Learn How to Adopt the Growth Mindset and Build a Successful Data-Driven Business
What is data-driven decision making?
Why is it essential for your business?
How to take actionable steps to apply the growth mindset framework?
By the end of this course, you will be able to answer these questions and will have a clear idea of how to transform your company into a data-driven enterprise.
About the authors
Tina Huang is one of the most popular data YouTubers with more than 350k subscribers. She holds a Master’s degree (Computer and Information Technology) from the University of Pennsylvania and has worked as a data scientist at Meta.
Davis Balaba, PhD is a data science manager with significant experience in one of the MANGA companies.
Why is this course different?
The authors are not consultants who want to sell abstract ideas. They are a pair of data science manager and data scientist who have actually implemented the strategies discussed in the course in their own work and seen the results. If you are a manager or a small business owner, you will know that strategy is crucial, but implementation is a whole other beast. Davis and Tina are here to help you develop the growth mindset and help you start making data-driven decisions.
The growth mindset framework
What is the growth mindset? Why was it such a revolution?
The growth mindset manifests itself in a culture of discontent of the current state. The underlying assumption is that there is always more value to be uncovered and data is the path to unlock that value.
What if our business does not have as much data as a large tech company?
Growth is a mindset. Data fuels your growth thinking
You don’t realize how much data you have until you start focusing on it
The course also provides actionable advice on how to get started on your data journey. You don’t have to have big data to be data-driven.
Why is the growth mindset important?
This course is an amazing collection of videos that teach you how to approach data science work with a growth mindset. This mindset can be the key lever to grow your business.
The growth mindset is important for three reasons:
Ensures that you always work on the most important problems first
Helps clarify what outcomes you can expect
Focused on the desired end-result and encourages an execution mindset
How will this course help you?
You want to grow your company by trying to ensure it makes disciplined data-driven decisions.
In the absence of prior data, the associated risk to a data science investment is assumed to be high. When the associated risk is high, decision makers’ desire to invest is lower.
Where does that leave you as a data scientist, a data science team, or a data science leader that is trying to accelerate your company’s data journey?
This course will show you how approaching your work with a growth mindset can help you reduce the associated risk to data investment.
Davis and Tina propose low-cost ways to de-risk the decision for the budget owner. In the process, you will acquire the budget to get the talent, skills, and the tools you need to fully unlock the value hidden in your data.
Does the content of this course apply to all industries and verticals?
The answer is yes. In any vertical, in which you can have multiple challenges or multiple potential solutions (which is all businesses) coupled with limited resources, having a growth mindset will always help you prioritize better.
How is the course structured?
If this sounds a bit vague to you, no problem. Let’s be more concrete with full examples of what you can achieve at each stage of maturity.
Assuming we can define three stages of data maturity (level 1, level 2, level 3), Tina and Davis will explain what the different stages look like (in case you are not sure which stage you are in) and discuss how to use the growth mindset in that stage and what you can accomplish using the data you have.
Then the lessons will be hands-on and Davis would walk you through a full project, so you can see the exact steps in implementation. At the end of each section, Tina will discuss, what are the steps you need to undertake to get to the next stage of data maturity.
Sounds like one of the most valuable online courses you have come across, doesn’t it?
Well then what are you waiting for?
What you’ll learn
- How to adopt the data-driven growth mindset
- Transform your company into a data-driven enterprise
- Develop the growth mindset
- Understand the 3 levels of data maturity
- Diagnose the current state of affairs in your business
- Uncover business value by using data
- Start making data-driven decisions
- Build a data-driven business
- Ensure your business makes disciplined data-driven decisions
- Leverage the power of A/B testing and experimentation
- Acquire the necessary budget to hire talent and invest in data infrastructure
- Get your business to data maturity level 3
Table of Contents
1 Introduction to the course meet your instructors
The stages of data maturity
2 The stages of data maturity and what you will see next
3 How to go from no data to some data Reach Level 1 Optional
Data maturity level 1 Project 1
4 Data maturity Level 1
5 Intro to Project 1
6 Why do the analysis
7 Formulating an analysis plan
8 The data we will use
9 Exploring the data large dataset
10 Exploring the data small dataset
11 Customer journey
12 Top of funnel opportunities
13 Middle of funnel opportunities
14 Bottom of funnel opportunities
15 Test and learn
16 Next steps
17 How to get to data maturity Level 2
18 How to ask for funding
Data maturity level 2 Project 2
19 Data maturity Level 2
20 Intro to Project 2
21 The Crawl stage
22 The Walk stage
23 The Run stage
24 AB testing and AB test mechanics
25 Statistical significance and potential outcomes
26 The impact of sample size
27 Test power vs lift
28 How to get to data maturity Level 3
29 How to ask for funding
Data Maturity Level 3
30 Data maturity Level 3
31 Intro to Project 3
32 Exploring the dataset
33 Analyzing purchase rate across groups
34 Understand the business problem and specify your objectives
35 Explore data Create train and test dataset
36 Perform the analysis
Beyond data maturity Level 3
37 What to do to improve even further