More on Society & Culture
Mike Meyer
3 years ago
Reality Distortion
Old power paradigm blocks new planetary paradigm
The difference between our reality and the media's reality is like a tale of two worlds. The greatest and worst of times, really.
Expanding information demands complex skills and understanding to separate important information from ignorance and crap. And that's just the start of determining the source's aim.
Trust who? We see people trust liars in public and then be destroyed by their decisions. Mistakes may be devastating.
Many give up and don't trust anyone. Reality is a choice, though. Same risks.
We must separate our needs and wants from reality. Needs and wants have rules. Greed and selfishness create an unlivable planet.
Culturally, we know this, but we ignore it as foolish. Selfish and greedy people obtain what they want, while others suffer.
We invade, plunder, rape, and burn. We establish civilizations by institutionalizing an exploitable underclass and denying its existence. These cultural lies promote greed and selfishness despite their destructiveness.
Controlling parts of society institutionalize these lies as fact. Many of each age are willing to gamble on greed because they were taught to see greed and selfishness as principles justified by prosperity.
Our cultural understanding recognizes the long-term benefits of collaboration and sharing. This older understanding generates an increasing tension between greedy people and those who see its planetary effects.
Survival requires distinguishing between global and regional realities. Simple, yet many can't do it. This is the first time human greed has had a global impact.
In the past, conflict stories focused on regional winners and losers. Losers lose, winners win, etc. Powerful people see potential decades of nuclear devastation as local, overblown, and not personally dangerous.
Mutually Assured Destruction (MAD) was a human choice that required people to acquiesce to irrational devastation. This prevented nuclear destruction. Most would refuse.
A dangerous “solution” relies on nuclear trigger-pullers not acting irrationally. Since then, we've collected case studies of sane people performing crazy things in experiments. We've been lucky, but the climate apocalypse could be different.
Climate disaster requires only continuing current behavior. These actions already cause global harm, but that's not a threat. These activities must be viewed differently.
Once grasped, denying planetary facts is hard to accept. Deniers can't think beyond regional power. Seeing planet-scale is unusual.
Decades of indoctrination defining any planetary perspective as un-American implies communal planetary assets are for plundering. The old paradigm limits any other view.
In the same way, the new paradigm sees the old regional power paradigm as a threat to planetary civilization and lifeforms. Insane!
While MAD relied on leaders not acting stupidly to trigger a nuclear holocaust, the delayed climatic holocaust needs correcting centuries of lunacy. We must stop allowing craziness in global leadership.
Nothing in our acknowledged past provides a paradigm for such. Only primitive people have failed to reach our level of sophistication.
Before European colonization, certain North American cultures built sophisticated regional nations but abandoned them owing to authoritarian cruelty and destruction. They were overrun by societies that saw no wrong in perpetual exploitation. David Graeber's The Dawn of Everything is an example of historical rediscovery, which is now crucial.
From the new paradigm's perspective, the old paradigm is irrational, yet it's too easy to see those in it as ignorant or malicious, if not both. These people are both, but the collapsing paradigm they promote is older or more ingrained than we think.
We can't shift that paradigm's view of a dead world. We must eliminate this mindset from our nations' leadership. No other way will preserve the earth.
Change is occurring. As always with tremendous transition, younger people are building the new paradigm.
The old paradigm's disintegration is insane. The ability to detect errors and abandon their sources is more important than age. This is gaining recognition.
The breakdown of the previous paradigm is not due to senile leadership, but to systemic problems that the current, conservative leadership cannot recognize.
Stop following the old paradigm.
Isaiah McCall
3 years ago
Is TikTok slowly destroying a new generation?
It's kids' digital crack
TikTok is a destructive social media platform.
The interface shortens attention spans and dopamine receptors.
TikTok shares more data than other apps.
Seeing an endless stream of dancing teens on my glowing box makes me feel like a Blade Runner extra.
TikTok did in one year what MTV, Hollywood, and Warner Music tried to do in 20 years. TikTok has psychotized the two-thirds of society Aldous Huxley said were hypnotizable.
Millions of people, mostly kids, are addicted to learning a new dance, lip-sync, or prank, and those who best dramatize this collective improvisation get likes, comments, and shares.
TikTok is a great app. So what?
The Commercial Magnifying Glass TikTok made me realize my generation's time was up and the teenage Zoomers were the target.
I told my 14-year-old sister, "Enjoy your time under the commercial magnifying glass."
TikTok sells your every move, gesture, and thought. Data is the new oil. If you tell someone, they'll say, "Yeah, they collect data, but who cares? I have nothing to hide."
It's a George Orwell novel's beginning. Look up Big Brother Award winners to see if TikTok won.
TikTok shares your data more than any other social media app, and where it goes is unclear. TikTok uses third-party trackers to monitor your activity after you leave the app.
Consumers can't see what data is shared or how it will be used. — Genius URL
32.5 percent of Tiktok's users are 10 to 19 and 29.5% are 20 to 29.
TikTok is the greatest digital marketing opportunity in history, and they'll use it to sell you things, track you, and control your thoughts. Any of its users will tell you, "I don't care, I just want to be famous."
TikTok manufactures mental illness
TikTok's effect on dopamine and the brain is absurd. Dopamine controls the brain's pleasure and reward centers. It's like a switch that tells your brain "this feels good, repeat."
Dr. Julie Albright, a digital culture and communication sociologist, said TikTok users are "carried away by dopamine." It's hypnotic, you'll keep watching."
TikTok constantly releases dopamine. A guy on TikTok recently said he didn't like books because they were slow and boring.
The US didn't ban Tiktok.
Biden and Trump agree on bad things. Both agree that TikTok threatens national security and children's mental health.
The Chinese Communist Party owns and operates TikTok, but that's not its only problem.
There’s borderline child porn on TikTok
It's unsafe for children and violated COPPA.
It's also Chinese spyware. I'm not a Trump supporter, but I was glad he wanted TikTok regulated and disappointed when he failed.
Full-on internet censorship is rare outside of China, so banning it may be excessive. US should regulate TikTok more.
We must reject a low-quality present for a high-quality future.
TikTok vs YouTube
People got mad when I wrote about YouTube's death.
They didn't like when I said TikTok was YouTube's first real challenger.
Indeed. TikTok is the fastest-growing social network. In three years, the Chinese social media app TikTok has gained over 1 billion active users. In the first quarter of 2020, it had the most downloads of any app in a single quarter.
TikTok is the perfect social media app in many ways. It's brief and direct.
Can you believe they had a YouTube vs TikTok boxing match? We are doomed as a species.
YouTube hosts my favorite videos. That’s why I use it. That’s why you use it. New users expect more. They want something quicker, more addictive.
TikTok's impact on other social media platforms frustrates me. YouTube copied TikTok to compete.
It's all about short, addictive content.
I'll admit I'm probably wrong about TikTok. My friend says his feed is full of videos about food, cute animals, book recommendations, and hot lesbians.
Whatever.
TikTok makes us bad
TikTok is the opposite of what the Ancient Greeks believed about wisdom.
It encourages people to be fake. It's like a never-ending costume party where everyone competes.
It does not mean that Gen Z is doomed.
They could be the saviors of the world for all I know.
TikTok feels like a step towards Mike Judge's "Idiocracy," where the average person is a pleasure-seeking moron.
DC Palter
2 years ago
Why Are There So Few Startups in Japan?
Japan's startup challenge: 7 reasons
Every day, another Silicon Valley business is bought for a billion dollars, making its founders rich while growing the economy and improving consumers' lives.
Google, Amazon, Twitter, and Medium dominate our daily lives. Tesla automobiles and Moderna Covid vaccinations.
The startup movement started in Silicon Valley, California, but the rest of the world is catching up. Global startup buzz is rising. Except Japan.
644 of CB Insights' 1170 unicorns—successful firms valued at over $1 billion—are US-based. China follows with 302 and India third with 108.
Japan? 6!
1% of US startups succeed. The third-largest economy is tied with small Switzerland for startup success.
Mexico (8), Indonesia (12), and Brazil (12) have more successful startups than Japan (16). South Korea has 16. Yikes! Problem?
Why Don't Startups Exist in Japan More?
Not about money. Japanese firms invest in startups. To invest in startups, big Japanese firms create Silicon Valley offices instead of Tokyo.
Startups aren't the issue either. Local governments are competing to be Japan's Shirikon Tani, providing entrepreneurs financing, office space, and founder visas.
Startup accelerators like Plug and Play in Tokyo, Osaka, and Kyoto, the Startup Hub in Kobe, and Google for Startups are many.
Most of the companies I've encountered in Japan are either local offices of foreign firms aiming to expand into the Japanese market or small businesses offering local services rather than disrupting a staid industry with new ideas.
There must be a reason Japan can develop world-beating giant corporations like Toyota, Nintendo, Shiseido, and Suntory but not inventive startups.
Culture, obviously. Japanese culture excels in teamwork, craftsmanship, and quality, but it hates moving fast, making mistakes, and breaking things.
If you have a brilliant idea in Silicon Valley, quit your job, get money from friends and family, and build a prototype. To fund the business, you approach angel investors and VCs.
Most non-startup folks don't aware that venture capitalists don't want good, profitable enterprises. That's wonderful if you're developing a solid small business to consult, open shops, or make a specialty product. However, you must pay for it or borrow money. Venture capitalists want moon rockets. Silicon Valley is big or bust. Almost 90% will explode and crash. The few successes are remarkable enough to make up for the failures.
Silicon Valley's high-risk, high-reward attitude contrasts with Japan's incrementalism. Japan makes the best automobiles and cleanrooms, but it fails to produce new items that grow the economy.
Changeable? Absolutely. But, what makes huge manufacturing enterprises successful and what makes Japan a safe and comfortable place to live are inextricably connected with the lack of startups.
Barriers to Startup Development in Japan
These are the 7 biggest obstacles to Japanese startup success.
Unresponsive Employment Market
While the lifelong employment system in Japan is evolving, the average employee stays at their firm for 12 years (15 years for men at large organizations) compared to 4.3 years in the US. Seniority, not experience or aptitude, determines career routes, making it tough to quit a job to join a startup and then return to corporate work if it fails.
Conservative Buyers
Even if your product is buggy and undocumented, US customers will migrate to a cheaper, superior one. Japanese corporations demand perfection from their trusted suppliers and keep with them forever. Startups need income fast, yet product evaluation takes forever.
Failure intolerance
Japanese business failures harm lives. Failed forever. It hinders risk-taking. Silicon Valley embraces failure. Build another startup if your first fails. Build a third if that fails. Every setback is viewed as a learning opportunity for success.
4. No Corporate Purchases
Silicon Valley industrial giants will buy fast-growing startups for a lot of money. Many huge firms have stopped developing new goods and instead buy startups after the product is validated.
Japanese companies prefer in-house product development over startup acquisitions. No acquisitions mean no startup investment and no investor reward.
Startup investments can also be monetized through stock market listings. Public stock listings in Japan are risky because the Nikkei was stagnant for 35 years while the S&P rose 14x.
5. Social Unity Above Wealth
In Silicon Valley, everyone wants to be rich. That creates a competitive environment where everyone wants to succeed, but it also promotes fraud and societal problems.
Japan values communal harmony above individual success. Wealthy folks and overachievers are avoided. In Japan, renegades are nearly impossible.
6. Rote Learning Education System
Japanese high school graduates outperform most Americans. Nonetheless, Japanese education is known for its rote memorization. The American system, which fails too many kids, emphasizes creativity to create new products.
Immigration.
Immigrants start 55% of successful Silicon Valley firms. Some come for university, some to escape poverty and war, and some are recruited by Silicon Valley startups and stay to start their own.
Japan is difficult for immigrants to start a business due to language barriers, visa restrictions, and social isolation.
How Japan Can Promote Innovation
Patchwork solutions to deep-rooted cultural issues will not work. If customers don't buy things, immigration visas won't aid startups. Startups must have a chance of being acquired for a huge sum to attract investors. If risky startups fail, employees won't join.
Will Japan never have a startup culture?
Once a consensus is reached, Japan changes rapidly. A dwindling population and standard of living may lead to such consensus.
Toyota and Sony were firms with renowned founders who used technology to transform the world. Repeatable.
Silicon Valley is flawed too. Many people struggle due to wealth disparities, job churn and layoffs, and the tremendous ups and downs of the economy caused by stock market fluctuations.
The founders of the 10% successful startups are heroes. The 90% that fail and return to good-paying jobs with benefits are never mentioned.
Silicon Valley startup culture and Japanese corporate culture are opposites. Each have pros and cons. Big Japanese corporations make the most reliable, dependable, high-quality products yet move too slowly. That's good for creating cars, not social networking apps.
Can innovation and success be encouraged without eroding social cohesion? That can motivate software firms to move fast and break things while recognizing the beauty and precision of expert craftsmen? A hybrid culture where Japan can make the world's best and most original items. Hopefully.
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Vitalik
3 years ago
Fairness alternatives to selling below market clearing prices (or community sentiment, or fun)
When a seller has a limited supply of an item in high (or uncertain and possibly high) demand, they frequently set a price far below what "the market will bear." As a result, the item sells out quickly, with lucky buyers being those who tried to buy first. This has happened in the Ethereum ecosystem, particularly with NFT sales and token sales/ICOs. But this phenomenon is much older; concerts and restaurants frequently make similar choices, resulting in fast sell-outs or long lines.
Why do sellers do this? Economists have long wondered. A seller should sell at the market-clearing price if the amount buyers are willing to buy exactly equals the amount the seller has to sell. If the seller is unsure of the market-clearing price, they should sell at auction and let the market decide. So, if you want to sell something below market value, don't do it. It will hurt your sales and it will hurt your customers. The competitions created by non-price-based allocation mechanisms can sometimes have negative externalities that harm third parties, as we will see.
However, the prevalence of below-market-clearing pricing suggests that sellers do it for good reason. And indeed, as decades of research into this topic has shown, there often are. So, is it possible to achieve the same goals with less unfairness, inefficiency, and harm?
Selling at below market-clearing prices has large inefficiencies and negative externalities
An item that is sold at market value or at an auction allows someone who really wants it to pay the high price or bid high in the auction. So, if a seller sells an item below market value, some people will get it and others won't. But the mechanism deciding who gets the item isn't random, and it's not always well correlated with participant desire. It's not always about being the fastest at clicking buttons. Sometimes it means waking up at 2 a.m. (but 11 p.m. or even 2 p.m. elsewhere). Sometimes it's just a "auction by other means" that's more chaotic, less efficient, and has far more negative externalities.
There are many examples of this in the Ethereum ecosystem. Let's start with the 2017 ICO craze. For example, an ICO project would set the price of the token and a hard maximum for how many tokens they are willing to sell, and the sale would start automatically at some point in time. The sale ends when the cap is reached.
So what? In practice, these sales often ended in 30 seconds or less. Everyone would start sending transactions in as soon as (or just before) the sale started, offering higher and higher fees to encourage miners to include their transaction first. Instead of the token seller receiving revenue, miners receive it, and the sale prices out all other applications on-chain.
The most expensive transaction in the BAT sale set a fee of 580,000 gwei, paying a fee of $6,600 to get included in the sale.
Many ICOs after that tried various strategies to avoid these gas price auctions; one ICO notably had a smart contract that checked the transaction's gasprice and rejected it if it exceeded 50 gwei. But that didn't solve the issue. Buyers hoping to game the system sent many transactions hoping one would get through. An auction by another name, clogging the chain even more.
ICOs have recently lost popularity, but NFTs and NFT sales have risen in popularity. But the NFT space didn't learn from 2017; they do fixed-quantity sales just like ICOs (eg. see the mint function on lines 97-108 of this contract here). So what?
That's not the worst; some NFT sales have caused gas price spikes of up to 2000 gwei.
High gas prices from users fighting to get in first by sending higher and higher transaction fees. An auction renamed, pricing out all other applications on-chain for 15 minutes.
So why do sellers sometimes sell below market price?
Selling below market value is nothing new, and many articles, papers, and podcasts have written (and sometimes bitterly complained) about the unwillingness to use auctions or set prices to market-clearing levels.
Many of the arguments are the same for both blockchain (NFTs and ICOs) and non-blockchain examples (popular restaurants and concerts). Fairness and the desire not to exclude the poor, lose fans or create tension by being perceived as greedy are major concerns. The 1986 paper by Kahneman, Knetsch, and Thaler explains how fairness and greed can influence these decisions. I recall that the desire to avoid perceptions of greed was also a major factor in discouraging the use of auction-like mechanisms in 2017.
Aside from fairness concerns, there is the argument that selling out and long lines create a sense of popularity and prestige, making the product more appealing to others. Long lines should have the same effect as high prices in a rational actor model, but this is not the case in reality. This applies to ICOs and NFTs as well as restaurants. Aside from increasing marketing value, some people find the game of grabbing a limited set of opportunities first before everyone else is quite entertaining.
But there are some blockchain-specific factors. One argument for selling ICO tokens below market value (and one that persuaded the OmiseGo team to adopt their capped sale strategy) is community dynamics. The first rule of community sentiment management is to encourage price increases. People are happy if they are "in the green." If the price drops below what the community members paid, they are unhappy and start calling you a scammer, possibly causing a social media cascade where everyone calls you a scammer.
This effect can only be avoided by pricing low enough that post-launch market prices will almost certainly be higher. But how do you do this without creating a rush for the gates that leads to an auction?
Interesting solutions
It's 2021. We have a blockchain. The blockchain is home to a powerful decentralized finance ecosystem, as well as a rapidly expanding set of non-financial tools. The blockchain also allows us to reset social norms. Where decades of economists yelling about "efficiency" failed, blockchains may be able to legitimize new uses of mechanism design. If we could use our more advanced tools to create an approach that more directly solves the problems, with fewer side effects, wouldn't that be better than fiddling with a coarse-grained one-dimensional strategy space of selling at market price versus below market price?
Begin with the goals. We'll try to cover ICOs, NFTs, and conference tickets (really a type of NFT) all at the same time.
1. Fairness: don't completely exclude low-income people from participation; give them a chance. The goal of token sales is to avoid high initial wealth concentration and have a larger and more diverse initial token holder community.
2. Don’t create races: Avoid situations where many people rush to do the same thing and only a few get in (this is the type of situation that leads to the horrible auctions-by-another-name that we saw above).
3. Don't require precise market knowledge: the mechanism should work even if the seller has no idea how much demand exists.
4. Fun: The process of participating in the sale should be fun and game-like, but not frustrating.
5. Give buyers positive expected returns: in the case of a token (or an NFT), buyers should expect price increases rather than decreases. This requires selling below market value.
Let's start with (1). From Ethereum's perspective, there is a simple solution. Use a tool designed for the job: proof of personhood protocols! Here's one quick idea:
Mechanism 1 Each participant (verified by ID) can buy up to ‘’X’’ tokens at price P, with the option to buy more at an auction.
With the per-person mechanism, buyers can get positive expected returns for the portion sold through the per-person mechanism, and the auction part does not require sellers to understand demand levels. Is it race-free? The number of participants buying through the per-person pool appears to be high. But what if the per-person pool isn't big enough to accommodate everyone?
Make the per-person allocation amount dynamic.
Mechanism 2 Each participant can deposit up to X tokens into a smart contract to declare interest. Last but not least, each buyer receives min(X, N / buyers) tokens, where N is the total sold through the per-person pool (some other amount can also be sold by auction). The buyer gets their deposit back if it exceeds the amount needed to buy their allocation.
No longer is there a race condition based on the number of buyers per person. No matter how high the demand, it's always better to join sooner rather than later.
Here's another idea if you like clever game mechanics with fancy quadratic formulas.
Mechanism 3 Each participant can buy X units at a price P X 2 up to a maximum of C tokens per buyer. C starts low and gradually increases until enough units are sold.
The quantity allocated to each buyer is theoretically optimal, though post-sale transfers will degrade this optimality over time. Mechanisms 2 and 3 appear to meet all of the above objectives. They're not perfect, but they're good starting points.
One more issue. For fixed and limited supply NFTs, the equilibrium purchased quantity per participant may be fractional (in mechanism 2, number of buyers > N, and in mechanism 3, setting C = 1 may already lead to over-subscription). With fractional sales, you can offer lottery tickets: if there are N items available, you have a chance of N/number of buyers of getting the item, otherwise you get a refund. For a conference, groups could bundle their lottery tickets to guarantee a win or a loss. The certainty of getting the item can be auctioned.
The bottom tier of "sponsorships" can be used to sell conference tickets at market rate. You may end up with a sponsor board full of people's faces, but is that okay? After all, John Lilic was on EthCC's sponsor board!
Simply put, if you want to be reliably fair to people, you need an input that explicitly measures people. Authentication protocols do this (and if desired can be combined with zero knowledge proofs to ensure privacy). So we should combine the efficiency of market and auction-based pricing with the equality of proof of personhood mechanics.
Answers to possible questions
Q: Won't people who don't care about your project buy the item and immediately resell it?
A: Not at first. Meta-games take time to appear in practice. If they do, making them untradeable for a while may help mitigate the damage. Using your face to claim that your previous account was hacked and that your identity, including everything in it, should be moved to another account works because proof-of-personhood identities are untradeable.
Q: What if I want to make my item available to a specific community?
A: Instead of ID, use proof of participation tokens linked to community events. Another option, also serving egalitarian and gamification purposes, is to encrypt items within publicly available puzzle solutions.
Q: How do we know they'll accept? Strange new mechanisms have previously been resisted.
A: Having economists write screeds about how they "should" accept a new mechanism that they find strange is difficult (or even "equity"). However, abrupt changes in context effectively reset people's expectations. So the blockchain space is the best place to try this. You could wait for the "metaverse", but it's possible that the best version will run on Ethereum anyway, so start now.
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
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:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
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.
Owolabi Judah
3 years ago
How much did YouTube pay for 10 million views?
Ali's $1,054,053.74 YouTube Adsense haul.
YouTuber, entrepreneur, and former doctor Ali Abdaal. He began filming productivity and financial videos in 2017. Ali Abdaal has 3 million YouTube subscribers and has crossed $1 million in AdSense revenue. Crazy, no?
Ali will share the revenue of his top 5 youtube videos, things he's learned that you can apply to your side hustle, and how many views it takes to make a livelihood off youtube.
First, "The Long Game."
All good things take time to bear fruit. Compounding improves everything. Long-term work yields better returns. Ali made his first dollar after nine months and 85 videos.
Second, "One piece of content can transform your life, but you never know which one."
Had he abandoned YouTube at 84 videos without making any money, he wouldn't have filmed the 85th video that altered everything.
Third Lesson: Your Industry Choice Can Multiply.
The industry or niche you target as a business owner or side hustler can have a major impact on how much money you make.
Here are the top 5 videos.
1) 9.8m views: $191,258.16 for 9 passive income ideas
Ali made 2 points.
We should consider YouTube videos digital assets. They're investments, which make us money. His investments are yielding passive income.
Investing extra time and effort in your films can pay off.
2) How to Invest for Beginners — 5.2m Views: $87,200.08.
This video did poorly in the first several weeks after it was published; it was his tenth poorest performer. Don't worry about things you can't control. This applies to life, not just YouTube videos.
He stated we constantly have anxieties, fears, and concerns about things outside our control, but if we can find that line, life is easier and more pleasurable.
3) How to Build a Website in 2022— 866.3k views: $42,132.72.
The RPM was $48.86 per thousand views, making it his highest-earning video. Squarespace, Wix, and other website builders are trying to put ads on it and competing against one other, so ad rates go up.
Because it was beyond his niche, Ali almost didn't make the video. He made the video because he wanted to help at least one person.
4) How I take notes on my iPad in medical school — 5.9m views: $24,479.80
85th video. It's the video that affected Ali's YouTube channel and his life the most. The video's success wasn't certain.
5) How I Type Fast 156 Words Per Minute — 8.2M views: $25,143.17
Ali didn't know this video would perform well; he made it because he can type fast and has been practicing for 10 years. So he made a video with his best advice.
How many views to different wealth levels?
It depends on geography, niche, and other monetization sources. To keep things simple, he would solely utilize AdSense.
How many views to generate money?
To generate money on Youtube, you need 1,000 subscribers and 4,000 hours of view time. How much work do you need to make pocket money?
Ali's first 1,000 subscribers took 52 videos and 6 months. The typical channel with 1,000 subscribers contains 152 videos, according to Tubebuddy. It's time-consuming.
After monetizing, you'll need 15,000 views/month to make $5-$10/day.
How many views to go part-time?
Say you make $35,000/year at your day job. If you work 5 days/week, you make $7,000/year each day. If you want to drop down from 5 days to 4 days/week, you need to make an extra $7,000/year from YouTube, or $600/month.
What's the quit-your-job budget?
Silicon Valley Girl is in a highly successful niche targeting tech-focused folks in the west. When her channel had 500k views/month, she made roughly $3,000/month or $47,000/year, enough to quit your work.
Marina has another 1.5m subscriber channel in Russia, which has a lower rpm because fewer corporations advertise there than in the west. 2.3 million views/month is $4,000/month or $50,000/year, enough to quit your employment.
Marina is an intriguing example because she has three YouTube channels with the same skills, but one is 16x more profitable due to the niche she chose.
In Ali's case, he made 100+ videos when his channel was producing enough money to quit his job, roughly $4,000/month.
How many views make you rich?
Depending on how you define rich. Ali felt prosperous with over $100,000/year and 3–5m views/month.
Conclusion
YouTubers and artists don't treat their work like a company, which is a mistake. Businesses have been attempting to figure this out for decades, if not centuries.
We can learn from the business world how to monetize YouTube, Instagram, and Tiktok and make them into sustainable enterprises where we can hire people and delegate tasks.
Bonus
Watch Ali's video explaining all this:
This post is a summary. Read the full article here