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.

Calmness is a vital life skill.

It aids communication. It boosts creativity and performance.

I've studied calm people's habits for years. Commonalities:

Have learned to laugh at themselves.

Those who have something to protect can’t help but make it a very serious business, which drains the energy out of the room.

They are fixated on positive pursuits like making cool things, building a strong physique, and having fun with others rather than on depressing influences like the news and gossip.

Every day, spend at least 20 minutes moving, whether it's walking, yoga, or lifting weights.

Discover ways to take pleasure in life's challenges.

Since perspective is malleable, they change their view.

Set your own needs first.

Stressed people neglect themselves and wonder why they struggle.

Prioritize self-care.

Don't ruin your life to please others.

Make something.

Calm people create more than react.

They love creating beautiful things—paintings, children, relationships, and projects.

Hold your breath, please.

If you're stressed or angry, you may be surprised how much time you spend holding your breath and tightening your belly.

Release, breathe, and relax to find calm.

Stopped rushing.

Rushing is disadvantageous.

Calm people handle life better.

Are attuned to their personal dietary needs.

They avoid junk food and eat foods that keep them healthy, happy, and calm.

Don’t take anything personally.

Stressed people control everything.

Self-conscious.

Calm people put others and their work first.

Keep their surroundings neat.

Maintaining an uplifting and clutter-free environment daily calms the mind.

Minimise negative people.

Calm people are ruthless with their boundaries and avoid negative and drama-prone people.

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.

DATA MINING WITH SUPERALGORES

Old pots produce the best soup.

Science has always inspired indicator design. From physics to signal processing, many indicators use concepts from mechanical engineering, electronics, and probability. In Superalgos' Data Mining section, we've explored using thermodynamics and information theory to construct indicators and using statistical and probabilistic techniques like reduced normal law to take advantage of low probability events.

An asset's price is like a mechanical object revolving around its moving average. Using this approach, we could design an indicator using the oscillator's Total Energy. An oscillator's energy is finite and constant. Since we don't expect the price to follow the harmonic oscillator, this energy should deviate from the perfect situation, and the maximum of divergence may provide us valuable information on the price's moving average.

Definition of the Harmonic Oscillator in Few Words

Sinusoidal function describes a harmonic oscillator. The time-constant energy equation for a harmonic oscillator is:

With

Time saves energy.

In a mechanical harmonic oscillator, total energy equals kinetic energy plus potential energy. The formula for energy is the same for every kind of harmonic oscillator; only the terms of total energy must be adapted to fit the relevant units. Each oscillator has a velocity component (kinetic energy) and a position to equilibrium component (potential energy).

The Price Oscillator and the Energy Formula

Considering the harmonic oscillator definition, we must specify kinetic and potential components for our price oscillator. We define oscillator velocity as the rate of change and equilibrium position as the price's distance from its moving average.

Price kinetic energy:

It's like:

With

and

L is the number of periods for the rate of change calculation and P for the close price EMA calculation.

Total price oscillator energy =

Given that an asset's price can theoretically vary at a limitless speed and be endlessly far from its moving average, we don't expect this formula's outcome to be constrained. We'll normalize it using Z-Score for convenience of usage and readability, which also allows probabilistic interpretation.

Over 20 periods, we'll calculate E's moving average and standard deviation.

We calculated Z on BTC/USDT with L = 10 and P = 21 using Knime Analytics.

The graph is detrended. We added two horizontal lines at +/- 1.6 to construct a 94.5% probability zone based on reduced normal law tables. Price cycles to its moving average oscillate clearly. Red and green arrows illustrate where the oscillator crosses the top and lower limits, corresponding to the maximum/minimum price oscillation. Since the results seem noisy, we may apply a non-lagging low-pass or multipole filter like Butterworth or Laguerre filters and employ dynamic bands at a multiple of Z's standard deviation instead of fixed levels.

Kinetic Detrender Implementation in Superalgos

The Superalgos Kinetic detrender features fixed upper and lower levels and dynamic volatility bands.

The code is pretty basic and does not require a huge amount of code lines.

It starts with the standard definitions of the candle pointer and the constant declaration :

let candle = record.current
let len = 10
let P = 21
let T = 20
let up = 1.6
let low = 1.6

Upper and lower dynamic volatility band constants are up and low.

We proceed to the initialization of the previous value for EMA :

if (variable.prevEMA === undefined) {
    variable.prevEMA = candle.close
}

And the calculation of EMA with a function (it is worth noticing the function is declared at the end of the code snippet in Superalgos) :

variable.ema = calculateEMA(P, candle.close, variable.prevEMA)
//EMA calculation
function calculateEMA(periods, price, previousEMA) {
    let k = 2 / (periods + 1)
    return price * k + previousEMA * (1 - k)
}

The rate of change is calculated by first storing the right amount of close price values and proceeding to the calculation by dividing the current close price by the first member of the close price array:

variable.allClose.push(candle.close)
if (variable.allClose.length > len) {
    variable.allClose.splice(0, 1)
}
if (variable.allClose.length === len) {
    variable.roc = candle.close / variable.allClose[0]
} else {
    variable.roc = 1
}

Finally, we get energy with a single line:

variable.E = 1 / 2 * len * variable.roc + 1 / 2 * P * candle.close / variable.ema

The Z calculation reuses code from Z-Normalization-based indicators:

variable.allE.push(variable.E)
if (variable.allE.length > T) {
    variable.allE.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allE.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allE[i]
    }
    variable.MA = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allE[i] - variable.MA, 2)
    }
    variable.sigma = Math.sqrt(variable.SQ / T)
variable.Z = (variable.E - variable.MA) / variable.sigma
} else {
    variable.Z = 0
}
variable.allZ.push(variable.Z)
if (variable.allZ.length > T) {
    variable.allZ.splice(0, 1)
}
variable.sum = 0
variable.SQ = 0
if (variable.allZ.length === T) {
    for (var i = 0; i < T; i++) {
        variable.sum += variable.allZ[i]
    }
    variable.MAZ = variable.sum / T
for (var i = 0; i < T; i++) {
        variable.SQ += Math.pow(variable.allZ[i] - variable.MAZ, 2)
    }
    variable.sigZ = Math.sqrt(variable.SQ / T)
} else {
    variable.MAZ = variable.Z
    variable.sigZ = variable.MAZ * 0.02
}
variable.upper = variable.MAZ + up * variable.sigZ
variable.lower = variable.MAZ - low * variable.sigZ

We also update the EMA value.

variable.prevEMA = variable.EMA

Conclusion

We showed how to build a detrended oscillator using simple harmonic oscillator theory. Kinetic detrender's main line oscillates between 2 fixed levels framing 95% of the values and 2 dynamic levels, leading to auto-adaptive mean reversion zones.

Superalgos' Normalized Momentum data mine has the Kinetic detrender indication.

All the material here can be reused and integrated freely by linking to this article and Superalgos.

This post is informative and not financial advice. Seek expert counsel before trading. Risk using this material.

Is iPhone's Dynamic Island invention or a marketing ploy?

First of all, why the notch?

When Apple debuted the iPhone X with the notch, some were surprised, confused, and amused by the goof. Let the Brits keep the new meaning of top-notch.

Apple removed the bottom home button to enhance screen space. The tides couldn't overtake part of the top. This section contained sensors, a speaker, a microphone, and cameras for facial recognition. A town resisted Apple's new iPhone design.

From iPhone X to 13, the notch has gotten smaller. We expected this as technology and engineering progressed, but we hated the notch. Apple approved. They attached it to their other gadgets.

Apple accepted, owned, and ran with the iPhone notch, it has become iconic (or infamous); and that’s intentional.

The Island Where Apple Is

Apple needs to separate itself, but they know how to do it well. The iPhone 14 Pro finally has us oohing and aahing. Life-changing, not just higher pixel density or longer battery.

Dynamic Island turned a visual differentiation into great usefulness, which may not be life-changing. Apple always welcomes the controversy, whether it's $700 for iMac wheels, no charging block with a new phone, or removing the headphone jack.

Apple knows its customers will be loyal, even if they're irritated. Their odd design choices often cause controversy. It's calculated that people blog, review, and criticize Apple's products. We accept what works for them.

While the competition zigs, Apple zags. Sometimes they zag too hard and smash into a wall, but we talk about it anyways, and that’s great publicity for them.

Getting Dependent on the drug

The notch became a crop. Dynamic Island's design is helpful, intuitive, elegant, and useful. It increases iPhone usability, productivity (slightly), and joy. No longer unsightly.

The medication helps with multitasking. It's a compact version of the iPhone's Live Activities lock screen function. Dynamic Island enhances apps and activities with visual effects and animations whether you engage with it or not. As you use the pill, its usefulness lessens. It lowers user notifications and consolidates them with live and permanent feeds, delivering quick app statuses. It uses the black pixels on the iPhone 14's display, which looked like a poor haircut.

The pill may be a gimmick to entice customers to use more Apple products and services. Apps may promote to their users like a live billboard.

Be prepared to get a huge dose of Dynamic Island’s “pill” like you never had before with the notch. It might become so satisfying and addicting to use, that every interaction with it will become habit-forming, and you’re going to forget that it ever existed.

WARNING: A Few Potential Side Effects

Vision blurred Dynamic Island's proximity to the front-facing camera may leave behind grease that blurs photos. Before taking a selfie, wipe the camera clean.

Strained thumb To fully use Dynamic Island, extend your thumb's reach 6.7 inches beyond your typical, comfortable range.

Happiness, contentment The Dynamic Island may enhance Endorphins and Dopamine. Multitasking, interactions, animations, and haptic feedback make you want to use this function again and again.

Motion-sickness Dynamic Island's motions and effects may make some people dizzy. If you can disable animations, you can avoid motion sickness.

I'm not a doctor, therefore they aren't established adverse effects.

Does Dynamic Island Include Multiple Tasks?

Dynamic Islands is a placebo for multitasking. Apple might have compromised on iPhone multitasking. It won't make you super productive, but it's a step up.

iPhone is primarily for personal use, like watching videos, messaging friends, sending money to friends, calling friends about the money you were supposed to send them, taking 50 photos of the same leaf, investing in crypto, driving for Uber because you lost all your money investing in crypto, listening to music and hailing an Uber from a deserted crop field because while you were driving for Uber your passenger stole your car and left you stranded, so you used Apple’s new SOS satellite feature to message your friend, who still didn’t receive their money, to hail you an Uber; now you owe them more money… karma?

We won't be watching videos on iPhones while perusing 10,000-row spreadsheets anytime soon. True multitasking and productivity aren't priorities for Apple's iPhone. Apple doesn't to preserve the iPhone's experience. Like why there's no iPad calculator. Apple doesn't want iPad users to do math, but isn't essential for productivity?

Digressing.

Apple will block certain functions so you must buy and use their gadgets and services, immersing yourself in their ecosystem and dictating how to use their goods.

Dynamic Island is a poor man’s multi-task for iPhone, and that’s fine it works for most iPhone users. For substantial productivity Apple prefers you to get an iPad or a MacBook. That’s part of the reason for restrictive features on certain Apple devices, but sometimes it’s based on principles to preserve the integrity of the product, according to Apple’s definition.

Is Apple using deception?

Dynamic Island may be distracting you from a design decision. The answer is kind of. Elegant distraction

When you pull down a smartphone webpage to refresh it or minimize an app, you get seamless animations. It's not simply because it appears better; it's due to iPhone and smartphone processing speeds. Such limits reduce the system's response to your activity, slowing the experience. Designers and developers use animations and effects to distract us from the time lag (most of the time) and sometimes because it looks cooler and smoother.

Dynamic Island makes apps more useable and interactive. It shows system states visually. Turn signal audio and visual cues, voice assistance, physical and digital haptic feedbacks, heads-up displays, fuel and battery level gauges, and gear shift indicators helped us overcome vehicle design problems.

Dynamic Island is a wonderfully delightful (and temporary) solution to a design “problem” until Apple or other companies can figure out a way to sink the cameras under the smartphone screen.

Apple Has Returned to Being an Innovative & Exciting Company

Now Apple's products are exciting. Next, bring back real Apple events, not pre-recorded demos.

Dynamic Island integrates hardware and software. What will this new tech do? How would this affect device use? Or is it just hype?

Dynamic Island may be an insignificant improvement to the iPhone, but it sure is promising for the future of bridging the human and computer interaction gap.

OpenAI's ChatGPT is an amazing AI-powered chatbot. It can write poetry, fiction, and marketing ideas. I was impressed how well ChatGPT followed the interview, albeit there were a few classics “I’m afraid I can’t do that”.

Full interview below.

Me: I'm your interviewer. We'll discuss crypto. Thanks for being interviewed. What do you think about cryptocurrency?

CGPT: Cryptocurrency is an innovative technology that could transform money and financial activities. It might boost the efficiency and security of financial institutions and let consumers deal directly without banks. Cryptocurrency is still a new and untested technology with many unknowns and hurdles before it can be broadly accepted.

Me: Over 22,000 cryptocurrencies exist, and many have little use. We need how much cryptocurrencies?

CGPT: The appropriate quantity will rely on a multitude of factors, including each cryptocurrency's use cases, adoption rate, and market rivalry. Only a few cryptocurrencies will likely receive widespread adoption and be successful. Many existing cryptocurrencies may not survive since they can't deliver new or valued solutions or compete with more established cryptocurrencies.

Me: Will cryptocurrencies grow? What's next for worldwide adoption?

CGPT: It's hard to forecast the future of cryptocurrencies because the acceptance of new technologies is unpredictable. I believe cryptocurrencies will expand and acquire popularity in the future years. Regulatory ambiguity, security concerns, and scalability issues must be overcome for this to happen. Cryptocurrency companies and organizations must engage together with governments and authorities to address these obstacles and establish a growth-friendly climate. Continued innovation and improvement of the technology, including public education and awareness, will drive acceptance.

Me: Do bear markets affect bitcoin adoption?

CGPT: On the one hand, a bear market might induce uncertainty and hesitation among potential consumers. Bear markets can present opportunities for long-term investors who can acquire cryptocurrencies at cheaper prices and are more likely to hang onto their assets. A bear market can also test the tenacity and power of the underlying technology and community, which can boost long-term confidence and adoption. A bear market might have profound and nuanced effects on bitcoin adoption.

Me: What are some likely outcomes if Bitcoin reaches $100,000 by next year?

CGPT: If Bitcoin saw rapid growth, it would undoubtedly have repercussions for the cryptocurrency market and economy. Possible scenarios include increased public interest and awareness of cryptocurrency, a surge in demand for Bitcoin and other cryptocurrencies, and a growth in the cryptocurrency industry's market value. By shifting their focus and cash to the cryptocurrency market, investors and traders may also affect other asset classes, such as traditional equities and commodities. Governments and financial organizations may focus more on the cryptocurrency sector as they try to grasp Bitcoin's rapid rise.

Me: Will other countries accept crypto as currency?

CGPT: It's hard to say if additional governments will adopt cryptocurrencies as legal tender. Some countries have recognized the promise of bitcoin and are exploring its use, while others are cautious. Adoption of bitcoin as legal cash may vary by country, based on financial infrastructure, rules, and political atmosphere. As cryptocurrency technology evolves and becomes more accepted, more governments may embrace it as legal tender.

Me: Final question: will the recent cryptocurrency hacks affect long term adoption?

CGPT: Hacks might tarnish cryptocurrency's reputation and public faith in its security, the technology underpinning it is continually improving. Many cryptocurrency projects have developed strong security mechanisms to prevent hacks and other dangers, and these safeguards will undoubtedly improve over time. The decentralized nature of cryptocurrency makes it more resistant to attacks. Hacks and other security breaches are a concern, but they may not affect cryptocurrency adoption.