More on Personal Growth
Darius Foroux
2 years ago
My financial life was changed by a single, straightforward mental model.
Prioritize big-ticket purchases
I've made several spending blunders. I get sick thinking about how much money I spent.
My financial mental model was poor back then.
Stoicism and mindfulness keep me from attaching to those feelings. It still hurts.
Until four or five years ago, I bought a new winter jacket every year.
Ten years ago, I spent twice as much. Now that I have a fantastic, warm winter parka, I don't even consider acquiring another one. No more spending. I'm not looking for jackets either.
Saving time and money by spending well is my thinking paradigm.
The philosophy is expressed in most languages. Cheap is expensive in the Netherlands. This applies beyond shopping.
In this essay, I will offer three examples of how this mental paradigm transformed my financial life.
Publishing books
In 2015, I presented and positioned my first book poorly.
I called the book Huge Life Success and made a funny Canva cover in 30 minutes. This:
That looks nothing like my present books. No logo or style. The book felt amateurish.
The book started bothering me a few weeks after publication. The advice was good, but it didn't appear professional. I studied the book business extensively.
I created a style for all my designs. Branding. Win Your Inner Wars was reissued a year later.
Title, cover, and description changed. Rearranging the chapters improved readability.
Seven years later, the book sells hundreds of copies a month. That taught me a lot.
Rushing to finish a project is enticing. Send it and move forward.
Avoid rushing everything. Relax. Develop your projects. Perform well. Perform the job well.
My first novel was underfunded and underworked. A bad book arrived. I then invested time and money in writing the greatest book I could.
That book still sells.
Traveling
I hate travel. Airports, flights, trains, and lines irritate me.
But, I enjoy traveling to beautiful areas.
I do it strangely. I make up travel rules. I never go to airports in summer. I hate being near airports on holidays. Unworthy.
No vacation packages for me. Those airline packages with a flight, shuttle, and hotel. I've had enough.
I try to avoid crowds and popular spots. July Paris? Nuts and bolts, please. Christmas in NYC? No, please keep me sane.
I fly business class behind. I accept upgrades upon check-in. I prefer driving. I drove from the Netherlands to southern Spain.
Thankfully, no lines. What if travel costs more? Thus? I enjoy it from the start. I start traveling then.
I rarely travel since I'm so difficult. One great excursion beats several average ones.
Personal effectiveness
New apps, tools, and strategies intrigue most productivity professionals.
No.
I researched years ago. I spent years investigating productivity in university.
I bought books, courses, applications, and tools. It was expensive and time-consuming.
Im finished. Productivity no longer costs me time or money. OK. I worked on it once and now follow my strategy.
I avoid new programs and systems. My stuff works. Why change winners?
Spending wisely saves time and money.
Spending wisely means spending once. Many people ignore productivity. It's understudied. No classes.
Some assume reading a few articles or a book is enough. Productivity is personal. You need a personal system.
Time invested is one-time. You can trust your system for life once you find it.
Concentrate on the expensive choices.
Life's short. Saving money quickly is enticing.
Spend less on groceries today. True. That won't fix your finances.
Adopt a lifestyle that makes you affluent over time. Consider major choices.
Are they causing long-term poverty? Are you richer?
Leasing cars comes to mind. The automobile costs a fortune today. The premium could accomplish a million nice things.
Focusing on important decisions makes life easier. Consider your future. You want to improve next year.
Daniel Vassallo
3 years ago
Why I quit a $500K job at Amazon to work for myself
I quit my 8-year Amazon job last week. I wasn't motivated to do another year despite promotions, pay, recognition, and praise.
In AWS, I built developer tools. I could have worked in that field forever.
I became an Amazon developer. Within 3.5 years, I was promoted twice to senior engineer and would have been promoted to principal engineer if I stayed. The company said I had great potential.
Over time, I became a reputed expert and leader within the company. I was respected.
First year I made $75K, last year $511K. If I stayed another two years, I could have made $1M.
Despite Amazon's reputation, my work–life balance was good. I no longer needed to prove myself and could do everything in 40 hours a week. My team worked from home once a week, and I rarely opened my laptop nights or weekends.
My coworkers were great. I had three generous, empathetic managers. I’m very grateful to everyone I worked with.
Everything was going well and getting better. My motivation to go to work each morning was declining despite my career and income growth.
Another promotion, pay raise, or big project wouldn't have boosted my motivation. Motivation was also waning. It was my freedom.
Demotivation
My motivation was high in the beginning. I worked with someone on an internal tool with little scrutiny. I had more freedom to choose how and what to work on than in recent years. Me and another person improved it, talked to users, released updates, and tested it. Whatever we wanted, we did. We did our best and were mostly self-directed.
In recent years, things have changed. My department's most important project had many stakeholders and complex goals. What I could do depended on my ability to convince others it was the best way to achieve our goals.
Amazon was always someone else's terms. The terms started out simple (keep fixing it), but became more complex over time (maximize all goals; satisfy all stakeholders). Working in a large organization imposed restrictions on how to do the work, what to do, what goals to set, and what business to pursue. This situation forced me to do things I didn't want to do.
Finding New Motivation
What would I do forever? Not something I did until I reached a milestone (an exit), but something I'd do until I'm 80. What could I do for the next 45 years that would make me excited to wake up and pay my bills? Is that too unambitious? Nope. Because I'm motivated by two things.
One is an external carrot or stick. I'm not forced to file my taxes every April, but I do because I don't want to go to jail. Or I may not like something but do it anyway because I need to pay the bills or want a nice car. Extrinsic motivation
One is internal. When there's no carrot or stick, this motivates me. This fuels hobbies. I wanted a job that was intrinsically motivated.
Is this too low-key? Extrinsic motivation isn't sustainable. Getting promoted felt good for a week, then it was over. When I hit $100K, I admired my W2 for a few days, but then it wore off. Same thing happened at $200K, $300K, $400K, and $500K. Earning $1M or $10M wouldn't change anything. I feel the same about every material reward or possession. Getting them feels good at first, but quickly fades.
Things I've done since I was a kid, when no one forced me to, don't wear off. Coding, selling my creations, charting my own path, and being honest. Why not always use my strengths and motivation? I'm lucky to live in a time when I can work independently in my field without large investments. So that’s what I’m doing.
What’s Next?
I'm going all-in on independence and will make a living from scratch. I won't do only what I like, but on my terms. My goal is to cover my family's expenses before my savings run out while doing something I enjoy. What more could I want from my work?
You can now follow me on Twitter as I continue to document my journey.
This post is a summary. Read full article here
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.
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Sam Hickmann
3 years ago
Donor-Advised Fund Tax Benefits (DAF)
Giving through a donor-advised fund can be tax-efficient. Using a donor-advised fund can reduce your tax liability while increasing your charitable impact.
Grow Your Donations Tax-Free.
Your DAF's charitable dollars can be invested before being distributed. Your DAF balance can grow with the market. This increases grantmaking funds. The assets of the DAF belong to the charitable sponsor, so you will not be taxed on any growth.
Avoid a Windfall Tax Year.
DAFs can help reduce tax burdens after a windfall like an inheritance, business sale, or strong market returns. Contributions to your DAF are immediately tax deductible, lowering your taxable income. With DAFs, you can effectively pre-fund years of giving with assets from a single high-income event.
Make a contribution to reduce or eliminate capital gains.
One of the most common ways to fund a DAF is by gifting publicly traded securities. Securities held for more than a year can be donated at fair market value and are not subject to capital gains tax. If a donor liquidates assets and then donates the proceeds to their DAF, capital gains tax reduces the amount available for philanthropy. Gifts of appreciated securities, mutual funds, real estate, and other assets are immediately tax deductible up to 30% of Adjusted gross income (AGI), with a five-year carry-forward for gifts that exceed AGI limits.
Using Appreciated Stock as a Gift
Donating appreciated stock directly to a DAF rather than liquidating it and donating the proceeds reduces philanthropists' tax liability by eliminating capital gains tax and lowering marginal income tax.
In the example below, a donor has $100,000 in long-term appreciated stock with a cost basis of $10,000:
Using a DAF would allow this donor to give more to charity while paying less taxes. This strategy often allows donors to give more than 20% more to their favorite causes.
For illustration purposes, this hypothetical example assumes a 35% income tax rate. All realized gains are subject to the federal long-term capital gains tax of 20% and the 3.8% Medicare surtax. No other state taxes are considered.
The information provided here is general and educational in nature. It is not intended to be, nor should it be construed as, legal or tax advice. NPT does not provide legal or tax advice. Furthermore, the content provided here is related to taxation at the federal level only. NPT strongly encourages you to consult with your tax advisor or attorney before making charitable contributions.
Scott Hickmann
3 years ago Draft
This is a draft
My wallpape
The woman
3 years ago
Why Google's Hiring Process is Brilliant for Top Tech Talent
Without a degree and experience, you can get a high-paying tech job.
Most organizations follow this hiring rule: you chat with HR, interview with your future boss and other senior managers, and they make the final hiring choice.
If you've ever applied for a job, you know how arduous it can be. A newly snapped photo and a glossy resume template can wear you out. Applying to Google can change this experience.
According to an Universum report, Google is one of the world's most coveted employers. It's not simply the search giant's name and reputation that attract candidates, but its role requirements or lack thereof.
Candidates no longer need a beautiful resume, cover letter, Ivy League laurels, or years of direct experience. The company requires no degree or experience.
Elon Musk started it. He employed the two-hands test to uncover talented non-graduates. The billionaire eliminated the requirement for experience.
Google is deconstructing traditional employment with programs like the Google Project Management Degree, a free online and self-paced professional credential course.
Google's hiring is interesting. After its certification course, applicants can work in project management. Instead of academic degrees and experience, the company analyzes coursework.
Google finds the best project managers and technical staff in exchange. Google uses three strategies to find top talent.
Chase down the innovators
Google eliminates restrictions like education, experience, and others to find the polar bear amid the snowfall. Google's free project management education makes project manager responsibilities accessible to everyone.
Many jobs don't require a degree. Overlooking individuals without a degree can make it difficult to locate a candidate who can provide value to a firm.
Firsthand knowledge follows the same rule. A lack of past information might be an employer's benefit. This is true for creative teams or businesses that prefer to innovate.
Or when corporations conduct differently from the competition. No-experience candidates can offer fresh perspectives. Fast Company reports that people with no sales experience beat those with 10 to 15 years of experience.
Give the aptitude test first priority.
Google wants the best candidates. Google wouldn't be able to receive more applications if it couldn't screen them for fit. Its well-organized online training program can be utilized as a portfolio.
Google learns a lot about an applicant through completed assignments. It reveals their ability, leadership style, communication capability, etc. The course mimics the job to assess candidates' suitability.
Basic screening questions might provide information to compare candidates. Any size small business can use screening questions and test projects to evaluate prospective employees.
Effective training for employees
Businesses must train employees regardless of their hiring purpose. Formal education and prior experience don't guarantee success. Maintaining your employees' professional knowledge gaps is key to their productivity and happiness. Top-notch training can do that. Learning and development are key to employee engagement, says Bob Nelson, author of 1,001 Ways to Engage Employees.
Google's online certification program isn't available everywhere. Improving the recruiting process means emphasizing aptitude over experience and a degree. Instead of employing new personnel and having them work the way their former firm trained them, train them how you want them to function.
If you want to know more about Google’s recruiting process, we recommend you watch the movie “Internship.”