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.

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.

I shouldn't generalize, yet this is true.

Spending is simpler than earning.

Prove me wrong, but with home debt at $145k in 2020 and individual debt at $67k, people don't have their priorities straight.

Where does this loan originate?

Under-50 Americans owed $7.86 trillion in Q4 20T. That's more than the US's 3-trillion-dollar deficit.

Here’s a breakdown:
🏡 Mortgages/Home Equity Loans = $5.28 trillion (67%)
🎓 Student Loans = $1.20 trillion (15%)
🚗 Auto Loans = $0.80 trillion (10%)
💳 Credit Cards = $0.37 trillion (5%)
🏥 Other/Medical = $0.20 trillion (3%)

Images.google.com

At least the Fed and government can explain themselves with their debt balance which includes:

-Providing stimulus packages 2x for Covid relief

-Stabilizing the economy

-Reducing inflation and unemployment

-Providing for the military, education and farmers

No American should have this much debt.

Don’t get me wrong. Debt isn’t all the same. Yes, it’s a negative number but it carries different purposes which may not be all bad.

Good debt: Use those funds in hopes of them appreciating as an investment in the future

-Student loans
-Business loan
-Mortgage, home equity loan
-Experiences

Paying cash for a home is wasteful. Just if the home is exceptionally uncommon, only 1 in a million on the market, and has an incredible bargain with numerous bidders seeking higher prices should you do so.

To impress the vendor, pay cash so they can sell it quickly. Most people can't afford most properties outright. Only 15% of U.S. homebuyers can afford their home. Zillow reports that only 37% of homes are mortgage-free.

People have clearly overreached.

Ignore appearances.

5% down can buy a 10-bedroom mansion.

Not paying in cash isn't necessarily a negative thing given property prices have increased by 30% since 2008, and throughout the epidemic, we've seen work-from-homers resort to the midwest, avoiding pricey coastal cities like NYC and San Francisco.

By no means do I think NYC is dead, nothing will replace this beautiful city that never sleeps, and now is the perfect time to rent or buy when everything is below average value for people who always wanted to come but never could. Once social distance ends, cities will recover. 24/7 sardine-packed subways prove New York isn't designed for isolation.

When buying a home, pay 20% cash and the balance with a mortgage. A mortgage must be incorporated into other costs such as maintenance, brokerage fees, property taxes, etc. If you're stuck on why a home isn't right for you, read here. A mortgage must be paid until the term date. Whether its a 10 year or 30 year fixed mortgage, depending on interest rates, especially now as the 10-year yield is inching towards 1.25%, it's better to refinance in a lower interest rate environment and pay off your debt as well since the Fed will be inching interest rates up following the 10-year eventually to stabilize the economy, but I believe that won't be until after Covid and when businesses like luxury, air travel, and tourism will get bashed.

Bad debt: I guess the contrary must be true. There is no way to profit from the loan in the future, therefore it is just money down the drain.

-Luxury goods
-Credit card debt
-Fancy junk
-Vacations, weddings, parties, etc.

Credit cards and school loans are the two largest risks to the financial security of those under 50 since banks love to compound interest to affect your credit score and make it tougher to take out more loans, not that you should with that much debt anyhow. With a low credit score and heavy debt, banks take advantage of you because you need aid to pay more for their services. Paying back debt is the challenge for most.

Choose Not Chosen

As a financial literacy advocate and blogger, I prefer not to brag, but I will now. I know what to buy and what to avoid. My parents educated me to live a frugal, minimalist stealth wealth lifestyle by choice, not because we had to.

That's the lesson.

The poorest person who shows off with bling is trying to seem rich.

Rich people know garbage is a bad investment. Investing in education is one of the best long-term investments. With information, you can do anything.

Good with money shun some items out of respect and appreciation for what they have.

Less is more.

Instead of copying the Joneses, use what you have. They may look cheerful and stylish in their 20k ft home, yet they may be as broke as OJ Simpson in his 20-bedroom mansion.

Let's look at what appears good to follow and maintain your wealth.

#1: Quality comes before quantity

Being frugal doesn't entail being cheap and cruel. Rich individuals care about relationships and treating others correctly, not impressing them. You don't have to be rich to be good with money, although most are since they don't live the fantasy lifestyle.

Underspending is appreciating what you have.

Many people believe organic food is the same as washing chemical-laden produce. Hopefully. Organic, vegan, fresh vegetables from upstate may be more expensive in the short term, but they will help you live longer and save you money in the long run.

Consider. You'll save thousands a month eating McDonalds 3x a day instead of fresh seafood, veggies, and organic fruit, but your life will be shortened. If you want to save money and die early, go ahead, but I assume we all want to break the world record for longest person living and would rather spend less. Plus, elderly people get tax breaks, medicare, pensions, 401ks, etc. You're living for free, therefore eating fast food forever is a terrible decision.

With a few longer years, you may make hundreds or millions more in the stock market, spend more time with family, and just live.

Folks, health is wealth.

Consider the future benefit, not simply the cash sign. Cheapness is useless.

Same with stuff. Don't stock your closet with fast-fashion you can't wear for years. Buying inexpensive goods that will fail tomorrow is stupid.

Investing isn't only in stocks. You're living. Consume less.

#2: If you cannot afford it twice, you cannot afford it once

I learned this from my dad in 6th grade. I've been lucky to travel, experience things, go to a great university, and conduct many experiments that others without a stable, decent lifestyle can afford.

I didn't live this way because of my parents' paycheck or financial knowledge.

Saving and choosing caused it.

I always bring cash when I shop. I ditch Apple Pay and credit cards since I can spend all I want on even if my account bounces.

Banks are nasty. When you lose it, they profit.

Cash hinders banks' profits. Carrying a big, hefty wallet with cash is lame and annoying, but it's the best method to only spend what you need. Not for vacation, but for tiny daily expenses.

Physical currency lets you know how much you have for lunch or a taxi.

It's physical, thus losing it prevents debt.

If you can't afford it, it will harm more than help.

#3: You really can purchase happiness with money.

If used correctly, yes.

Happiness and satisfaction differ.

It won't bring you fulfillment because you must work hard on your own to help others, but you can travel and meet individuals you wouldn't otherwise meet.

You can meet your future co-worker or strike a deal while waiting an hour in first class for takeoff, or you can meet renowned people at a networking brunch.

Seen a pattern here?

Your time and money are best spent on connections. Not automobiles or firearms. That’s just stuff. It doesn’t make you a better person.

Be different if you've earned less. Instead of trying to win the lotto or become an NFL star for your first big salary, network online for free.

Be resourceful. Sign up for LinkedIn, post regularly, and leave unengaged posts up because that shows power.

Consistency is beneficial.

I did that for a few months and met amazing people who helped me get jobs. Money doesn't create jobs, it creates opportunities.

Resist social media and scammers that peddle false hopes.

Choose wisely.

#4: Avoid gushing over titles and purchasing trash.

As Insider’s Hillary Hoffower reports, “Showing off wealth is no longer the way to signify having wealth. In the US particularly, the top 1% have been spending less on material goods since 2007.”

I checked my closet. No brand comes to mind. I've never worn a brand's logo and rotate 6 white shirts daily. I have my priorities and don't waste money or effort on clothing that won't fit me in a year.

Unless it's your full-time work, clothing shouldn't be part of our mornings.

Lifestyle of stealth wealth. You're so fulfilled that seeming homeless won't hurt your self-esteem.

That's self-assurance.

Extroverts aren't required.

That's irrelevant.

Showing off won't win you friends.

They'll like your personality.

#5: Time is the most valuable commodity.

Being rich doesn't entail working 24/7 M-F.

They work when they are ready to work.

Waking up at 5 a.m. won't make you a millionaire, but it will inculcate diligence and tenacity in you.

You have a busy day yet want to exercise. You can skip the workout or wake up at 4am instead of 6am to do it.

Emotion-driven lazy bums stay in bed.

Those that are accountable keep their promises because they know breaking one will destroy their week.

Since 7th grade, I've worked out at 5am for myself, not to impress others. It gives me greater energy to contribute to others, especially on weekends and holidays.

It's a habit that I have in my life.

Find something that you take seriously and makes you a better person.

As someone who is close to becoming a millionaire and has encountered them throughout my life, I can share with you a few important differences that have shaped who we are as a society based on the weekends:

-Read

-Sleep

-Best time to work with no distractions

-Eat together

-Take walks and be in nature

-Gratitude

-Major family time

-Plan out weeks

-Go grocery shopping because health = wealth

#6. Perspective is Important

Timing the markets will slow down your career. Professors preach scarcity, not abundance. Why should school teach success? They give us bad advice.

If you trust in abundance and luck by attempting and experimenting, growth will come effortlessly. Passion isn't a term that just appears. Mistakes and fresh people help. You can get money. If you don't think it's worth it, you won't.

You don’t have to be wealthy to be good at money, but most are for these reasons.  Rich is a mindset, wealth is power. Prioritize your resources. Invest in yourself, knowing the toughest part is starting.

Thanks for reading!

I analyze the top 200 DeFi crypto projects in this article.

This isn't a study. The findings benefit crypto investors.

Let’s go!

A set of data

I analyzed data from defillama.com. In my analysis, I used the top 200 DeFis by TVL in October 2022.

Total Locked Value

The chart below shows platform-specific locked value.

14 platforms had $1B+ TVL. 65 platforms have $100M-$1B TVL. The remaining 121 platforms had TVLs below $100 million, with the lowest being $23 million.

TVLs are distributed Pareto. Top 40% of DeFis account for 80% of TVLs.

Compliant Blockchains

Ethereum's blockchain leads DeFi. 96 of the examined projects offer services on Ethereum. Behind BSC, Polygon, and Avalanche.

Five platforms used 10+ blockchains. 36 between 2-10 159 used 1 blockchain.

Use Cases for DeFi

The chart below shows platform use cases. Each platform has decentralized exchanges, liquid staking, yield farming, and lending.

These use cases are DefiLlama's main platform features.

Which use case costs the most? Chart explains. Collateralized debt, liquid staking, dexes, and lending have high TVLs.

The DeFi Industry

I compared three high-TVL platforms (Maker DAO, Balancer, AAVE). The columns show monthly TVL and token price changes. The graph shows monthly Bitcoin price changes.

Each platform's market moves similarly.

Probably because most DeFi deposits are cryptocurrencies. Since individual currencies are highly correlated with Bitcoin, it's not surprising that they move in unison.

Takeaways

This analysis shows that the most common DeFi services (decentralized exchanges, liquid staking, yield farming, and lending) also have the highest average locked value.

Some projects run on one or two blockchains, while others use 15 or 20. Our analysis shows that a project's blockchain count has no correlation with its success.

It's hard to tell if certain use cases are rising. Bitcoin's price heavily affects the entire DeFi market.

TVL seems to be a good indicator of a DeFi platform's success and quality. Higher TVL platforms are cheaper. They're a better long-term investment because they gain or lose less value than DeFis with lower TVLs.

Mainstream economics haven't noticed.

What come to mind when I say the word "economics"?

Probably GDP, unemployment, and inflation.

If you've ever watched the news or listened to an economist, they'll use data like these to defend a political goal.

The issue is that these statistics are total bunk.

I'm being provocative, but I mean it:

• The economy is not measured by GDP.

• How many people are unemployed is not counted in the unemployment rate.

• Inflation is not measured by the CPI.

All orthodox economists' major economic statistics are either wrong or falsified.

Government institutions create all these stats. The administration wants to reassure citizens the economy is doing well.

GDP does not reflect economic expansion.

GDP measures a country's economic size and growth. It’s calculated by the BEA, a government agency.

The US has the world's largest (self-reported) GDP, growing 2-3% annually.

If GDP rises, the economy is healthy, say economists.

Why is the GDP flawed?

GDP measures a country's yearly spending.

The government may adjust this to make the economy look good.

GDP = C + G + I + NX

C = Consumer Spending

G = Government Spending

I = Investments (Equipment, inventories, housing, etc.)

NX = Exports minus Imports

GDP is a country's annual spending.

The government can print money to boost GDP. The government has a motive to increase and manage GDP.

Because government expenditure is part of GDP, printing money and spending it on anything will raise GDP.

They've done this. Since 1950, US government spending has grown 8% annually, faster than GDP.

In 2022, government spending accounted for 44% of GDP. It's the highest since WWII. In 1790-1910, it was 3% of GDP.

Who cares?

The economy isn't only spending. Focus on citizens' purchasing power or quality of life.

Since GDP just measures spending, the government can print money to boost GDP.

Even if Americans are poorer than last year, economists can say GDP is up and everything is fine.

How many people are unemployed is not counted in the unemployment rate.

The unemployment rate measures a country's labor market. If unemployment is high, people aren't doing well economically.

The BLS estimates the (self-reported) unemployment rate as 3-4%.

Why is the unemployment rate so high?

The US government surveys 100k persons to measure unemployment. They extrapolate this data for the country.

They come into 3 categories:

• Employed

People with jobs are employed … duh.

• Unemployed

People who are “jobless, looking for a job, and available for work” are unemployed

• Not in the labor force

The “labor force” is the employed + the unemployed.

The unemployment rate is the percentage of unemployed workers.

Problem is unemployed definition. You must actively seek work to be considered unemployed.

You're no longer unemployed if you haven't interviewed in 4 weeks.

This shit makes no goddamn sense.

Why does this matter?

You can't interview if there are no positions available. You're no longer unemployed after 4 weeks.

In 1994, the BLS redefined "unemployed" to exclude discouraged workers.

If you haven't interviewed in 4 weeks, you're no longer counted in the unemployment rate.

If unemployment were measured by total unemployed, it would be 25%.

Because the government wants to keep the unemployment rate low, they modify the definition.

If every US resident was unemployed and had no job interviews, economists would declare 0% unemployment. Excellent!

Inflation is not measured by the CPI.

The BLS measures CPI. This month was the highest since 1981.

CPI measures the cost of a basket of products across time. Food, energy, shelter, and clothes are included.

A 9.1% CPI means the basket of items is 9.1% more expensive.

What is the CPI problem?

Here's a more detailed explanation of CPI's flaws.

In summary, CPI is manipulated to be understated.

Housing costs are understated to manipulate CPI. Housing accounts for 33% of the CPI because it's the biggest expense for most people.

This signifies it's the biggest CPI weight.

Rather than using actual house prices, the Bureau of Labor Statistics essentially makes shit up. You can read more about the process here.

Surprise! It’s bullshit

The BLS stated Shelter's price rose 5.5% this month.

House prices are up 11-21%. (Source 1, Source 2, Source 3)

Rents are up 14-26%. (Source 1, Source 2)

Why is this important?

If CPI included housing prices, it would be 12-15 percent this month, not 9.1 percent.

9% inflation is nuts. Your money's value halves every 7 years at 9% inflation.

Worse is 15% inflation. Your money halves every 4 years at 15% inflation.

If everyone realized they needed to double their wage every 4-5 years to stay wealthy, there would be riots.

Inflation drains our money's value so the government can keep printing it.

The Solution

Most individuals know the existing system doesn't work, but can't explain why.

People work hard yet lag behind. The government lies about the economy's data.

In reality:

• GDP has been down since 2008

• 25% of Americans are unemployed

• Inflation is actually 15%

People might join together to vote out kleptocratic politicians if they knew the reality.

Having reliable economic data is the first step.

People can't understand the situation without sufficient information. Instead of immigrants or billionaires, people would blame liar politicians.

Here’s the vision:

A decentralized, transparent, and global dashboard that tracks economic data like GDP, unemployment, and inflation for every country on Earth.

Government incentives influence economic statistics.

ShadowStats has already started this effort, but the calculations must be transparent, decentralized, and global to be effective.

If interested, email me at trevorstark02@gmail.com.

Here are some links to further your research:

1. MIT Billion Prices Project

2. 1729 Decentralized Inflation Dashboard Project

3. Balaji Srinivasan on “Fiat Information VS. Crypto Information”