Don’t give a number

Google pays its software engineers generously. While many of their employees are competent, they disregard a critical skill to maximize their pay.

Negotiation.

If Google employees have never negotiated, they're as helpless as anyone else.

In this piece, I'll reveal a compensation negotiation tip that will set you apart.

The Fallacy of Negotiating

How do you negotiate your salary? “Just give them a number twice the amount you really want”. - Someplace on the internet

Above is typical negotiation advice. If you ask for more than you want, the recruiter may meet you halfway.

It seems logical and great, but here's why you shouldn't follow that advice.

Haitian hostage rescue

In 1977, an official's aunt was kidnapped in Haiti. The kidnappers demanded $150,000 for the aunt's life. It seems reasonable until you realize why kidnappers want $150,000.

FBI detective and negotiator Chris Voss researched why they demanded so much.

“So they could party through the weekend”

When he realized their ransom was for partying, he offered $4,751 and a CD stereo. Criminals freed the aunt.

These thieves gave 31.57x their estimated amount and got a fraction. You shouldn't trust these thieves to negotiate your compensation.

What happened?

Negotiating your offer and Haiti

This narrative teaches you how to negotiate with a large number.

You can and will be talked down.

If a recruiter asks your wage expectation and you offer double, be ready to explain why.

If you can't justify your request, you may be offered less. The recruiter will notice and talk you down.

Reasonably,

• a tiny bit more than the present amount you earn

• a small premium over an alternative offer

• a little less than the role's allotted amount

Real-World Illustration

Recruiter: What’s your expected salary? Candidate: (I know the role is usually $100,000) $200,000 Recruiter: How much are you compensated in your current role? Candidate: $90,000 Recruiter: We’d be excited to offer you $95,000 for your experiences for the role.

So Why Do They Even Ask?

Recruiters ask for a number to negotiate a lower one. Asking yourself limits you.

You'll rarely get more than you asked for, and your request can be lowered.

The takeaway from all of this is to never give an expected compensation.

Tell them you haven't thought about it when you applied.

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.

Sometimes doing everything correctly isn't enough.

In 2020, I could fix my life.

After failing to start a business, I owed $40,000 and had no work.

A $1.2 billion startup on the cusp of going public pulled me up.

Ironically, it was getting ready for an epic fall — with the world watching.

Life sometimes helps. Without a base, even the strongest fall. A corporation that did everything right failed 3 months after going public.

First-row view.

Apple is the creator of Adore.

Out of respect, I've altered the company and employees' names in this account, despite their failure.

Although being a publicly traded company, it may become obvious.

We’ll call it “Adore” — a revolutionary concept in retail shopping.

Two Apple execs established Adore in 2014 with a focus on people-first purchasing.

Jon and Tim:

• The concept for the stylish Apple retail locations you see today was developed by retail expert Jon Swanson, who collaborated closely with Steve Jobs.

• Tim Cruiter is a graphic designer who produced the recognizable bouncing lamp video that appears at the start of every Pixar film.

The dynamic duo realized their vision.

“What if you could combine the convenience of online shopping with the confidence of the conventional brick-and-mortar store experience.”

Adore's mobile store concept combined traditional retail with online shopping.

Adore brought joy to 70+ cities and 4 countries over 7 years, including the US, Canada, and the UK.

Being employed on the ground floor, with world dominance and IPO on the horizon, was exciting.

I started as an Adore Expert.

I delivered cell phones, helped consumers set them up, and sold add-ons.

As the company grew, I became a Virtual Learning Facilitator and trained new employees across North America using Zoom.

In this capacity, I gained corporate insider knowledge. I worked with the creative team and Jon and Tim.

It's where I saw company foundation fissures. Despite appearances, investors were concerned.

The business strategy was ground-breaking.

Even after seeing my employee stocks fall from a home down payment to $0 (when Adore filed for bankruptcy), it's hard to pinpoint what went wrong.

Solid business model, well-executed.

Jon and Tim's chase for public funding ended in glory.

Here’s the business model in a nutshell:

Buying cell phones is cumbersome. You have two choices:

1. Online purchase: not knowing what plan you require or how to operate your device.

2. Enter a store, which can be troublesome and stressful.

Apple, AT&T, and Rogers offered Adore as a free delivery add-on. Customers could:

• Have their phone delivered by UPS or Canada Post in 1-2 weeks.

• Alternately, arrange for a person to visit them the same day (or sometimes even the same hour) to assist them set up their phone and demonstrate how to use it (transferring contacts, switching the SIM card, etc.).

Each Adore Expert brought a van with extra devices and accessories to customers.

Happy customers.

Here’s how Adore and its partners made money:

Adores partners appreciated sending Experts to consumers' homes since they improved customer satisfaction, average sale, and gadget returns.

**Telecom enterprises have low customer satisfaction. The average NPS is 30/100. Adore's global NPS was 80.

Adore made money by:

• a set cost for each delivery

• commission on sold warranties and extras

Consumer product applications seemed infinite.

A proprietary scheduling system (“The Adore App”), allowed for same-day, even same-hour deliveries.

It differentiates Adore.

They treated staff generously by:

• Options on stock

• health advantages

• sales enticements

• high rates per hour

Four-day workweeks were set by experts.

Being hired early felt like joining Uber, Netflix, or Tesla. We hoped the company's stocks would rise.

Exciting times.

I smiled as I greeted more than 1,000 new staff.

I spent a decade in retail before joining Adore. I needed a change.

After a leap of faith, I needed a lifeline. So, I applied for retail sales jobs in the spring of 2019.

The universe typically offers you what you want after you accept what you need. I needed a job to settle my debt and reach $0 again.

And the universe listened.

After being hired as an Adore Expert, I became a Virtual Learning Facilitator. Enough said.

After weeks of economic damage from the pandemic.

This employment let me work from home during the pandemic. It taught me excellent business skills.

I was active in brainstorming, onboarding new personnel, and expanding communication as we grew.

This job gave me vital skills and a regular paycheck during the pandemic.

It wasn’t until January of 2022 that I left on my own accord to try to work for myself again — this time, it’s going much better.

Adore was perfect. We valued:

• Connection

• Discovery

• Empathy

Everything we did centered on compassion, and we held frequent Justice Calls to discuss diversity and work culture.

The last day of onboarding typically ended in tears as employees felt like they'd found a home, as I had.

Like all nice things, the wonderful vibes ended.

First indication of distress

My first day at the workplace was great.

Fun, intuitive, and they wanted creative individuals, not salesman.

While sales were important, the company's vision was more important.

“To deliver joy through life-changing mobile retail experiences.”

Thorough, forward-thinking training. We had a module on intuition. It gave us role ownership.

We were flown cross-country for training, gave feedback, and felt like we made a difference. Multiple contacts responded immediately and enthusiastically.

The atmosphere was genuine.

Making money was secondary, though. Incredible service was a priority.

Jon and Tim answered new hires' questions during Zoom calls during onboarding. CEOs seldom meet new hires this way, but they seemed to enjoy it.

All appeared well.

But in late 2021, things started changing.

Adore's leadership changed after its IPO. From basic values to sales maximization. We lost communication and were forced to fend for ourselves.

Removed the training wheels.

It got tougher to gain instructions from those above me, and new employees told me their roles weren't as advertised.

External money-focused managers were hired.

Instead of creative types, we hired salespeople.

With a new focus on numbers, Adore's uniqueness began to crumble.

Via Zoom, hundreds of workers were let go.

So.

Early in 2022, mass Zoom firings were trending. A CEO firing 900 workers over Zoom went viral.

Adore was special to me, but it became a headline.

30 June 2022, Vice Motherboard published Watch as Adore's CEO Fires Hundreds.

It described a leaked video of Jon Swanson laying off all staff in Canada and the UK.

They called it a “notice of redundancy”.

The corporation couldn't pay its employees.

I loved Adore's underlying ideals, among other things. We called clients Adorers and sold solutions, not add-ons.

But, like anything, a company is only as strong as its weakest link. And obviously, the people-first focus wasn’t making enough money.

There were signs. The expansion was presumably a race against time and money.

Adore finally declared bankruptcy.

Adore declared bankruptcy 3 months after going public. It happened in waves, like any large-scale fall.

• Initial key players to leave were

• Then, communication deteriorated.

• Lastly, the corporate culture disintegrated.

6 months after leaving Adore, I received a letter in the mail from a Law firm — it was about my stocks.

Adore filed Chapter 11. I had to sue to collect my worthless investments.

I hoped those stocks will be valuable someday. Nope. Nope.

Sad, I sighed.

$1.2 billion firm gone.

I left the workplace 3 months before starting a writing business. Despite being mediocre, I'm doing fine.

I got up as Adore fell.

Finally, can we scale kindness?

I trust my gut. Changes at Adore made me leave before it sank.

Adores' unceremonious slide from a top startup to bankruptcy is astonishing to me.

The company did everything perfectly, in my opinion.

• first to market,

• provided excellent service

• paid their staff handsomely.

• was responsible and attentive to criticism

The company wasn't led by an egotistical eccentric. The crew had centuries of cumulative space experience.

I'm optimistic about the future of work culture, but is compassion scalable?

Technical books aren't like novels. We need a new approach to technical texts. I've spent years looking for a decent reading method. I tried numerous ways before finding one that worked. This post explains how I read technical books efficiently.

What Do I Mean When I Say Effective?

Effectiveness depends on the book. Effective implies I know where to find answers after reading a reference book. Effective implies I learned the book's knowledge after reading it.

I use reference books as tools in my toolkit. I won't carry all my tools; I'll merely need them. Non-reference books teach me techniques. I never have to make an effort to use them since I always have them.

Reference books I like:

• Design Patterns: Elements of Reusable Object-Oriented Software

• Refactoring: Improving the Design of Existing Code 
  You can also check My Top Takeaways from Refactoring here.

• Practical Object-Oriented Design in Ruby: An Agile Primer

Non-reference books I like:

• The Pragmatic Programmer: your journey to mastery

• Clean Code: A Handbook of Agile Software Craftsmans

The Approach

Technical books might be overwhelming to read in one sitting. Especially when you have no idea what is coming next as you read. When you don't know how deep the rabbit hole goes, you feel lost as you read. This is my years-long method for overcoming this difficulty.

Whether you follow the step-by-step guide or not, remember these:

• Understand the terminology. Make sure you get the meaning of any terms you come across more than once. The likelihood that a term will be significant increases as you encounter it more frequently.

• Know when to stop. I've always believed that in order to truly comprehend something, I must delve as deeply as possible into it. That, however, is not usually very effective. There are moments when you have to draw the line and start putting theory into practice (if applicable).

• Look over your notes. When reading technical books or documents, taking notes is a crucial habit to develop. Additionally, you must regularly examine your notes if you want to get the most out of them. This will assist you in internalizing the lessons you acquired from the book. And you'll see that the urge to review reduces with time.

Let's talk about how I read a technical book step by step.

0. Read the Foreword/Preface

These sections are crucial in technical books. They answer Who should read it, What each chapter discusses, and sometimes How to Read? This is helpful before reading the book. Who could know the ideal way to read the book better than the author, right?

1. Scanning

I scan the chapter. Fast scanning is needed.

• I review the headings.

• I scan the pictures quickly.

• I assess the chapter's length to determine whether I might divide it into more manageable sections.

2. Skimming

Skimming is faster than reading but slower than scanning.

• I focus more on the captions and subtitles for the photographs.

• I read each paragraph's opening and closing sentences.

• I examined the code samples.

• I attempt to grasp each section's basic points without getting bogged down in the specifics.

• Throughout the entire reading period, I make an effort to make mental notes of what may require additional attention and what may not. Because I don't want to spend time taking physical notes, kindly notice that I am using the term "mental" here. It is much simpler to recall. You may think that this is more significant than typing or writing “Pay attention to X.”

• I move on quickly. This is something I considered crucial because, when trying to skim, it is simple to start reading the entire thing.

3. Complete reading

Previous steps pay off.

• I finished reading the chapter.

• I concentrate on the passages that I mentally underlined when skimming.

• I put the book away and make my own notes. It is typically more difficult than it seems for me. But it's important to speak in your own words. You must choose the right words to adequately summarize what you have read. How do those words make you feel? Additionally, you must be able to summarize your notes while you are taking them. Sometimes as I'm writing my notes, I realize I have no words to convey what I'm thinking or, even worse, I start to doubt what I'm writing down. This is a good indication that I haven't internalized that idea thoroughly enough.

• I jot my inquiries down. Normally, I read on while compiling my questions in the hopes that I will learn the answers as I read. I'll explore those issues more if I wasn't able to find the answers to my inquiries while reading the book.

Bonus!

Best part: If you take lovely notes like I do, you can publish them as a blog post with a few tweaks.

Conclusion

This is my learning journey. I wanted to show you. This post may help someone with a similar learning style. You can alter the principles above for any technical material.

And why we must keep arming Ukraine

Russian expatriates write about horrific news from home.

Read this from Nadin Brzezinski. She's not a native English speaker, so there are grammar errors, but her tale smells true.

Terrible truth.

There's much more that reveals Russia's grim reality.

Non-leadership. Millions of missing supplies are presumably sold for profit, leaving untrained troops without food or gear. Missile attacks pause because they run out. Fake schemes to hold talks as a way of stalling while they scramble for solutions.

Street men were mobilized. Millions will be ground up to please a crazed despot. Fear, wrath, and hunger pull apart civilization.

It's the most dystopian story, but Ukraine is worse. Destruction of a society, country, and civilization. Only the invaders' corruption and incompetence save the Ukrainians.

Rochester, NY. My suburb had many Soviet-era Ukrainian refugees. Their kids were my classmates. Fifty years later, many are still my friends. I loved their food and culture. My town has 20,000 Ukrainians.

Grieving but determined. They don't quit. They won't quit. Russians are eternal enemies.

It's the Russian people's willingness to tolerate corruption, abuse, and stupidity by their leaders. They are paying. 65000 dead. Ruined economy. No freedom to speak. Americans do not appreciate that freedom as we should.

It lets me write/publish.

Russian friends are shocked. Many are here because their parents escaped Russian anti-semitism and authoritarian oppression. A Russian cultural legacy says a strongman's methods are admirable.

A legacy of a slavery history disguised as serfdom. Peasants and Princes.

Read Tolstoy. Then Anna Karenina. The main characters are princes and counts, whose leaders are incompetent idiots with wealth and power.

Peasants who die in their wars due to incompetence are nameless ciphers.

Sound familiar?

Decentralizing Web3's soul

Our tech must reflect our non-transactional connections. Web3 arose from a lack of social links. It must strengthen these linkages to get widespread adoption. Soulbound NFTs help.

This NFT creates digital proofs of our social ties. It embodies G. Simmel's idea of identity, in which individuality emerges from social groups, just as social groups evolve from people.

It's multipurpose. First, gather online our distinctive social features. Second, highlight and categorize social relationships between entities and people to create a spiderweb of networks.

1. 🌐 Reducing online manipulation: Only socially rich or respectable crypto wallets can participate in projects, ensuring that no one can create several wallets to influence decentralized project governance.

2. 🤝 Improving social links: Some sectors of society lack social context. Racism, sexism, and homophobia do that. Public wallets can help identify and connect distinct social groupings.

3. 👩‍❤️‍💋‍👨 Increasing pluralism: Soulbound tokens can ensure that socially connected wallets have less voting power online to increase pluralism. We can also overweight a minority of numerous voices.

4. 💰Making more informed decisions: Taking out an insurance policy requires a life review. Why not loans? Character isn't limited by income, and many people need a chance.

5. 🎶 Finding a community: Soulbound tokens are accessible to everyone. This means we can find people who are like us but also different. This is probably rare among your friends and family.

NFTs are dangerous, and I don't like them. Social credit score, privacy, lost wallet. We must stay informed and keep talking to innovators.

E. Glen Weyl, Puja Ohlhaver and Vitalik Buterin get all the credit for these ideas, having written the very accessible white paper “Decentralized Society: Finding Web3’s Soul”.