More on Personal Growth
Glorin Santhosh
3 years ago
Start organizing your ideas by using The Second Brain.
Building A Second Brain helps us remember connections, ideas, inspirations, and insights. Using contemporary technologies and networks increases our intelligence.
This approach makes and preserves concepts. It's a straightforward, practical way to construct a second brain—a remote, centralized digital store for your knowledge and its sources.
How to build ‘The Second Brain’
Have you forgotten any brilliant ideas? What insights have you ignored?
We're pressured to read, listen, and watch informative content. Where did the data go? What happened?
Our brains can store few thoughts at once. Our brains aren't idea banks.
Building a Second Brain helps us remember thoughts, connections, and insights. Using digital technologies and networks expands our minds.
Ten Rules for Creating a Second Brain
1. Creative Stealing
Instead of starting from scratch, integrate other people's ideas with your own.
This way, you won't waste hours starting from scratch and can focus on achieving your goals.
Users of Notion can utilize and customize each other's templates.
2. The Habit of Capture
We must record every idea, concept, or piece of information that catches our attention since our minds are fragile.
When reading a book, listening to a podcast, or engaging in any other topic-related activity, save and use anything that resonates with you.
3. Recycle Your Ideas
Reusing our own ideas across projects might be advantageous since it helps us tie new information to what we already know and avoids us from starting a project with no ideas.
4. Projects Outside of Category
Instead of saving an idea in a folder, group it with documents for a project or activity.
If you want to be more productive, gather suggestions.
5. Burns Slowly
Even if you could finish a job, work, or activity if you focused on it, you shouldn't.
You'll get tired and can't advance many projects. It's easier to divide your routine into daily tasks.
Few hours of daily study is more productive and healthier than entire nights.
6. Begin with a surplus
Instead of starting with a blank sheet when tackling a new subject, utilise previous articles and research.
You may have read or saved related material.
7. Intermediate Packets
A bunch of essay facts.
You can utilize it as a document's section or paragraph for different tasks.
Memorize useful information so you can use it later.
8. You only know what you make
We can see, hear, and read about anything.
What matters is what we do with the information, whether that's summarizing it or writing about it.
9. Make it simpler for yourself in the future.
Create documents or files that your future self can easily understand. Use your own words, mind maps, or explanations.
10. Keep your thoughts flowing.
If you don't employ the knowledge in your second brain, it's useless.
Few people exercise despite knowing its benefits.
Conclusion:
You may continually move your activities and goals closer to completion by organizing and applying your information in a way that is results-focused.
Profit from the information economy's explosive growth by turning your specialized knowledge into cash.
Make up original patterns and linkages between topics.
You may reduce stress and information overload by appropriately curating and managing your personal information stream.
Learn how to apply your significant experience and specific knowledge to a new job, business, or profession.
Without having to adhere to tight, time-consuming constraints, accumulate a body of relevant knowledge and concepts over time.
Take advantage of all the learning materials that are at your disposal, including podcasts, online courses, webinars, books, and articles.
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.
Aparna Jain
3 years ago
Negative Effects of Working for a FAANG Company
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.
large salaries and benefits
Prestige
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
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.
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
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.
Keep being proactive and look for mentorship elsewhere if you believe your boss doesn't have enough time to work on your career goals.
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
Your life is not worth a company.
Do you want your job title and workplace to be listed on your gravestone? If not, leave if conditions deteriorate.
Recognize that change can be challenging. It's difficult to leave a job you've held for a number of years.
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.
You might also like
Sam Hickmann
3 years ago
A quick guide to formatting your text on INTΞGRITY
[06/20/2022 update] We have now implemented a powerful text editor, but you can still use markdown.
Markdown:
Headers
SYNTAX:
# This is a heading 1
## This is a heading 2
### This is a heading 3
#### This is a heading 4
RESULT:
This is a heading 1
This is a heading 2
This is a heading 3
This is a heading 4
Emphasis
SYNTAX:
**This text will be bold**
~~Strikethrough~~
*You **can** combine them*
RESULT:
This text will be italic
This text will be bold
You can combine them
Images
SYNTAX:
RESULT:
Videos
SYNTAX:
https://www.youtube.com/watch?v=7KXGZAEWzn0
RESULT:
Links
SYNTAX:
[Int3grity website](https://www.int3grity.com)
RESULT:
Tweets
SYNTAX:
https://twitter.com/samhickmann/status/1503800505864130561
RESULT:
Blockquotes
SYNTAX:
> Human beings face ever more complex and urgent problems, and their effectiveness in dealing with these problems is a matter that is critical to the stability and continued progress of society. \- Doug Engelbart, 1961
RESULT:
Human beings face ever more complex and urgent problems, and their effectiveness in dealing with these problems is a matter that is critical to the stability and continued progress of society. - Doug Engelbart, 1961
Inline code
SYNTAX:
Text inside `backticks` on a line will be formatted like code.
RESULT:
Text inside backticks on a line will be formatted like code.
Code blocks
SYNTAX:
'''js
function fancyAlert(arg) {
if(arg) {
$.facebox({div:'#foo'})
}
}
'''
RESULT:
function fancyAlert(arg) {
if(arg) {
$.facebox({div:'#foo'})
}
}
Maths
We support LaTex to typeset math. We recommend reading the full documentation on the official website
SYNTAX:
$$[x^n+y^n=z^n]$$
RESULT:
[x^n+y^n=z^n]
Tables
SYNTAX:
| header a | header b |
| ---- | ---- |
| row 1 col 1 | row 1 col 2 |
RESULT:
| header a | header b | header c |
|---|---|---|
| row 1 col 1 | row 1 col 2 | row 1 col 3 |
Alex Mathers
24 years ago
400 articles later, nobody bothered to read them.
Writing for readers:
14 years of daily writing.
I post practically everything on social media. I authored hundreds of articles, thousands of tweets, and numerous volumes to almost no one.
Tens of thousands of readers regularly praise me.
I despised writing. I'm stuck now.
I've learned what readers like and what doesn't.
Here are some essential guidelines for writing with impact:
Readers won't understand your work if you can't.
Though obvious, this slipped me up. Share your truths.
Stories engage human brains.
Showing the journey of a person from worm to butterfly inspires the human spirit.
Overthinking hinders powerful writing.
The best ideas come from inner understanding in between thoughts.
Avoid writing to find it. Write.
Writing a masterpiece isn't motivating.
Write for five minutes to simplify. Step-by-step, entertaining, easy steps.
Good writing requires a willingness to make mistakes.
So write loads of garbage that you can edit into a good piece.
Courageous writing.
A courageous story will move readers. Personal experience is best.
Go where few dare.
Templates, outlines, and boundaries help.
Limitations enhance writing.
Excellent writing is straightforward and readable, removing all the unnecessary fat.
Use five words instead of nine.
Use ordinary words instead of uncommon ones.
Readers desire relatability.
Too much perfection will turn it off.
Write to solve an issue if you can't think of anything to write.
Instead, read to inspire. Best authors read.
Every tweet, thread, and novel must have a central idea.
What's its point?
This can make writing confusing.
️ Don't direct your reader.
Readers quit reading. Demonstrate, describe, and relate.
Even if no one responds, have fun. If you hate writing it, the reader will too.
Aure's Notes
3 years ago
I met a man who in just 18 months scaled his startup to $100 million.
A fascinating business conversation.
This week at Web Summit, I had mentor hour.
Mentor hour connects startups with experienced entrepreneurs.
The YC-selected founder who mentored me had grown his company to $100 million in 18 months.
I had 45 minutes to question him.
I've compiled this.
Context
Founder's name is Zack.
After working in private equity, Zack opted to acquire an MBA.
Surrounded by entrepreneurs at a prominent school, he decided to become one himself.
Unsure how to proceed, he bet on two horses.
On one side, he received an offer from folks who needed help running their startup owing to lack of time. On the other hand, he had an idea for a SaaS to start himself.
He just needed to validate it.
Validating
Since Zack's proposal helped companies, he contacted university entrepreneurs for comments.
He contacted university founders.
Once he knew he'd correctly identified the problem and that people were willing to pay to address it, he started developing.
He earned $100k in a university entrepreneurship competition.
His plan was evident by then.
The other startup's founders saw his potential and granted him $400k to launch his own SaaS.
Hiring
He started looking for a tech co-founder because he lacked IT skills.
He interviewed dozens and picked the finest.
As he didn't want to wait for his program to be ready, he contacted hundreds of potential clients and got 15 letters of intent promising they'd join up when it was available.
YC accepted him by then.
He had enough positive signals to raise.
Raising
He didn't say how many VCs he called, but he indicated 50 were interested.
He jammed meetings into two weeks to generate pressure and encourage them to invest.
Seed raise: $11 million.
Selling
His objective was to contact as many entrepreneurs as possible to promote his product.
He first contacted startups by scraping CrunchBase data.
Once he had more money, he started targeting companies with ZoomInfo.
His VC urged him not to hire salespeople until he closed 50 clients himself.
He closed 100 and hired a CRO through a headhunter.
Scaling
Three persons started the business.
He primarily works in sales.
Coding the product was done by his co-founder.
Another person performing operational duties.
He regretted recruiting the third co-founder, who was ineffective (could have hired an employee instead).
He wanted his company to be big, so he hired two young marketing people from a competing company.
After validating several marketing channels, he chose PR.
$100 Million and under
He developed a sales team and now employs 30 individuals.
He raised a $100 million Series A.
Additionally, he stated
He’s been rejected a lot. Like, a lot.
Two great books to read: Steve Jobs by Isaacson, and Why Startups Fail by Tom Eisenmann.
The best skill to learn for non-tech founders is “telling stories”, which means sales. A founder’s main job is to convince: co-founders, employees, investors, and customers. Learn code, or learn sales.
Conclusion
I often read about these stories but hardly take them seriously.
Zack was amazing.
Three things about him stand out:
His vision. He possessed a certain amount of fire.
His vitality. The man had a lot of enthusiasm and spoke quickly and decisively. He takes no chances and pushes the envelope in all he does.
His Rolex.
He didn't do all this in 18 months.
Not really.
He couldn't launch his company without private equity experience.
These accounts disregard entrepreneurs' original knowledge.
Hormozi will tell you how he founded Gym Launch, but he won't tell you how he had a gym first, how he worked at uni to pay for his gym, or how he went to the gym and learnt about fitness, which gave him the idea to open his own.
Nobody knows nothing. If you scale quickly, it's probable because you gained information early.
Lincoln said, "Give me six hours to chop down a tree, and I'll spend four sharpening the axe."
Sharper axes cut trees faster.