Market fraud

Due to its decentralized and fragmented character, the crypto market has integrity difficulties.

Cryptocurrencies are an immature sector, therefore market manipulation becomes a bigger issue. Many research have attempted to uncover these abuses. CryptoCompare's newest one highlights some of the industry's most typical scams.

Why are these concerns so common in the crypto market? First, even the largest centralized exchanges remain unregulated due to industry immaturity. A low-liquidity market segment makes an attack more harmful. Finally, market surveillance solutions not implemented reduce transparency.

In CryptoCompare's latest exchange benchmark, 62.4% of assessed exchanges had a market surveillance system, although only 18.1% utilised an external solution. To address market integrity, this measure must improve dramatically. Before discussing the report's malpractices, note that this is not a full list of attacks and hacks.

Clean Trading

An investor buys and sells concurrently to increase the asset's price. Centralized and decentralized exchanges show this misconduct. 23 exchanges have a volume-volatility correlation < 0.1 during the previous 100 days, according to CryptoCompares. In August 2022, Exchange A reported $2.5 trillion in artificial and/or erroneous volume, up from $33.8 billion the month before.

Spoofing

Criminals create and cancel fake orders before they can be filled. Since manipulators can hide in larger trading volumes, larger exchanges have more spoofing. A trader placed a 20.8 BTC ask order at $19,036 when BTC was trading at $19,043. BTC declined 0.13% to $19,018 in a minute. At 18:48, the trader canceled the ask order without filling it.

Front-Running

Most cryptocurrency front-running involves inside trading. Traditional stock markets forbid this. Since most digital asset information is public, this is harder. Retailers could utilize bots to front-run.

CryptoCompare found digital wallets of people who traded like insiders on exchange listings. The figure below shows excess cumulative anomalous returns (CAR) before a coin listing on an exchange.

Finally, LAYERING is a sequence of spoofs in which successive orders are put along a ladder of greater (layering offers) or lower (layering bids) values. The paper concludes with recommendations to mitigate market manipulation. Exchange data transparency, market surveillance, and regulatory oversight could reduce manipulative tactics.

A TECHNICAL THOUGHT

The IT giant is seeding the digital Great Renaissance.

Recently, technology has been dull.

We're still fascinated by processing speeds. Wearables are no longer an engineer's dream.

Apple has been quiet and avoided huge announcements. Slowness speaks something. Everything in the spaceship HQ seems to be turning slowly, unlike competitors around buzzwords.

Is this a sign of the impending storm?

Metas stock has fallen while Google milks dumb people. Microsoft steals money from corporations and annexes platforms like Linkedin.

Just surface bubbles?

Is Apple, one of the technology continents, pushing against all others to create a paradigm shift?

The fundamental human right to privacy

Apple's unusual remarks emphasize privacy. They incorporate it into their business models and judgments.

Apple believes privacy is a human right. There are no compromises.

This makes it hard for other participants to gain Apple's ecosystem's efficiencies.

Other players without hardware platforms lose.

Apple delivers new kidneys without rejection, unlike other software vendors. Nothing compromises your privacy.

Corporate citizenship will become more popular.

Apples have full coffers. They've started using that flow to better communities, which is great.

Apple's $2.5B home investment is one example. Google and Facebook are building or proposing to build workforce housing.

Apple's funding helps marginalized populations in more than 25 California counties, not just Apple employees.

Is this a trend, and does Apple keep giving back? Hope so.

I'm not cynical enough to suspect these investments have malicious motives.

The last frontier is the environment.

Climate change is a battle-to-win.

Long-term winners will be companies that protect the environment, turning climate change dystopia into sustainable growth.

Apple has been quietly changing its supply chain to be carbon-neutral by 2030.

“Apple is dedicated to protecting the planet we all share with solutions that are supporting the communities where we work.” Lisa Jackson, Apple’s vice president of environment.

Apple's $4.7 billion Green Bond investment will produce 1.2 gigawatts of green energy for the corporation and US communities. Apple invests $2.2 billion in Europe's green energy. In the Philippines, Thailand, Nigeria, Vietnam, Colombia, Israel, and South Africa, solar installations are helping communities obtain sustainable energy.

Apple is already carbon neutral today for its global corporate operations, and this new commitment means that by 2030, every Apple device sold will have net zero climate impact. -Apple.

Apple invests in green energy and forests to reduce its paper footprint in China and the US. Apple and the Conservation Fund are safeguarding 36,000 acres of US working forest, according to GreenBiz.

Apple's packaging paper is recycled or from sustainably managed forests.

What matters is the scale.

$1 billion is a rounding error for Apple.

These small investments originate from a tree with deep, spreading roots.

Apple's genes are anchored in building the finest products possible to improve consumers' lives.

I felt it when I switched to my iPhone while waiting for a train and had to pack my Macbook. iOS 16 dictation makes writing more enjoyable. Small change boosts productivity. Smooth transition from laptop to small screen and dictation.

Apples' tiny, well-planned steps have great growth potential for all consumers in everything they do.

There is clearly disruption, but it doesn't have to be violent

Digital channels, methods, and technologies have globalized human consciousness. One person's responsibility affects many.

Apple gives us tools to be privately connected. These technologies foster creativity, innovation, fulfillment, and safety.

Apple has invented a mountain of technologies, services, and channels to assist us adapt to the good future or combat evil forces who cynically aim to control us and ruin the environment and communities. Apple has quietly disrupted sectors for decades.

Google, Microsoft, and Meta, among others, should ride this wave. It's a tsunami, but it doesn't have to be devastating if we care, share, and cooperate with political decision-makers and community leaders worldwide.

A fresh Renaissance

Renaissance geniuses Michelangelo and Da Vinci. Different but seeing something no one else could yet see. Both were talented in many areas and could discover art in science and science in art.

These geniuses exemplified a period that changed humanity for the better. They created, used, and applied new, valuable things. It lives on.

Apple is a digital genius orchard. Wozniak and Jobs offered us fertile ground for the digital renaissance. We'll build on their legacy.

We may put our seeds there and see them bloom despite corporate greed and political ignorance.

I think the coming tsunami will illuminate our planet like the Renaissance.

Consider yourself lucky if your last FAANG interview was rejected.

FAANG—Facebook, Apple, Amazon, Netflix, Google

(I know its manga now, but watch me not care)

These big companies offer many benefits.

1. large salaries and benefits

2. Prestige

3. high expectations for both you and your coworkers.

However, these jobs may have major drawbacks that only become apparent when you're thrown to the wolves, so it's up to you whether you see them as drawbacks or opportunities.

I know most college graduates start working at big tech companies because of their perceived coolness.

I've worked in these companies for years and can tell you what to expect if you get a job here.

Little fish in a vast ocean

The most obvious. Most billion/trillion-dollar companies employ thousands.

You may work on a small, unnoticed product part.

Directors and higher will sometimes make you redo projects they didn't communicate well without respecting your time, talent, or will to work on trivial stuff that doesn't move company needles.

Peers will only say, "Someone has to take out the trash," even though you know company resources are being wasted.

The power imbalance is frustrating.

What you can do about it

Know your WHY. Consider long-term priorities. Though riskier, I stayed in customer-facing teams because I loved building user-facing products.

This increased my impact. However, if you enjoy helping coworkers build products, you may be better suited for an internal team.

I told the Directors and Vice Presidents that their actions could waste Engineering time, even though it was unpopular. Some were receptive, some not.

I kept having tough conversations because they were good for me and the company.

However, some of my coworkers praised my candor but said they'd rather follow the boss.

An outdated piece of technology can take years to update.

Apple introduced Swift for iOS development in 2014. Most large tech companies adopted the new language after five years.

This is frustrating if you want to learn new skills and increase your market value.

Knowing that my lack of Swift practice could hurt me if I changed jobs made writing verbose Objective C painful.

What you can do about it

1. Work on the new technology in side projects; one engineer rewrote the Lyft app in Swift over the course of a weekend and promoted its adoption throughout the entire organization.

2. To integrate new technologies and determine how to combine legacy and modern code, suggest minor changes to the existing codebase.

Most managers spend their entire day in consecutive meetings.

After their last meeting, the last thing they want is another meeting to discuss your career goals.

Sometimes a manager has 15-20 reports, making it hard to communicate your impact.

Misunderstandings and stress can result.

Especially when the manager should focus on selfish parts of the team. Success won't concern them.

What you can do about it

1. Tell your manager that you are a self-starter and that you will pro-actively update them on your progress, especially if they aren't present at the meetings you regularly attend.

2. Keep being proactive and look for mentorship elsewhere if you believe your boss doesn't have enough time to work on your career goals.

3. Alternately, look for a team where the manager has more authority to assist you in making career decisions.

After a certain point, company loyalty can become quite harmful.

Because big tech companies create brand loyalty, too many colleagues stayed in unhealthy environments.

When you work for a well-known company and strangers compliment you, it's fun to tell your friends.

Work defines you. This can make you stay too long even though your career isn't progressing and you're unhappy.

Google may become your surname.

Workplaces are not families.

If you're unhappy, don't stay just because they gave you the paycheck to buy your first home and make you feel like you owe your life to them.

Many employees stayed too long. Though depressed and suicidal.

What you can do about it

1. Your life is not worth a company.

2. Do you want your job title and workplace to be listed on your gravestone? If not, leave if conditions deteriorate.

3. Recognize that change can be challenging. It's difficult to leave a job you've held for a number of years.

4. Ask those who have experienced this change how they handled it.

You still have a bright future if you were rejected from FAANG interviews.

Rejections only lead to amazing opportunities. If you're young and childless, work for a startup.

Companies may pay more than FAANGs. Do your research.

Ask recruiters and hiring managers tough questions about how the company and teams prioritize respectful working hours and boundaries for workers.

I know many 15-year-olds who have a lifelong dream of working at Google, and it saddens me that they're chasing a name on their resume instead of excellence.

This article is not meant to discourage you from working at these companies, but to share my experience about what HR/managers will never mention in interviews.

Read both sides before signing the big offer letter.

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.