Kim Kardashian created Skky Partners. Consumer products, internet & e-commerce, consumer media, hospitality, and luxury are company targets.

Some call this another Kardashian publicity gimmick.

This maneuver is brilliance upon closer inspection. Why?

1) Kim has amassed a sizable social media fan base:

Over 320 million Instagram and 70 million Twitter users follow Kim Kardashian.

Kim Kardashian's Instagram account ranks 8th. Three Kardashians in top 10 is ridiculous.

This gives her access to consumer data. She knows what people are discussing. Investment firms need this data.

Quality, not quantity, of her followers matters. Studies suggest that her following are more engaged than Selena Gomez and Beyonce's.

Kim's followers are worth roughly $500 million to her brand, according to a research. They trust her and buy what she recommends.

2) She has a special aptitude for identifying trends.

Kim Kardashian can sense trends.

She's always ahead of fashion and beauty trends. She's always trying new things, too. She doesn't mind making mistakes when trying anything new. Her desire to experiment makes her a good business prospector.

Kim has also created a lifestyle brand that followers love. Kim is an entrepreneur, mom, and role model, not just a reality TV star or model. She's established a brand around her appearance, so people want to buy her things.

Her fragrance collection has sold over $100 million since its 2009 introduction, and her Sears apparel line did over $200 million in its first year.

SKIMS is her latest $3.2bn brand. She can establish multibillion-dollar firms with her enormous distribution platform.

Early founders would kill for Kim Kardashian's network.

Making great products is hard, but distribution is more difficult. — David Sacks, All-in-Podcast

3) She can delegate the financial choices to Jay Sammons, one of the greatest in the industry.

Jay Sammons is well-suited to develop Kim Kardashian's new private equity fund.

Sammons has 16 years of consumer investing experience at Carlyle. This will help Kardashian invest in consumer-facing enterprises.

Sammons has invested in Supreme and Beats Electronics, both of which have grown significantly. Sammons' track record and competence make him the obvious choice.

Kim Kardashian and Jay Sammons have joined forces to create a new business endeavor. The agreement will increase Kardashian's commercial empire. Sammons can leverage one of the world's most famous celebrities.

“Together we hope to leverage our complementary expertise to build the next generation consumer and media private equity firm” — Kim Kardashian

Kim Kardashian is a successful businesswoman. She developed an empire by leveraging social media to connect with fans. By developing a global lifestyle brand, she has sold things and experiences that have made her one of the world's richest celebrities.

She's a shrewd entrepreneur who knows how to maximize on herself and her image.

Imagine how much interest Kim K will bring to private equity and venture capital.

I'm curious about the company's growth.

You have to use it.

Chat-GPT is a revolution.

All social media outlets are discussing it. How it will impact the future and different things.

True.

I've been using Chat-GPT for a few days, and it's a rare revolution. It's amazing and will only improve.

I asked Chat-GPT about the best non-fiction books. It advised this, albeit results rely on interests.

The Immortal Life of Henrietta Lacks

by Rebecca Skloot

Science, Biography

A impoverished tobacco farmer dies of cervical cancer in The Immortal Life of Henrietta Lacks. Her cell strand helped scientists treat polio and other ailments.

Rebecca Skloot discovers about Henrietta, her family, how the medical business exploited black Americans, and how her cells can live forever in a fascinating and surprising research.

You ought to read it.

1. if you want to discover more about the past of medicine.

2. if you want to discover more about American history.

Bad Blood: Secrets and Lies in a Silicon Valley Startup

by John Carreyrou

Tech, Bio

Bad Blood tells the terrifying story of how a Silicon Valley tech startup's blood-testing device placed millions of lives at risk.

John Carreyrou, a Pulitzer Prize-winning journalist, wrote this book.

Theranos and its wunderkind CEO, Elizabeth Holmes, climbed to popularity swiftly and then plummeted.

You ought to read it.

1. if you are a start-up employee.

2. specialists in medicine.

The Power of Now: A Guide to Spiritual Enlightenment

by Eckhart Tolle

Self-improvement, Spirituality

The Power of Now shows how to stop suffering and attain inner peace by focusing on the now and ignoring your mind.

The book also helps you get rid of your ego, which tries to control your ideas and actions.

If you do this, you may embrace the present, reduce discomfort, strengthen relationships, and live a better life.

You ought to read it.

1. if you're looking for serenity and illumination.

2. If you believe that you are ruining your life, stop.

3. if you're not happy.

The 7 Habits of Highly Effective People

by Stephen R. Covey

Profession, Success

The 7 Habits of Highly Effective People is an iconic self-help book.

This vital book offers practical guidance for personal and professional success.

This non-fiction book is one of the most popular ever.

You ought to read it.

1. if you want to reach your full potential.

2. if you want to discover how to achieve all your objectives.

3. if you are just beginning your journey toward personal improvement.

Sapiens: A Brief History of Humankind

by Yuval Noah Harari

Science, History

Sapiens explains how our species has evolved from our earliest ancestors to the technology age.

How did we, a species of hairless apes without tails, come to control the whole planet?

It describes the shifts that propelled Homo sapiens to the top.

You ought to read it.

1. if you're interested in discovering our species' past.

2. if you want to discover more about the origins of human society and culture.

His story will inspire you.

“Google search went down for a few hours in 2002; Jeff Dean handled all the queries by hand and checked quality doubled.”- Jeff Dean Facts.

One of many Jeff Dean jokes, but you get the idea.

Google's top six engineers met in a war room in mid-2000. Google's crawling system, which indexed the Web, stopped working. Users could still enter queries, but results were five months old.

Google just signed a deal with Yahoo to power a ten-times-larger search engine. Tension rose. It was crucial. If they failed, the Yahoo agreement would likely fall through, risking bankruptcy for the firm. Their efforts could be lost.

A rangy, tall, energetic thirty-one-year-old man named Jeff dean was among those six brilliant engineers in the makeshift room. He had just left D. E. C. a couple of months ago and started his career in a relatively new firm Google, which was about to change the world. He rolled his chair over his colleague Sanjay and sat right next to him, cajoling his code like a movie director. The history started from there.

When you think of people who shaped the World Wide Web, you probably picture founders and CEOs like Larry Page and Sergey Brin, Marc Andreesen, Tim Berners-Lee, Bill Gates, and Mark Zuckerberg. They’re undoubtedly the brightest people on earth.

Under these giants, legions of anonymous coders work at keyboards to create the systems and products we use. These computer workers are irreplaceable.

Let's get to know him better.

It's possible you've never heard of Jeff Dean. He's American. Dean created many behind-the-scenes Google products. Jeff, co-founder and head of Google's deep learning research engineering team, is a popular technology, innovation, and AI keynote speaker.

While earning an MS and Ph.D. in computer science at the University of Washington, he was a teaching assistant, instructor, and research assistant. Dean joined the Compaq Computer Corporation Western Research Laboratory research team after graduating.

Jeff co-created ProfileMe and the Continuous Profiling Infrastructure for Digital at Compaq. He co-designed and implemented Swift, one of the fastest Java implementations. He was a senior technical staff member at mySimon Inc., retrieving and caching electronic commerce content.

Dean, a top young computer scientist, joined Google in mid-1999. He was always trying to maximize a computer's potential as a child.

An expert

His high school program for processing massive epidemiological data was 26 times faster than professionals'. Epi Info, in 13 languages, is used by the CDC. He worked on compilers as a computer science Ph.D. These apps make source code computer-readable.

Dean never wanted to work on compilers forever. He left Academia for Google, which had less than 20 employees. Dean helped found Google News and AdSense, which transformed the internet economy. He then addressed Google's biggest issue, scaling.

Growing Google faced a huge computing challenge. They developed PageRank in the late 1990s to return the most relevant search results. Google's popularity slowed machine deployment.

Dean solved problems, his specialty. He and fellow great programmer Sanjay Ghemawat created the Google File System, which distributed large data over thousands of cheap machines.

These two also created MapReduce, which let programmers handle massive data quantities on parallel machines. They could also add calculations to the search algorithm. A 2004 research article explained MapReduce, which became an industry sensation.

Several revolutionary inventions

Dean's other initiatives were also game-changers. BigTable, a petabyte-capable distributed data storage system, was based on Google File. The first global database, Spanner, stores data on millions of servers in dozens of data centers worldwide.

It underpins Gmail and AdWords. Google Translate co-founder Jeff Dean is surprising. He contributes heavily to Google News. Dean is Senior Fellow of Google Research and Health and leads Google AI.

Recognitions

The National Academy of Engineering elected Dean in 2009. He received the 2009 Association for Computing Machinery fellowship and the 2016 American Academy of Arts and Science fellowship. He received the 2007 ACM-SIGOPS Mark Weiser Award and the 2012 ACM-Infosys Foundation Award. Lists could continue.

A sneaky question may arrive in your mind: How much does this big brain earn? Well, most believe he is one of the highest-paid employees at Google. According to a survey, he is paid $3 million a year.

He makes espresso and chats with a small group of Googlers most mornings. Dean steams milk, another grinds, and another brews espresso. They discuss families and technology while making coffee. He thinks this little collaboration and idea-sharing keeps Google going.

“Some of us have been working together for more than 15 years,” Dean said. “We estimate that we’ve collectively made more than 20,000 cappuccinos together.”

We all know great developers and software engineers. It may inspire many.

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.

Affiliate marketing is a popular online moneymaker. You promote others' products and get commissions. Affiliate marketing requires constant product promotion.

Affiliate marketing can be profitable even without much promotion. Yes, this is Autopilot Money.

How to Pick an Affiliate Program to Generate Income Autonomously

Autopilot moneymaking requires a recurring affiliate marketing program.

Finding the best product and testing it takes a lot of time and effort.

Here are three ways to choose the best service or product to promote:

Find a good attachment-rate product or service.

When choosing a product, ask if you can easily switch to another service. Attachment rate is how much people like a product.

Higher attachment rates mean better Autopilot products.

Consider promoting GetResponse. It's a 33% recurring commission email marketing tool. This means you get 33% of the customer's plan as long as he pays.

GetResponse has a high attachment rate because it's hard to leave and start over with another tool.

2. Pick a good or service with a lot of affiliate assets.

Check if a program has affiliate assets or creatives before joining.

Images and banners to promote the product in your business.

They save time; I look for promotional creatives. Creatives or affiliate assets are website banners or images. This reduces design time.

3. Select a service or item that consumers already adore.

New products are hard to sell. Choosing a trusted company's popular product or service is helpful.

As a beginner, let people buy a product they already love.

Online entrepreneurs and digital marketers love Systeme.io. It offers tools for creating pages, email marketing, funnels, and more. This product guarantees a high ROI.

Make the product known!

Affiliate marketers struggle to get traffic. Using affiliate marketing to make money is easier than you think if you have a solid marketing strategy.

Your plan should include:

1- Publish affiliate-related blog posts and SEO-optimize them

2- Sending new visitors product-related emails

3- Create a product resource page.

4-Review products

5-Make YouTube videos with links in the description.

6- Answering FAQs about your products and services on your blog and Quora.

7- Create an eCourse on how to use this product.

8- Adding Affiliate Banners to Your Website.

With these tips, you can promote your products and make money on autopilot.

Weekend project: limitless design service. It was fun to see whether I could start a business quickly.

I use no-code apps to save time and resources.

TL;DR I started a business utilizing EditorX for my website, Notion for client project management, and a few favors to finish my portfolio.

First step: research (Day 1)

I got this concept from a Kimp Instagram ad. The Minimalist Hustler Daily newsletter mentioned a similar and cheaper service (Graphically).

I Googled other unlimited design companies. Many provide different costs and services. Some supplied solely graphic design, web development, or copywriting.

Step 2: Brainstorming (Day 1)

I did something simple.

• What benefits and services to provide

• Price to charge

Since it's a one-person performance (for now), I'm focusing on graphic design. I can charge less.

So I don't overwhelm myself and can accommodate budget-conscious clientele.

Step 3: Construction (Day 1 & 2)

This project includes a management tool, a website, and a team procedure.

I built a project management tool and flow first. Once I had the flow and a Notion board, I tested it with design volunteers. They fake-designed while I built the website.

Tool for Project Management

I modified a Notion template. My goal is to keep clients and designers happy.

Team Approach

My sister, my partner, and I kept this business lean. I tweaked the Notion board to make the process smooth. By the end of Sunday, I’d say it’s perfect!

Website

I created the website after they finished the fake design demands. EditorX's drag-and-drop builder attracted me. I didn't need to learn code, and there are templates.

I used a template wireframe.

This project's hardest aspect is developing the site. It's my first time using EditorX and I'm no developer.

People answer all your inquiries in a large community forum.

As a first-time user developing a site in two days, I think I performed OK. Here's the site for feedback.

4th step: testing (Day 2)

Testing is frustrating because it works or doesn't. My testing day was split in two.

• testing the workflow from payment to onboarding to the website

• the demand being tested

It's working so far. If someone gets the trial, they can request design work.

I've gotten a couple of inquiries about demand. I’ll be working with them as a start.

Completion

Finally! I built my side project in one weekend. It's too early to tell if this is successful. I liked that I didn't squander months of resources testing out an idea.