As a data scientist, you may know how crucial it is to
have a strong product sense, especially if you are working
in a product data scientist or data analytics role.
If you are new to this field
you may be working on developing your product sense
but how do you develop product sense?
Well, the answer is simple.
You can start by reading books that help you bridge the gap
between your technical skills
and your ability to speak the language of the product.
In this video
I want to recommend three books that are suitable
for both beginners and seasoned data scientists.
These books are not your typical data scientist books
but they are incredibly valuable
in building your product sense
and helping you excel in your job.
The first two books provide a wealth
of product knowledge while the third one is highly
technical and can benefit any data scientist.
They will help you understand the importance of metrics
the role of product managers
and how to use data to drive business growth.
So if you are ready to take your product sense
to the next level, let's dive
into the three books that I highly recommend.
The first book I like to recommend is Lean Analytics
by Alistair Croll and Benjamin Yoskovitz.
Do you like to read books that are packed with key studies
and insights from over a hundred business experts?
If so, this book is a great choice.
This book is actually written for entrepreneurs
but it can be super helpful for data scientists too.
So how can data scientists effectively communicate
with stakeholders according to Lean Analytics?
Well, the second part
of the book called Finding the Right Metric
for Right Now provides a practical framework to measure
and optimize business operations for data-driven growth.
It covers commonly used analytical frameworks
and emphasizes the discipline
of one metric that matters to define metrics for success
and communicate them effectively to stakeholders.
It also explains how analytics can be applied to businesses
of different stages and types.
This book also covers when to use metrics such
as customer acquisition, retention, revenue, and virality
to measure success and optimize products and marketing.
This is especially useful for data scientists to
understand the specific needs of different businesses.
In the third part Line in the Sand, the authors
discuss several business models such as e-commerce, SaaS
mobile first businesses, and two-sided marketplaces.
It's all discussed in the context of Lean Analytics
which is a data driven approach to developing
and scaling a business.
On top of that, the authors provide case studies
of businesses that have successfully used analytics to grow
and scale their operations.
The examples offer practical applications which data
scientists can adapt to their own work.
Overall, I find Lean Analytics to be a super helpful book
in building my knowledge of metrics and understanding how
data scientists can contribute to the growth of a business.
The case studies were especially interesting and informative
for me.
So if you are a data scientist looking to build your product
sense and understand how to use data to contribute to
the success of a business
I highly recommend Lean Analytics.
This is an excellent resource that can help bridge the gap
between technical skills and the ability to
effectively communicate about the product.
Another book I found incredibly helpful
in building my product sense as a data scientist
is Cracking the Product Manager Interview
by Gayle Laakmann McDowell, and Jackie Bavaro.
This book is a valuable resource that provides guidance
and strategies for landing a job as a product manager.
Now you might be thinking, why should I care
about the book on product management as a data scientist?
Well, let me tell you
understanding product management is crucial in this field.
Firstly, data scientists work closely
with product managers or PMs.
They are key stakeholders and decision makers
when it comes to defining the product roadmap
and identifying opportunities for improvement.
Having a strong understanding
of product management can help you communicate more
effectively with PMs and make more informed decisions.
Secondly, many companies have PMs interview data scientists.
This is because PMs are often responsible
for overseeing the development of new products and features.
So they need to make sure that data scientists they work
with have a good understanding of the product and its goals.
That's why it's important that you can speak their language
and understand their perspective. With 16 chapters
the book covers a wide range of topics
including product manager responsibilities
how to gain the right experience
crafting an effective resume and honing technical skills.
The authors provide practical tips
and strategies for answering common interview
questions as well as case study examples to
help readers enhance their problem solving skills.
One of the things I found most helpful about this book is
in chapters 14 and 15 where it provides analytical
and metric frameworks such as the 5Cs framework
and the marketing mix to analyze
and answer some case questions.
This can be highly valuable
for data scientists who want to build their product sense
and understand how to use data to contribute
to the success of a product.
In conclusion, Cracking the Product Manager
Interview is an excellent resource for any data
scientist who wants to build their product knowledge
and understand how to work effectively
with product managers.
Now that you've got a solid understanding
of product knowledge from the previous books
let's dive into A/B testing a powerful tool used
by companies to make data-driven decisions.
So what's the role
of a data scientist in A/B testing? In this field
you likely play a crucial part in this process.
From defining metrics for success to analyzing results
and communicating insights to stakeholders
you will be responsible
for ensuring that the experiment is properly
designed to address the businesses questions and
that the results are reliable and practically significant.
Additionally, you may need to perform data cleaning
and processing to ensure accuracy and completeness
but perhaps most importantly
a data scientist needs to be able to interpret
and communicate results to stakeholders effectively.
They need to work closely with product managers, engineers
and other stakeholders to ensure
that the insights from the experiment are being
used to drive business decisions.
So a data scientist's role requires a deep understanding
of statistical concepts
and the ability to communicate effectively with others.
While the previous books I recommended offer
practical knowledge and transferrable skills that apply
to your role as a data scientist
the next book is the most technical of the three.
I had to read it multiple times to fully
understand the technicalities, so don't worry
if you don't get everything on the first try.
It's a book you can keep coming back to.
It's highly technical, but it's also very valuable.
The book I want to recommend is Trustworthy
Online Controlled Experiments by three co-authors
and all of them are industry experts.
It's a valuable resource
for anyone looking to build their A/B testing knowledge
and acquire the practical skills needed to succeed
in a data scientist role.
It is my go-to book for AB testing.
While you can learn statistics and machine learning
in school, you probably won't learn AB testing.
That's why this book is a must have
for every data scientist
and here are some benefits this book offers.
You'll understand how A/B testing works under the hood even
if you don't have any actual experience in A/B testing.
This makes it especially helpful
if you are new to this field.
Even if you are an experienced data scientist
you can still learn a lot
of practical knowledge from this book, such
as best practices in the industry and the solutions
to real problems data scientists face in their jobs.
When you have conversations with colleagues and work
on real problems in practice, you can refer back
to the book when faced with problems you remember reading
about. Now, let's dive into what this book is all about.
It provides a comprehensive guide to conducting A/B tests,
also known as online experiments, in a reliable
and ethical manner.
The book is divided into five parts, Introductory Topics
for Everyone, Selected Topics for Everyone, Complimentary
and Alternative Techniques to Controlled Experiments,
Advanced Topics for Building an Experimentation Platform,
and Advanced Topics for Analyzing Experiments.
In the first two parts, the authors provide a comprehensive
introduction to the fundamentals of online experimentation.
They cover the importance of experiments, how
they are conducted, and the statistical principles
that underpin them.
Additionally, the authors supplement the theory
with real world case studies, providing practical examples
that illustrate the concepts discussed.
In the third part, the authors present a range
of complimentary techniques to online experiments.
These techniques include logs-based analysis, user experience
research, and observational causal studies
which can augment online experiments to provide more
comprehensive insights.
In the last two parts
the authors explore advanced topics in online
experimentation such as hypothesis testing and A/A tests.
They also provide solutions for common challenges such
as multiple testing and the leakage between variants.
One of the book's greatest things is its adaptability
to different audiences, including data scientists
data science managers, product managers, and engineers.
The authors have thoughtfully organized the content to
make it easy to navigate
and find the information that is most relevant to each role.
For instance, data scientists can start
with chapters specifically tailored to their needs.
Overall Trustworthy
Online Controlled Experiments is an essential resource
for anyone seeking to expand their knowledge of A/B testing
and acquire practical skills to excel in this area.
Now, to wrap up these three books provide valuable insights
and practical knowledge
for data scientists looking to build their product sense
and make data-driven decisions.
By understanding product management, lean analytics
and A/B testing, you can connect your technical expertise
with product insights, which will allow you to
better communicate with your team and drive business impact.
Remember, building your product sense takes time and effort
but it's a crucial skill for data scientists to have.
So keep reading and learning and you will be well
on your way to becoming a valuable asset to any team.
If you have any questions or book recommendations
of your own, feel free to leave them in the comments below.
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Thanks for watching, and I will see you in the next video.