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Jano le Roux

Jano le Roux

3 years ago

My Top 11 Tools For Building A Modern Startup, With A Free Plan

More on Productivity

Aldric Chen

Aldric Chen

3 years ago

Jack Dorsey's Meeting Best Practice was something I tried. It Performs Exceptionally Well in Consulting Engagements.

Photo by Cherrydeck on Unsplash

Yes, client meetings are difficult. Especially when I'm alone.

Clients must tell us their problems so we can help.

In-meeting challenges contribute nothing to our work. Consider this:

  • Clients are unprepared.

  • Clients are distracted.

  • Clients are confused.

Introducing Jack Dorsey's Google Doc approach

I endorse his approach to meetings.

Not Google Doc-related. Jack uses it for meetings.

This is what his meetings look like.

  • Prior to the meeting, the Chair creates the agenda, structure, and information using Google Doc.

  • Participants in the meeting would have 5-10 minutes to read the Google Doc.

  • They have 5-10 minutes to type their comments on the document.

  • In-depth discussion begins

There is elegance in simplicity. Here's how Jack's approach is fantastic.

Unprepared clients are given time to read.

During the meeting, they think and work on it.

They can see real-time remarks from others.

Discussion ensues.

Three months ago, I fell for this strategy. After trying it with a client, I got good results.

I conducted social control experiments in a few client workshops.

Context matters.

I am sure Jack Dorsey’s method works well in meetings. What about client workshops?

So, I tested Enterprise of the Future with a consulting client.

I sent multiple emails to client stakeholders describing the new approach.

No PowerPoints that day. I spent the night setting up the Google Doc with conversation topics, critical thinking questions, and a Before and After section.

The client was shocked. First, a Google Doc was projected. Second surprise was a verbal feedback.

“No pre-meeting materials?”

“Don’t worry. I know you are not reading it before our meeting, anyway.”

We laughed. The experiment started.

Observations throughout a 90-minute engagement workshop from beginning to end

For 10 minutes, the workshop was silent.

People read the Google Doc. For some, the silence was unnerving.

“Are you not going to present anything to us?”

I said everything's in Google Doc. I asked them to read, remark, and add relevant paragraphs.

As they unlocked their laptops, they were annoyed.

Ten client stakeholders are typing on the Google Doc. My laptop displays comment bubbles, red lines, new paragraphs, and strikethroughs.

The first 10 minutes were productive. Everyone has seen and contributed to the document.

I was silent.

The move to a classical workshop was smooth. I didn't stimulate dialogue. They did.

Stephanie asked Joe why a blended workforce hinders company productivity. She questioned his comments and additional paragraphs.

That is when a light bulb hit my head. Yes, you want to speak to the right person to resolve issues!

Not only that was discussed. Others discussed their remark bubbles with neighbors. Debate circles sprung up one after the other.

The best part? I asked everyone to add their post-discussion thoughts on a Google Doc.

After the workshop, I have:

  • An agreement-based working document

  • A post-discussion minutes that are prepared for publication

  • A record of the discussion points that were brought up, argued, and evaluated critically

It showed me how stakeholders viewed their Enterprise of the Future. It allowed me to align with them.

Finale Keynotes

Client meetings are a hit-or-miss. I know that.

Jack Dorsey's meeting strategy works for consulting. It promotes session alignment.

It relieves clients of preparation.

I get the necessary information to advance this consulting engagement.

It is brilliant.

Todd Lewandowski

Todd Lewandowski

3 years ago

DWTS: How to Organize Your To-Do List Quickly

Don't overcomplicate to-do lists. DWTS (Done, Waiting, Top 3, Soon) organizes your to-dos.

Everyone’s got a system.

How Are You Going to Manage Everything?

Modern America is busy. Work involves meetings. Anytime, Slack communications arrive. Many software solutions offer a @-mention notification capability. Emails.

Work obligations continue. At home, there are friends, family, bills, chores, and fun things.

How are you going to keep track of it all? Enter the todo list. It’s been around forever. It’s likely to stay forever in some way, shape, or form.

Everybody has their own system. You probably modified something from middle school. Post-its? Maybe it’s an app? Maybe both, another system, or none.

I suggest a format that has worked for me in 15 years of professional and personal life.

Try it out and see if it works for you. If not, no worries. You do you! Hopefully though you can learn a thing or two, and I from you too.

It is merely a Google Doc, yes.

As an example, here’s my personal todo list. Don’t worry, there’s nothing here I don’t mind sharing.

It's a giant list. One task per line. Indent subtasks on a new line. Add or move new tasks as needed.

I recommend using Google Docs. It's easy to use and flexible for structuring.

Prioritizing these tasks is key. I organize them using DWTS (Done, Waiting, Top 3, Soon). Chronologically is good because it implicitly provides both a priority (high, medium, low) and an ETA (now, soon, later).

Yes, I recognize the similarities to DWTS (Dancing With The Stars) TV Show. Although I'm not a fan, it's entertaining. The acronym is easy to remember and adds fun to something dull.

That feeling when you complete everything on your todo list.

What each section contains

Done

All tasks' endpoint. Finish here. Don't worry about it again.

Waiting

You're blocked and can't continue. Blocked tasks usually need someone. Write Person Task so you know who's waiting.

Blocking tasks shouldn't last long. After a while, remind them kindly. If people don't help you out of kindness, they will if you're persistent.

Top 3

Mental focus areas. These can be short- to mid-term goals or recent accomplishments. 2 to 5 is a good number to stay focused.

Top 3 reminds us to prioritize. If they don't fit your Top 3 goals, delay them.

Every 1:1 at work is a project update. Another chance to list your top 3. You should know your Top 3 well and be able to discuss them confidently.

Soon

Here's your short-term to-do list. Rank them from highest to lowest.

I usually subdivide it with empty lines. First is what I have to do today, then week, then month. Subsections can be arranged however you like.

Inventories by Concept

Tasks that aren’t in your short or medium future go into the backlog. 
Eventually you’ll complete these tasks, assign them to someone else, or mark them as “wont’ do” (like done but in another sense).

Backlog tasks don't need to be organized chronologically because their timing and priority may change. Theme-organize them. When planning/strategic, you can choose themes to focus on, so future top 3 topics.

More Tips on Todos

Decide Upon a Morning Goal

Morning routines are universal. Coffee and Wordle. My to-do list is next. Two things:

  • As needed, update the to-do list: based on the events of yesterday and any fresh priorities.

  • Pick a few jobs to complete today: Pick a few goals that you know you can complete today. Push the remainder below and move them to the top of the Soon section. I typically select a few tasks I am confident I can complete along with one stretch task that might extend into tomorrow.

Finally. By setting and achieving small goals every day, you feel accomplished and make steady progress on medium and long-term goals.

Tech companies call this a daily standup. Everyone shares what they did yesterday, what they're doing today, and any blockers. The name comes from a tradition of holding meetings while standing up to keep them short. Even though it's virtual, everyone still wants a quick meeting.

Your team may or may not need daily standups. Make a daily review a habit with your coffee.

Review Backwards & Forwards on a regular basis

While you're updating your to-do list daily, take time to review it.

Review your Done list. Remember things you're proud of and things that could have gone better. Your Done list can be long. Archive it so your main to-do list isn't overwhelming.

Future-gaze. What you considered important may no longer be. Reorder tasks. Backlog grooming is a workplace term.

Backwards-and-forwards reviews aren't required often. Every 3-6 months is fine. They help you see the forest as often as the trees.

Final Remarks

Keep your list simple. Done, Waiting, Top 3, Soon. These are the necessary sections. If you like, add more subsections; otherwise, keep it simple.

I recommend a morning review. By having clear goals and an action-oriented attitude, you'll be successful.

Leonardo Castorina

Leonardo Castorina

3 years ago

How to Use Obsidian to Boost Research Productivity

Tools for managing your PhD projects, reading lists, notes, and inspiration.

As a researcher, you have to know everything. But knowledge is useless if it cannot be accessed quickly. An easy-to-use method of archiving information makes taking notes effortless and enjoyable.
As a PhD student in Artificial Intelligence, I use Obsidian (https://obsidian.md) to manage my knowledge.

The article has three parts:

  1. What is a note, how to organize notes, tags, folders, and links? This section is tool-agnostic, so you can use most of these ideas with any note-taking app.
  2. Instructions for using Obsidian, managing notes, reading lists, and useful plugins. This section demonstrates how I use Obsidian, my preferred knowledge management tool.
  3. Workflows: How to use Zotero to take notes from papers, manage multiple projects' notes, create MOCs with Dataview, and more. This section explains how to use Obsidian to solve common scientific problems and manage/maintain your knowledge effectively.

This list is not perfect or complete, but it is my current solution to problems I've encountered during my PhD. Please leave additional comments or contact me if you have any feedback. I'll try to update this article.
Throughout the article, I'll refer to your digital library as your "Obsidian Vault" or "Zettelkasten".
Other useful resources are listed at the end of the article.

1. Philosophy: Taking and organizing notes

Carl Sagan: “To make an apple pie from scratch, you must first create the universe.”

Before diving into Obsidian, let's establish a Personal Knowledge Management System and a Zettelkasten. You can skip to Section 2 if you already know these terms.
Niklas Luhmann, a prolific sociologist who wrote 400 papers and 70 books, inspired this section and much of Zettelkasten. Zettelkasten means “slip box” (or library in this article). His Zettlekasten had around 90000 physical notes, which can be found here.
There are now many tools available to help with this process. Obsidian's website has a good introduction section: https://publish.obsidian.md/hub/

Notes

We'll start with "What is a note?" Although it may seem trivial, the answer depends on the topic or your note-taking style. The idea is that a note is as “atomic” (i.e. You should read the note and get the idea right away.
The resolution of your notes depends on their detail. Deep Learning, for example, could be a general description of Neural Networks, with a few notes on the various architectures (eg. Recurrent Neural Networks, Convolutional Neural Networks etc..).
Limiting length and detail is a good rule of thumb. If you need more detail in a specific section of this note, break it up into smaller notes. Deep Learning now has three notes:

  • Deep Learning
  • Recurrent Neural Networks
    - Convolutional Neural Networks

Repeat this step as needed until you achieve the desired granularity. You might want to put these notes in a “Neural Networks” folder because they are all about the same thing. But there's a better way:

#Tags and [[Links]] over /Folders/

The main issue with folders is that they are not flexible and assume that all notes in the folder belong to a single category. This makes it difficult to make connections between topics.
Deep Learning has been used to predict protein structure (AlphaFold) and classify images (ImageNet). Imagine a folder structure like this:

- /Proteins/ 
     - Protein Folding
- /Deep Learning/
     - /Proteins/ 

Your notes about Protein Folding and Convolutional Neural Networks will be separate, and you won't be able to find them in the same folder.
This can be solved in several ways. The most common one is to use tags rather than folders. A note can be grouped with multiple topics this way. Obsidian tags can also be nested (have subtags).

You can also link two notes together. You can build your “Knowledge Graph” in Obsidian and other note-taking apps like Obsidian.


My Knowledge Graph. Green: Biology, Red: Machine Learning, Yellow: Autoencoders, Blue: Graphs, Brown: Tags.


My Knowledge Graph and the note “Backrpropagation” and its links.


Backpropagation note and all its links

Why use Folders?

Folders help organize your vault as it grows. The main suggestion is to have few folders that "weakly" collect groups of notes or better yet, notes from different sources.
Among my Zettelkasten folders are:


My Zettelkasten's 5 folders

They usually gather data from various sources:

MOC: Map of Contents for the Zettelkasten.
Projects: Contains one note for each side-project of my PhD where I log my progress and ideas. Notes are linked to these.
Bio and ML: These two are the main content of my Zettelkasten and could theoretically be combined.
Papers: All my scientific paper notes go here. A bibliography links the notes. Zotero .bib file
Books: I make a note for each book I read, which I then split into multiple notes.

Keeping images separate from other files can help keep your main folders clean.

I will elaborate on these in the Workflow Section.

My general recommendation is to use tags and links instead of folders.

Maps of Content (MOC) 

Making Tables of Contents is a good solution (MOCs).
These are notes that "signposts" your Zettelkasten library, directing you to the right type of notes. It can link to other notes based on common tags. This is usually done with a title, then your notes related to that title. As an example:

An example of a Machine Learning MOC generated with Dataview.

As shown above, my Machine Learning MOC begins with the basics. Then it's on to Variational Auto-Encoders. Not only does this save time, but it also saves scrolling through the tag search section.
So I keep MOCs at the top of my library so I can quickly find information and see my library. These MOCs are generated automatically using an Obsidian Plugin called Dataview (https://github.com/blacksmithgu/obsidian-dataview).
Ideally, MOCs could be expanded to include more information about the notes, their status, and what's left to do. In the absence of this, Dataview does a fantastic job at creating a good structure for your notes.
In the absence of this, Dataview does a fantastic job at creating a good structure for your notes.

2. Tools: Knowing Obsidian

Obsidian is my preferred tool because it is free, all notes are stored in Markdown format, and each panel can be dragged and dropped. You can get it here: https://obsidian.md/

Obsidian interface. 

Obsidian is highly customizable, so here is my preferred interface:


The theme is customized from https://github.com/colineckert/obsidian-things

Alternatively, each panel can be collapsed, moved, or removed as desired. To open a panel later, click on the vertical "..." (bottom left of the note panel).

My interface is organized as follows:

How my Obsidian Interface is organized.

Folders/Search:
This is where I keep all relevant folders. I usually use the MOC note to navigate, but sometimes I use the search button to find a note.

Tags:
I use nested tags and look into each one to find specific notes to link.

cMenu:
Easy-to-use menu plugin cMenu (https://github.com/chetachiezikeuzor/cMenu-Plugin)

Global Graph:
The global graph shows all your notes (linked and unlinked). Linked notes will appear closer together. Zoom in to read each note's title. It's a bit overwhelming at first, but as your library grows, you get used to the positions and start thinking of new connections between notes.

Local Graph:
Your current note will be shown in relation to other linked notes in your library. When needed, you can quickly jump to another link and back to the current note.

Links:
Finally, an outline panel and the plugin Obsidian Power Search (https://github.com/aviral-batra/obsidian-power-search) allow me to search my vault by highlighting text.

Start using the tool and worry about panel positioning later. I encourage you to find the best use-case for your library.

Plugins

An additional benefit of using Obsidian is the large plugin library. I use several (Calendar, Citations, Dataview, Templater, Admonition):
Obsidian Calendar Plugin: https://github.com/liamcain
It organizes your notes on a calendar. This is ideal for meeting notes or keeping a journal.

Calendar addon from hans/obsidian-citation-plugin
Obsidian Citation Plugin: https://github.com/hans/
Allows you to cite papers from a.bib file. You can also customize your notes (eg. Title, Authors, Abstract etc..)

Plugin citation from hans/obsidian-citation-plugin
Obsidian Dataview: https://github.com/blacksmithgu/
A powerful plugin that allows you to query your library as a database and generate content automatically. See the MOC section for an example.
Allows you to create notes with specific templates like dates, tags, and headings.

Templater. Obsidian Admonition: https://github.com/valentine195/obsidian-admonition
Blocks allow you to organize your notes.

Plugin warning. Obsidian Admonition (valentine195)
There are many more, but this list should get you started.

3. Workflows: Cool stuff

Here are a few of my workflows for using obsidian for scientific research. This is a list of resources I've found useful for my use-cases. I'll outline and describe them briefly so you can skim them quickly.
3.1 Using Templates to Structure Notes
3.2 Free Note Syncing (Laptop, Phone, Tablet)
3.3 Zotero/Mendeley/JabRef -> Obsidian — Managing Reading Lists
3.4 Projects and Lab Books
3.5 Private Encrypted Diary

3.1 Using Templates to Structure Notes

Plugins: Templater and Dataview (optional).
To take effective notes, you must first make adding new notes as easy as possible. Templates can save you time and give your notes a consistent structure. As an example:


An example of a note using a template.

### [[YOUR MOC]]
# Note Title of your note
**Tags**:: 
**Links**::

The top line links to your knowledge base's Map of Content (MOC) (see previous sections). After the title, I add tags (and a link between the note and the tag) and links to related notes.
To quickly identify all notes that need to be expanded, I add the tag “#todo”. In the “TODO:” section, I list the tasks within the note.
The rest are notes on the topic.
Templater can help you create these templates. For new books, I use the following template:

### [[Books MOC]]
# Title
**Author**:: 
**Date::
**Tags:: 
**Links::


A book template example.

Using a simple query, I can hook Dataview to it.

dataview  
table author as Author, date as “Date Finished”, tags as “Tags”, grade as “Grade”  
from “4. Books”  
SORT grade DESCENDING


using Dataview to query templates.

3.2 Free Note Syncing (Laptop, Phone, Tablet)

No plugins used.

One of my favorite features of Obsidian is the library's self-contained and portable format. Your folder contains everything (plugins included).

Ordinary folders and documents are available as well. There is also a “.obsidian” folder. This contains all your plugins and settings, so you can use it on other devices.
So you can use Google Drive, iCloud, or Dropbox for free as long as you sync your folder (note: your folder should be in your Cloud Folder).

For my iOS and macOS work, I prefer iCloud. You can also use the paid service Obsidian Sync.
3.3 Obsidian — Managing Reading Lists and Notes in Zotero/Mendeley/JabRef
Plugins: Quotes (required).

3.3 Zotero/Mendeley/JabRef -> Obsidian — Taking Notes and Managing Reading Lists of Scientific Papers

My preferred reference manager is Zotero, but this workflow should work with any reference manager that produces a .bib file. This file is exported to my cloud folder so I can access it from any platform.

My Zotero library is tagged as follows:

My reference manager's tags

For readings, I usually search for the tags “!!!” and “To-Read” and select a paper. Annotate the paper next (either on PDF using GoodNotes or on physical paper).
Then I make a paper page using a template in the Citations plugin settings:


An example of my citations template.

Create a new note, open the command list with CMD/CTRL + P, and find the Citations “Insert literature note content in the current pane” to see this lovely view.


Citation generated by the article https://doi.org/10.1101/2022.01.24.22269144

You can then convert your notes to digital. I found that transcribing helped me retain information better.

3.4 Projects and Lab Books

Plugins: Tweaker (required).
PhD students offering advice on thesis writing are common (read as regret). I started asking them what they would have done differently or earlier.

“Deep stuff Leo,” one person said. So my main issue is basic organization, losing track of my tasks and the reasons for them.
As a result, I'd go on other experiments that didn't make sense, and have to reverse engineer my logic for thesis writing. - PhD student now wise Postdoc

Time management requires planning. Keeping track of multiple projects and lab books is difficult during a PhD. How I deal with it:

  • One folder for all my projects
  • One file for each project
    I use a template to create each project
### [[Projects MOC]]  
# <% tp.file.title %>  
**Tags**::  
**Links**::  
**URL**::  
**Project Description**::## Notes:  
### <% tp.file.last_modified_date(“dddd Do MMMM YYYY”) %>  
#### Done:  
#### TODO:  
#### Notes

You can insert a template into a new note with CMD + P and looking for the Templater option.

I then keep adding new days with another template:

### <% tp.file.last_modified_date("dddd Do MMMM YYYY") %>  
#### Done:  
#### TODO:  
#### Notes:

This way you can keep adding days to your project and update with reasonings and things you still have to do and have done. An example below:


Example of project note with timestamped notes.

3.5 Private Encrypted Diary

This is one of my favorite Obsidian uses.
Mini Diary's interface has long frustrated me. After the author archived the project, I looked for a replacement. I had two demands:

  1. It had to be private, and nobody had to be able to read the entries.
  2. Cloud syncing was required for editing on multiple devices.

Then I learned about encrypting the Obsidian folder. Then decrypt and open the folder with Obsidian. Sync the folder as usual.
Use CryptoMator (https://cryptomator.org/). Create an encrypted folder in Cryptomator for your Obsidian vault, set a password, and let it do the rest.
If you need a step-by-step video guide, here it is:

Conclusion

So, I hope this was helpful!
In the first section of the article, we discussed notes and note-taking techniques. We discussed when to use tags and links over folders and when to break up larger notes.
Then we learned about Obsidian, its interface, and some useful plugins like Citations for citing papers and Templater for creating note templates.
Finally, we discussed workflows and how to use Zotero to take notes from scientific papers, as well as managing Lab Books and Private Encrypted Diaries.
Thanks for reading and commenting :)

Read original post here

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Langston Thomas

3 years ago

A Simple Guide to NFT Blockchains

Ethereum's blockchain rules NFTs. Many consider it the one-stop shop for NFTs, and it's become the most talked-about and trafficked blockchain in existence.

Other blockchains are becoming popular in NFTs. Crypto-artists and NFT enthusiasts have sought new places to mint and trade NFTs due to Ethereum's high transaction costs and environmental impact.

When choosing a blockchain to mint on, there are several factors to consider. Size, creator costs, consumer spending habits, security, and community input are important. We've created a high-level summary of blockchains for NFTs to help clarify the fast-paced world of web3 tech.

Ethereum

Ethereum currently has the most NFTs. It's decentralized and provides financial and legal services without intermediaries. It houses popular NFT marketplaces (OpenSea), projects (CryptoPunks and the Bored Ape Yacht Club), and artists (Pak and Beeple).

It's also expensive and energy-intensive. This is because Ethereum works using a Proof-of-Work (PoW) mechanism. PoW requires computers to solve puzzles to add blocks and transactions to the blockchain. Solving these puzzles requires a lot of computer power, resulting in astronomical energy loss.

You should consider this blockchain first due to its popularity, security, decentralization, and ease of use.

Solana

Solana is a fast programmable blockchain. Its proof-of-history and proof-of-stake (PoS) consensus mechanisms eliminate complex puzzles. Reduced validation times and fees result.

PoS users stake their cryptocurrency to become a block validator. Validators get SOL. This encourages and rewards users to become stakers. PoH works with PoS to cryptographically verify time between events. Solana blockchain ensures transactions are in order and found by the correct leader (validator).

Solana's PoS and PoH mechanisms keep transaction fees and times low. Solana isn't as popular as Ethereum, so there are fewer NFT marketplaces and blockchain traders.

Tezos

Tezos is a greener blockchain. Tezos rose in 2021. Hic et Nunc was hailed as an economic alternative to Ethereum-centric marketplaces until Nov. 14, 2021.

Similar to Solana, Tezos uses a PoS consensus mechanism and only a PoS mechanism to reduce computational work. This blockchain uses two million times less energy than Ethereum. It's cheaper than Ethereum (but does cost more than Solana).

Tezos is a good place to start minting NFTs in bulk. Objkt is the largest Tezos marketplace.

Flow

Flow is a high-performance blockchain for NFTs, games, and decentralized apps (dApps). Flow is built with scalability in mind, so billions of people could interact with NFTs on the blockchain.

Flow became the NBA's blockchain partner in 2019. Flow, a product of Dapper labs (the team behind CryptoKitties), launched and hosts NBA Top Shot, making the blockchain integral to the popularity of non-fungible tokens.

Flow uses PoS to verify transactions, like Tezos. Developers are working on a model to handle 10,000 transactions per second on the blockchain. Low transaction fees.

Flow NFTs are tradeable on Blocktobay, OpenSea, Rarible, Foundation, and other platforms. NBA, NFL, UFC, and others have launched NFT marketplaces on Flow. Flow isn't as popular as Ethereum, resulting in fewer NFT marketplaces and blockchain traders.

Asset Exchange (WAX)

WAX is king of virtual collectibles. WAX is popular for digitalized versions of legacy collectibles like trading cards, figurines, memorabilia, etc.

Wax uses a PoS mechanism, but also creates carbon offset NFTs and partners with Climate Care. Like Flow, WAX transaction fees are low, and network fees are redistributed to the WAX community as an incentive to collectors.

WAX marketplaces host Topps, NASCAR, Hot Wheels, and cult classic film franchises like Godzilla, The Princess Bride, and Spiderman.

Binance Smart Chain

BSC is another good option for balancing fees and performance. High-speed transactions and low fees hurt decentralization. BSC is most centralized.

Binance Smart Chain uses Proof of Staked Authority (PoSA) to support a short block time and low fees. The 21 validators needed to run the exchange switch every 24 hours. 11 of the 21 validators are directly connected to the Binance Crypto Exchange, according to reports.

While many in the crypto and NFT ecosystems dislike centralization, the BSC NFT market picked up speed in 2021. OpenBiSea, AirNFTs, JuggerWorld, and others are gaining popularity despite not having as robust an ecosystem as Ethereum.

Zuzanna Sieja

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:

  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.

Will Lockett

Will Lockett

3 years ago

Russia's nukes may be useless

Russia's nuclear threat may be nullified by physics.

Putin seems nostalgic and wants to relive the Cold War. He's started a deadly war to reclaim the old Soviet state of Ukraine and is threatening the West with nuclear war. NATO can't risk starting a global nuclear war that could wipe out humanity to support Ukraine's independence as much as they want to. Fortunately, nuclear physics may have rendered Putin's nuclear weapons useless. However? How will Ukraine and NATO react?

To understand why Russia's nuclear weapons may be ineffective, we must first know what kind they are.

Russia has the world's largest nuclear arsenal, with 4,447 strategic and 1,912 tactical weapons (all of which are ready to be rolled out quickly). The difference between these two weapons is small, but it affects their use and logistics. Strategic nuclear weapons are ICBMs designed to destroy a city across the globe. Russia's ICBMs have many designs and a yield of 300–800 kilotonnes. 300 kilotonnes can destroy Washington. Tactical nuclear weapons are smaller and can be fired from artillery guns or small truck-mounted missile launchers, giving them a 1,500 km range. Instead of destroying a distant city, they are designed to eliminate specific positions, bases, or military infrastructure. They produce 1–50 kilotonnes.

These two nuclear weapons use different nuclear reactions. Pure fission bombs are compact enough to fit in a shell or small missile. All early nuclear weapons used this design for their fission bombs. This technology is inefficient for bombs over 50 kilotonnes. Larger bombs are thermonuclear. Thermonuclear weapons use a small fission bomb to compress and heat a hydrogen capsule, which undergoes fusion and releases far more energy than ignition fission reactions, allowing for effective giant bombs. 

Here's Russia's issue.

A thermonuclear bomb needs deuterium (hydrogen with one neutron) and tritium (hydrogen with two neutrons). Because these two isotopes fuse at lower energies than others, the bomb works. One problem. Tritium is highly radioactive, with a half-life of only 12.5 years, and must be artificially made.

Tritium is made by irradiating lithium in nuclear reactors and extracting the gas. Tritium is one of the most expensive materials ever made, at $30,000 per gram.

Why does this affect Putin's nukes?

Thermonuclear weapons need tritium. Tritium decays quickly, so they must be regularly refilled at great cost, which Russia may struggle to do.

Russia has a smaller economy than New York, yet they are running an invasion, fending off international sanctions, and refining tritium for 4,447 thermonuclear weapons.

The Russian military is underfunded. Because the state can't afford it, Russian troops must buy their own body armor. Arguably, Putin cares more about the Ukraine conflict than maintaining his nuclear deterrent. Putin will likely lose power if he loses the Ukraine war.

It's possible that Putin halted tritium production and refueling to save money for Ukraine. His threats of nuclear attacks and escalating nuclear war may be a bluff.

This doesn't help Ukraine, sadly. Russia's tactical nuclear weapons don't need expensive refueling and will help with the invasion. So Ukraine still risks a nuclear attack. The bomb that destroyed Hiroshima was 15 kilotonnes, and Russia's tactical Iskander-K nuclear missile has a 50-kiloton yield. Even "little" bombs are deadly.

We can't guarantee it's happening in Russia. Putin may prioritize tritium. He knows the power of nuclear deterrence. Russia may have enough tritium for this conflict. Stockpiling a material with a short shelf life is unlikely, though.

This means that Russia's most powerful weapons may be nearly useless, but they may still be deadly. If true, this could allow NATO to offer full support to Ukraine and push the Russian tyrant back where he belongs. If Putin withholds funds from his crumbling military to maintain his nuclear deterrent, he may be willing to sink the ship with him. Let's hope the former.