could be used besides internal data.
This is a truly comprehensive course. We’ve also included sections on:
- Data governance, ethics, and trust
- How to turn data into insights (a brief description of the various techniques that can be used to analyze data)
- How to create the appropriate technology and data infrastructure in your company
- How to build the necessary data competencies in your organization
- How to execute and revisit your data strategy
I’m very excited that you are interested in this subject because I believe that this is one of the most fascinating aspects of today’s business world. Innovation through the use of data and data analysis is something I am very passionate about. I’ll be happy if you start or advance your data analysis journey with the Data Strategy course and I hope I will see you inside the course!
Bernard Marr
Who this course is for:
Data scientists
Data analysts
Business intelligence analysts
Business executives
Ambitious managers
Aspiring entrepreneurs
Financial analysts
Anyone who wants to understand how data can create value for their business

What you'll learn

How to profit from a world of big data, analytics, and AI

How to use data to improve business decisions

Understand your customers and markets

Provide more intelligent data-driven services

Learn how to build more intelligent products

Put your business in a position to be able to monetize its data

Define relevant data use cases for your industry

Learn how to source and collect data

Understand the importance of data governance, ethics and trust

Be able to turn data into insights

Know how to collect, process, and store data

Improve your data communication skills

Build the necessary data competencies in your firm

Execute your data strategy

Ask clear Key Business Questions (KBQs)

Be able to distinguish the fundamental types of data analysis techniques

Learn how to design a KPI dashboard

Gain an idea which are the most valuable skills for data scientists and data analysts

Understand which data strategies fail

Acquire a ‘use data for good’ perspective

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Welcome to the course!
1
1.1-Welcome to the course!
2-Deciding your strategic data needs
1
2.1-Delineating the 5 strategic data use case areas
3-Using data to improve your decisions
8
3.1-Section Introduction
3.2-Curated dashboards vs. self-service data exploration
3.3-Challenges related to self-service data exploration
3.4-Asking key business questions first (KBQs)
3.5-The power of clear Key Business Questions (KBQs)
3.6-How to ask the right Key Business Questions
3.7-Giving people access to data
3.8-Curating the most important data insights
4-Using data to understand your customers and markets
5
4.1-Secton intro
4.2-How this butcher uses data to understand customers
4.3-Netflix use case - vs Disney - this is why Disney launched Disney +
4.4-Amazon use case
4.5-The increasing need for real-time data to understand customers and markets
5-Using data to provide more intelligent services
1
5.1-Using data to provide more intelligent services
6-Using data to make more intelligent products
1
6.1-Using data to make more intelligent products
7-Using data to improve your business processes
1
7.1-Using data to improve your business processes
8-Monetising your data
2
8.1-Monetising your data - intro
8.2-The Shotspotter case study
9-Defining your data use cases
3
9.1-Defining data use cases walk through (part 1)
9.2-Defining data use cases walk through (part 2)
9.3-Defining data use cases walk through (part 3)
10-Sourcing and collecting the data
10
10.1-Secton intro
10.2-Structured vs unstructured data
10.3-Internal vs external data
10.4-Different types of data
10.5-Meta data
10.6-The importance of realtime data
10.7-Gathering internal data
10.8-Accessing external data
10.9-Sources of external data
10.10-When the data you want doesn't exist
11-Data governance
4
11.1-Section intro
11.2-To own or not to own
11.3-Ensuring the correct rights are in place
11.4-Case study on building trust
12-Turning data into insights
17
12.1-Section intro
12.2-Text analytics
12.3-Sentiment analytics
12.4-Image analytics
12.5-Video analytics
12.6-Voice analytics
12.7-Data mining
12.8-Business experiments
12.9-Visual analytics
12.10-Time series analysis
12.11-Monte carlo simulation
12.12-Linear programming
12.13-Cohort analysis
12.14-Factor analysis
12.15-Neural network analysis
12.16-Deep learning
12.17-Reinforcement learning
13-Creating the technology and data infrastructure
11
13.1-Section intro
13.2-How to collect data
13.3-Database, Data warehouse, Data mart and Data lake
13.4-How to store data
13.5-How to process data
13.6-Communicating data
13.7-What is а KPI dashboard
13.8-How to design a KPI Dashboard
13.9-Reporting lessons from journalists
13.10-Using KPI dashboard software
13.11-Big data as a service
14-Building the data competencies in your organisation
6
14.1-Section intro
14.2-Skills shortage
14.3-The skills needed for a data scientist
14.4-Building internal skills and competencies
14.5-Outsourcing your data analysis
14.6-Leadership challenges
15-Executing and revisiting your strategy
6
15.1-Putting the data strategy into action
15.2-Why data strategies fail
15.3-Creating a data culture
15.4-Revisiting the data strategy
15.5-A changing business environment
15.6-Changing technology landscape
16-Looking ahead
1
16.1-Using data for good