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:

![Engelbart](https://history-computer.com/ModernComputer/Basis/images/Engelbart.jpg)

RESULT:

Videos

SYNTAX:

https://www.youtube.com/watch?v=7KXGZAEWzn0

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

(Edited)

More on Web3 & Crypto

OnChain Wizard

3 years ago

How to make a >800 million dollars in crypto attacking the once 3rd largest stablecoin, Soros style

Everyone is talking about the $UST attack right now, including Janet Yellen. But no one is talking about how much money the attacker made (or how brilliant it was). Lets dig in.

Our story starts in late March, when the Luna Foundation Guard (or LFG) starts buying BTC to help back $UST. LFG started accumulating BTC on 3/22, and by March 26th had a $1bn+ BTC position. This is leg #1 that made this trade (or attack) brilliant.

The second leg comes in the form of the 4pool Frax announcement for $UST on April 1st. This added the second leg needed to help execute the strategy in a capital efficient way (liquidity will be lower and then the attack is on).

We don't know when the attacker borrowed 100k BTC to start the position, other than that it was sold into Kwon's buying (still speculation). LFG bought 15k BTC between March 27th and April 11th, so lets just take the average price between these dates ($42k).

So you have a ~$4.2bn short position built. Over the same time, the attacker builds a $1bn OTC position in $UST. The stage is now set to create a run on the bank and get paid on your BTC short. In anticipation of the 4pool, LFG initially removes $150mm from 3pool liquidity.

The liquidity was pulled on 5/8 and then the attacker uses $350mm of UST to drain curve liquidity (and LFG pulls another $100mm of liquidity).

But this only starts the de-pegging (down to 0.972 at the lows). LFG begins selling $BTC to defend the peg, causing downward pressure on BTC while the run on $UST was just getting started.

With the Curve liquidity drained, the attacker used the remainder of their $1b OTC $UST position ($650mm or so) to start offloading on Binance. As withdrawals from Anchor turned from concern into panic, this caused a real de-peg as people fled for the exits

So LFG is selling $BTC to restore the peg while the attacker is selling $UST on Binance. Eventually the chain gets congested and the CEXs suspend withdrawals of $UST, fueling the bank run panic. $UST de-pegs to 60c at the bottom, while $BTC bleeds out.

The crypto community panics as they wonder how much $BTC will be sold to keep the peg. There are liquidations across the board and LUNA pukes because of its redemption mechanism (the attacker very well could have shorted LUNA as well). BTC fell 25% from $42k on 4/11 to $31.3k

So how much did our attacker make? There aren't details on where they covered obviously, but if they are able to cover (or buy back) the entire position at ~$32k, that means they made $952mm on the short.

On the $350mm of $UST curve dumps I don't think they took much of a loss, lets assume 3% or just $11m. And lets assume that all the Binance dumps were done at 80c, thats another $125mm cost of doing business. For a grand total profit of $815mm (bf borrow cost).

BTC was the perfect playground for the trade, as the liquidity was there to pull it off. While having LFG involved in BTC, and foreseeing they would sell to keep the peg (and prevent LUNA from dying) was the kicker.

Lastly, the liquidity being low on 3pool in advance of 4pool allowed the attacker to drain it with only $350mm, causing the broader panic in both BTC and $UST. Any shorts on LUNA would've added a lot of P&L here as well, with it falling -65% since 5/7.

And for the reply guys, yes I know a lot of this involves some speculation & assumptions. But a lot of money was made here either way, and I thought it would be cool to dive into how they did it.

mbvissers.eth

3 years ago

Why does every smart contract seem to implement ERC165?

ERC165 (or EIP-165) is a standard utilized by various open-source smart contracts like Open Zeppelin or Aavegotchi.

What's it? You must implement? Why do we need it? I'll describe the standard and answer any queries.

What is ERC165

ERC165 detects and publishes smart contract interfaces. Meaning? It standardizes how interfaces are recognized, how to detect if they implement ERC165, and how a contract publishes the interfaces it implements. How does it work?

Why use ERC165? Sometimes it's useful to know which interfaces a contract implements, and which version.

Identifying interfaces

An interface function's selector. This verifies an ABI function. XORing all function selectors defines an interface in this standard. The following code demonstrates.

// SPDX-License-Identifier: UNLICENCED
pragma solidity >=0.8.0 <0.9.0;

interface Solidity101 {
    function hello() external pure;
    function world(int) external pure;
}

contract Selector {
    function calculateSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.hello.selector ^ i.world.selector;
        // Returns 0xc6be8b58
    }

    function getHelloSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.hello.selector;
        // Returns 0x19ff1d21
    }

    function getWorldSelector() public pure returns (bytes4) {
        Solidity101 i;
        return i.world.selector;
        // Returns 0xdf419679
    }
}

This code isn't necessary to understand function selectors and how an interface's selector can be determined from the functions it implements.

Run that sample in Remix to see how interface function modifications affect contract function output.

Contracts publish their implemented interfaces.

We can identify interfaces. Now we must disclose the interfaces we're implementing. First, import IERC165 like so.

pragma solidity ^0.4.20;

interface ERC165 {
    /// @notice Query if a contract implements an interface
    /// @param interfaceID The interface identifier, as specified in ERC-165
    /// @dev Interface identification is specified in ERC-165. 
    /// @return `true` if the contract implements `interfaceID` and
    ///  `interfaceID` is not 0xffffffff, `false` otherwise
    function supportsInterface(bytes4 interfaceID) external view returns (bool);
}

We still need to build this interface in our smart contract. ERC721 from OpenZeppelin is a good example.

// SPDX-License-Identifier: MIT
// OpenZeppelin Contracts (last updated v4.5.0) (token/ERC721/ERC721.sol)

pragma solidity ^0.8.0;

import "./IERC721.sol";
import "./extensions/IERC721Metadata.sol";
import "../../utils/introspection/ERC165.sol";
// ...

contract ERC721 is Context, ERC165, IERC721, IERC721Metadata {
  // ...

  function supportsInterface(bytes4 interfaceId) public view virtual override(ERC165, IERC165) returns (bool) {
    return
      interfaceId == type(IERC721).interfaceId ||
      interfaceId == type(IERC721Metadata).interfaceId ||
      super.supportsInterface(interfaceId);
  }
  
  // ...
}

I deleted unnecessary code. The smart contract imports ERC165, IERC721 and IERC721Metadata. The is keyword at smart contract declaration implements all three.

Kind (interface).

Note that type(interface).interfaceId returns the same as the interface selector.

We override supportsInterface in the smart contract to return a boolean that checks if interfaceId is the same as one of the implemented contracts.

Super.supportsInterface() calls ERC165 code. Checks if interfaceId is IERC165.

function supportsInterface(bytes4 interfaceId) public view virtual override returns (bool) {
    return interfaceId == type(IERC165).interfaceId;
}

So, if we run supportsInterface with an interfaceId, our contract function returns true if it's implemented and false otherwise. True for IERC721, IERC721Metadata, andIERC165.

Conclusion

I hope this post has helped you understand and use ERC165 and why it's employed.

Have a great day, thanks for reading!

Ashraful Islam

4 years ago

Clean API Call With React Hooks


Photo by Juanjo Jaramillo on Unsplash

Calling APIs is the most common thing to do in any modern web application. When it comes to talking with an API then most of the time we need to do a lot of repetitive things like getting data from an API call, handling the success or error case, and so on.

When calling tens of hundreds of API calls we always have to do those tedious tasks. We can handle those things efficiently by putting a higher level of abstraction over those barebone API calls, whereas in some small applications, sometimes we don’t even care.

The problem comes when we start adding new features on top of the existing features without handling the API calls in an efficient and reusable manner. In that case for all of those API calls related repetitions, we end up with a lot of repetitive code across the whole application.

In React, we have different approaches for calling an API. Nowadays mostly we use React hooks. With React hooks, it’s possible to handle API calls in a very clean and consistent way throughout the application in spite of whatever the application size is. So let’s see how we can make a clean and reusable API calling layer using React hooks for a simple web application.

I’m using a code sandbox for this blog which you can get here.

import "./styles.css";
import React, { useEffect, useState } from "react";
import axios from "axios";

export default function App() {
  const [posts, setPosts] = useState(null);
  const [error, setError] = useState("");
  const [loading, setLoading] = useState(false);

  useEffect(() => {
    handlePosts();
  }, []);

  const handlePosts = async () => {
    setLoading(true);
    try {
      const result = await axios.get(
        "https://jsonplaceholder.typicode.com/posts"
      );
      setPosts(result.data);
    } catch (err) {
      setError(err.message || "Unexpected Error!");
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="App">
      <div>
        <h1>Posts</h1>
        {loading && <p>Posts are loading!</p>}
        {error && <p>{error}</p>}
        <ul>
          {posts?.map((post) => (
            <li key={post.id}>{post.title}</li>
          ))}
        </ul>
      </div>
    </div>
  );
}

I know the example above isn’t the best code but at least it’s working and it’s valid code. I will try to improve that later. For now, we can just focus on the bare minimum things for calling an API.

Here, you can try to get posts data from JsonPlaceholer. Those are the most common steps we follow for calling an API like requesting data, handling loading, success, and error cases.

If we try to call another API from the same component then how that would gonna look? Let’s see.

500: Internal Server Error

Now it’s going insane! For calling two simple APIs we’ve done a lot of duplication. On a top-level view, the component is doing nothing but just making two GET requests and handling the success and error cases. For each request, it’s maintaining three states which will periodically increase later if we’ve more calls.

Let’s refactor to make the code more reusable with fewer repetitions.

Step 1: Create a Hook for the Redundant API Request Codes

Most of the repetitions we have done so far are about requesting data, handing the async things, handling errors, success, and loading states. How about encapsulating those things inside a hook?

The only unique things we are doing inside handleComments and handlePosts are calling different endpoints. The rest of the things are pretty much the same. So we can create a hook that will handle the redundant works for us and from outside we’ll let it know which API to call.

500: Internal Server Error

Here, this request function is identical to what we were doing on the handlePosts and handleComments. The only difference is, it’s calling an async function apiFunc which we will provide as a parameter with this hook. This apiFunc is the only independent thing among any of the API calls we need.

With hooks in action, let’s change our old codes in App component, like this:

500: Internal Server Error

How about the current code? Isn’t it beautiful without any repetitions and duplicate API call handling things?

Let’s continue our journey from the current code. We can make App component more elegant. Now it knows a lot of details about the underlying library for the API call. It shouldn’t know that. So, here’s the next step…

Step 2: One Component Should Take Just One Responsibility

Our App component knows too much about the API calling mechanism. Its responsibility should just request the data. How the data will be requested under the hood, it shouldn’t care about that.

We will extract the API client-related codes from the App component. Also, we will group all the API request-related codes based on the API resource. Now, this is our API client:

import axios from "axios";

const apiClient = axios.create({
  // Later read this URL from an environment variable
  baseURL: "https://jsonplaceholder.typicode.com"
});

export default apiClient;

All API calls for comments resource will be in the following file:

import client from "./client";

const getComments = () => client.get("/comments");

export default {
  getComments
};

All API calls for posts resource are placed in the following file:

import client from "./client";

const getPosts = () => client.get("/posts");

export default {
  getPosts
};

Finally, the App component looks like the following:

import "./styles.css";
import React, { useEffect } from "react";
import commentsApi from "./api/comments";
import postsApi from "./api/posts";
import useApi from "./hooks/useApi";

export default function App() {
  const getPostsApi = useApi(postsApi.getPosts);
  const getCommentsApi = useApi(commentsApi.getComments);

  useEffect(() => {
    getPostsApi.request();
    getCommentsApi.request();
  }, []);

  return (
    <div className="App">
      {/* Post List */}
      <div>
        <h1>Posts</h1>
        {getPostsApi.loading && <p>Posts are loading!</p>}
        {getPostsApi.error && <p>{getPostsApi.error}</p>}
        <ul>
          {getPostsApi.data?.map((post) => (
            <li key={post.id}>{post.title}</li>
          ))}
        </ul>
      </div>
      {/* Comment List */}
      <div>
        <h1>Comments</h1>
        {getCommentsApi.loading && <p>Comments are loading!</p>}
        {getCommentsApi.error && <p>{getCommentsApi.error}</p>}
        <ul>
          {getCommentsApi.data?.map((comment) => (
            <li key={comment.id}>{comment.name}</li>
          ))}
        </ul>
      </div>
    </div>
  );
}

Now it doesn’t know anything about how the APIs get called. Tomorrow if we want to change the API calling library from axios to fetch or anything else, our App component code will not get affected. We can just change the codes form client.js This is the beauty of abstraction.

Apart from the abstraction of API calls, Appcomponent isn’t right the place to show the list of the posts and comments. It’s a high-level component. It shouldn’t handle such low-level data interpolation things.

So we should move this data display-related things to another low-level component. Here I placed those directly in the App component just for the demonstration purpose and not to distract with component composition-related things.

Final Thoughts

The React library gives the flexibility for using any kind of third-party library based on the application’s needs. As it doesn’t have any predefined architecture so different teams/developers adopted different approaches to developing applications with React. There’s nothing good or bad. We choose the development practice based on our needs/choices. One thing that is there beyond any choices is writing clean and maintainable codes.

Shawn Mordecai

3 years ago

The Apple iPhone 14 Pill is Easier to Swallow

Is iPhone's Dynamic Island invention or a marketing ploy?

First of all, why the notch?

When Apple debuted the iPhone X with the notch, some were surprised, confused, and amused by the goof. Let the Brits keep the new meaning of top-notch.

Apple removed the bottom home button to enhance screen space. The tides couldn't overtake part of the top. This section contained sensors, a speaker, a microphone, and cameras for facial recognition. A town resisted Apple's new iPhone design.

iPhone X with a notch cutout housing cameras, sensors, speaker, and a microphone / Photo from Apple

From iPhone X to 13, the notch has gotten smaller. We expected this as technology and engineering progressed, but we hated the notch. Apple approved. They attached it to their other gadgets.

Apple accepted, owned, and ran with the iPhone notch, it has become iconic (or infamous); and that’s intentional.

The Island Where Apple Is

Apple needs to separate itself, but they know how to do it well. The iPhone 14 Pro finally has us oohing and aahing. Life-changing, not just higher pixel density or longer battery.

Dynamic Island turned a visual differentiation into great usefulness, which may not be life-changing. Apple always welcomes the controversy, whether it's $700 for iMac wheels, no charging block with a new phone, or removing the headphone jack.

Apple knows its customers will be loyal, even if they're irritated. Their odd design choices often cause controversy. It's calculated that people blog, review, and criticize Apple's products. We accept what works for them.

While the competition zigs, Apple zags. Sometimes they zag too hard and smash into a wall, but we talk about it anyways, and that’s great publicity for them.

Getting Dependent on the drug

The notch became a crop. Dynamic Island's design is helpful, intuitive, elegant, and useful. It increases iPhone usability, productivity (slightly), and joy. No longer unsightly.

The medication helps with multitasking. It's a compact version of the iPhone's Live Activities lock screen function. Dynamic Island enhances apps and activities with visual effects and animations whether you engage with it or not. As you use the pill, its usefulness lessens. It lowers user notifications and consolidates them with live and permanent feeds, delivering quick app statuses. It uses the black pixels on the iPhone 14's display, which looked like a poor haircut.

iPhone 14 Pro’s ‘Dynamic Island’ animations and effects / GIF from Tenor

The pill may be a gimmick to entice customers to use more Apple products and services. Apps may promote to their users like a live billboard.

Be prepared to get a huge dose of Dynamic Island’s “pill” like you never had before with the notch. It might become so satisfying and addicting to use, that every interaction with it will become habit-forming, and you’re going to forget that it ever existed.

WARNING: A Few Potential Side Effects

Vision blurred Dynamic Island's proximity to the front-facing camera may leave behind grease that blurs photos. Before taking a selfie, wipe the camera clean.

Strained thumb To fully use Dynamic Island, extend your thumb's reach 6.7 inches beyond your typical, comfortable range.

Happiness, contentment The Dynamic Island may enhance Endorphins and Dopamine. Multitasking, interactions, animations, and haptic feedback make you want to use this function again and again.

Motion-sickness Dynamic Island's motions and effects may make some people dizzy. If you can disable animations, you can avoid motion sickness.

I'm not a doctor, therefore they aren't established adverse effects.

Does Dynamic Island Include Multiple Tasks?

Dynamic Islands is a placebo for multitasking. Apple might have compromised on iPhone multitasking. It won't make you super productive, but it's a step up.

iPad’s Split View Multitasking / Photo from WinBuzzer

iPhone is primarily for personal use, like watching videos, messaging friends, sending money to friends, calling friends about the money you were supposed to send them, taking 50 photos of the same leaf, investing in crypto, driving for Uber because you lost all your money investing in crypto, listening to music and hailing an Uber from a deserted crop field because while you were driving for Uber your passenger stole your car and left you stranded, so you used Apple’s new SOS satellite feature to message your friend, who still didn’t receive their money, to hail you an Uber; now you owe them more money… karma?

We won't be watching videos on iPhones while perusing 10,000-row spreadsheets anytime soon. True multitasking and productivity aren't priorities for Apple's iPhone. Apple doesn't to preserve the iPhone's experience. Like why there's no iPad calculator. Apple doesn't want iPad users to do math, but isn't essential for productivity?

Digressing.

Apple will block certain functions so you must buy and use their gadgets and services, immersing yourself in their ecosystem and dictating how to use their goods.

Dynamic Island is a poor man’s multi-task for iPhone, and that’s fine it works for most iPhone users. For substantial productivity Apple prefers you to get an iPad or a MacBook. That’s part of the reason for restrictive features on certain Apple devices, but sometimes it’s based on principles to preserve the integrity of the product, according to Apple’s definition.

Is Apple using deception?

Dynamic Island may be distracting you from a design decision. The answer is kind of. Elegant distraction

When you pull down a smartphone webpage to refresh it or minimize an app, you get seamless animations. It's not simply because it appears better; it's due to iPhone and smartphone processing speeds. Such limits reduce the system's response to your activity, slowing the experience. Designers and developers use animations and effects to distract us from the time lag (most of the time) and sometimes because it looks cooler and smoother.

Dynamic Island makes apps more useable and interactive. It shows system states visually. Turn signal audio and visual cues, voice assistance, physical and digital haptic feedbacks, heads-up displays, fuel and battery level gauges, and gear shift indicators helped us overcome vehicle design problems.

Dynamic Island is a wonderfully delightful (and temporary) solution to a design “problem” until Apple or other companies can figure out a way to sink the cameras under the smartphone screen.

Tim Cook at an Apple Event in 2014 / Photo from The Verge

Apple Has Returned to Being an Innovative & Exciting Company

Now Apple's products are exciting. Next, bring back real Apple events, not pre-recorded demos.

Dynamic Island integrates hardware and software. What will this new tech do? How would this affect device use? Or is it just hype?

Dynamic Island may be an insignificant improvement to the iPhone, but it sure is promising for the future of bridging the human and computer interaction gap.

Sarah Bird

3 years ago

Memes Help This YouTube Channel Earn Over $12k Per Month

Take a look at a YouTube channel making anything up to over $12k a month from making very simple videos.

And the best part? Its replicable by anyone. Basic videos can be generated for free without design abilities.

Join me as I deconstruct the channel to estimate how much they make, how they do it, and how you can too.

What Do They Do Exactly?

Happy Land posts memes with a simple caption they wrote. So, it's new. The videos are a slideshow of meme photos with stock music.

The site posts 12 times a day.

8-10-minute videos show 10 second images. Thus, each video needs 48-60 memes.

Memes are video titles (e.g. times a boyfriend was hilarious, back to school fails, funny restaurant signs).

Some stats about the channel:

Founded on October 30, 2020
873 videos were added.
81.8k subscribers
67,244,196 views of the video

What Value Are They Adding?

Everyone can find free memes online. This channel collects similar memes into a single video so you don't have to scroll or click for more. It’s right there, you just keep watching and more will come.

By theming it, the audience is prepared for the video's content.

If you want hilarious animal memes or restaurant signs, choose the video and you'll get up to 60 memes without having to look for them. Genius!

How much money do they make?

According to www.socialblade.com, the channel earns $800-12.8k (image shown in my home currency of GBP).

That's a crazy estimate, but it highlights the unbelievable potential of a channel that presents memes.

This channel thrives on quantity, thus putting out videos is necessary to keep the flow continuing and capture its audience's attention.

How Are the Videos Made?

Straightforward. Memes are added to a presentation without editing (so you could make this in PowerPoint or Keynote).

Each slide should include a unique image and caption. Set 10 seconds per slide.

Add music and post the video.

Finding enough memes for the material and theming is difficult, but if you enjoy memes, this is a fun job.

This case study should have shown you that you don't need expensive software or design expertise to make entertaining videos. Why not try fresh, easy-to-do ideas and see where they lead?

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.

A quick guide to formatting your text on INTΞGRITY

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More on Web3 & Crypto

How to make a >800 million dollars in crypto attacking the once 3rd largest stablecoin, Soros style

Why does every smart contract seem to implement ERC165?

What is ERC165

Identifying interfaces

Contracts publish their implemented interfaces.

Conclusion

Clean API Call With React Hooks

Step 1: Create a Hook for the Redundant API Request Codes

Step 2: One Component Should Take Just One Responsibility

Final Thoughts

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In 2022, each data scientist needs to read these 11 books.

Best books for data scientists

1. The Black Swan

2. High Output Management

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

4. Obviously Awesome: How to Nail Product Positioning

5. The Mom test

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

7. Python Data Science Handbook: Essential Tools for Working with Data

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

9. Data Science from Scratch

10. Machine Learning Yearning

11. Deep Learning with PyTorch Step-by-Step

Is every data scientist a humanist?