One-hour ebook sales, affiliate relationships, and more.

If you follow me, you may know I started a new ebook in August 2022.

Despite publishing on this platform, my website, and Quora, I'm not a writer.

My writing speed is slow, 2,000 words a day, and I struggle to communicate cohesively.

In April 2022, I wrote a successful guide on How to Write Google-Friendly Blog Posts.

I had no email list or social media presence. I've made $1,600+ selling ebooks.

Evidence:

My first digital offering isn't a book.

It's an actionable guide with my tried-and-true process for writing Google-friendly content.

I'm not bragging.

Established authors like Tim Denning make more from my ebook sales with one newsletter.

This experience taught me writing isn't a privilege.

Writing a book and making money online doesn't require expertise.

Many don't consult experts. They want someone approachable.

Two years passed before I realized my own limits.

I have a brain, two hands, and Internet to spread my message.

I wrote and published a second ebook after the first's success.

On Gumroad, I released my second digital product.

Here's my complete Gumroad evaluation.

Gumroad is a marketplace for content providers to develop and sell sales pages.

Gumroad handles payments and client requests. It's helpful when someone sends a bogus payment receipt requesting an ebook (actual story!).

You'll forget administrative concerns after your first ebook sale.

After my first ebook sale, I did this: I made additional cash!

After every sale, I tell myself, "I built a new semi-passive revenue source."

This thinking shift helps me become less busy while increasing my income and quality of life.

Besides helping others, folks sell evergreen digital things to earn passive money.

It's in my second ebook.

I explain how I built and sold 50+ copies of my SEO writing ebook without being an influencer.

I show how anyone can sell ebooks on Gumroad and automate their sales process.

This is my ebook.

After publicizing the ebook release, I sold three copies within an hour.

Wow, or meh?

I don’t know.

The answer is different for everyone.

These three sales came from a small email list of 40 motivated fans waiting for my ebook release.

I had bigger plans.

I'll market my ebook on Medium, my website, Quora, and email.

I'm testing affiliate partnerships this time.

One of my ebook buyers is now promoting it for 40% commission.

Become my affiliate if you think your readers would like my ebook.

My ebook is a few days old, but I'm interested to see where it goes.

My SEO writing book started without an email list, affiliates, or 4,000 website visitors. I've made four figures.

I'm slowly expanding my communication avenues to have more impact.

Even a small project can open doors you never knew existed.

So began my writing career.

In summary

If you dare, every concept can become a profitable trip.

Before, I couldn't conceive of creating an ebook.

How to Sell eBooks on Gumroad is my second digital product.

Marketing and writing taught me that anything can be sold online.

Inspired by someone I never met

Inspiration is good and bad.

Paul Jarvis inspires me. He's a web person and writer who created his own category by being himself.

Paul said no thank you when everyone else was developing, building, and assuming greater responsibilities. This isn't success. He rewrote the rules. Working for himself, expanding at his own speed, and doing what he loves were his definitions of success.

Play with a problem that you have

The biggest problem can be not recognizing a problem.

Acceptance without question is deception. When you don't push limits, you forget how. You start thinking everything must be as it is.

For example: working. Paul worked a 9-5 agency work with little autonomy. He questioned whether the 9-5 was a way to live, not the way.

Another option existed. So he chipped away at how to live in this new environment.

Don't simply jump

Internet writers tell people considering quitting 9-5 to just quit. To throw in the towel. To do what you like.

The advice is harmful, despite the good intentions. People think quitting is hard. Like courage is the issue. Like handing your boss a resignation letter.

Nope. The tough part comes after. It’s easy to jump. Landing is difficult.

The landing

Paul didn't quit. Intelligent individuals don't. Smart folks focus on landing. They imagine life after 9-5.

Paul had been a web developer for a long time, had solid clients, and was respected. Hence if he pushed the limits and discovered another route, he had the potential to execute.

Working on the side

Society loves polarization. It’s left or right. Either way. Or chaos. It's 9-5 or entrepreneurship.

But like Paul, you can stretch polarization's limits. In-between exists.

You can work a 9-5 and side jobs (as I do). A mix of your favorites. The 9-5's stability and creativity. Fire and routine.

Remember you can't have everything but anything. You can create and work part-time.

My hybrid lifestyle

Not selling books doesn't destroy my world. My globe keeps spinning if my new business fails or if people don't like my Tweets. Unhappy algorithm? Cool. I'm not bothered (okay maybe a little).

The mix gives me the best of both worlds. To create, hone my skill, and grasp big-business basics. I like routine, but I also appreciate spending 4 hours on Saturdays writing.

Some days I adore leaving work at 5 pm and disconnecting. Other days, I adore having a place to write if inspiration strikes during a run or a discussion.

I’m a part-time creator

I’m a part-time creator. No, I'm not trying to quit. I don't work 5 pm - 2 am on the side. No, I'm not at $10,000 MRR.

I work part-time but enjoy my 9-5. My 9-5 has goodies. My side job as well.

It combines both to meet my lifestyle. I'm satisfied.

Join the Part-time Creators Club for free here. I’ll send you tips to enhance your creative game.

> Say Hello To The Lean-Hedge Model

People think starting a business requires significant debt and investment. Like Shark Tank, you need a world-changing idea. I'm not saying to avoid investors or brilliant ideas.

Investing is essential to build a genuinely profitable company. Think Apple or Starbucks.

Entrepreneurship is risky because many people go bankrupt from debt. As starters, we shouldn't do it. Instead, use lean-hedge.

Simply defined, you construct a cash-flow business to hedge against long-term investment-heavy business expenses.

What the “fx!$rench-toast” is the lean-hedge model?

When you start a business, your money should move down, down, down, then up when it becomes profitable.

Many people don't survive the business's initial losses and debt. What if, we created a cash-flow business BEFORE we started our Starbucks to hedge against its initial expenses?

Lean-hedge has two sections. Start a cash-flow business. A cash-flow business takes minimal investment and usually involves sweat and time.

Let’s take a look at some examples:

A Translation company

Personal portfolio website (you make a site then you do cold e-mail marketing)

FREELANCE (UpWork, Fiverr).

Educational business.

Infomarketing. (You design a knowledge-based product. You sell the info).

Online fitness/diet/health coaching ($50-$300/month, calls, training plan)

Amazon e-book publishing. (Medium writers do this)

YouTube, cash-flow channel

A web development agency (I'm a dev, but if you're not, a graphic design agency, etc.) (Sell your time.)

Digital Marketing

Online paralegal (A million lawyers work in the U.S).

Some dropshipping (Organic Tik Tok dropshipping, where you create content to drive traffic to your shopify store instead of spend money on ads).

(Disclaimer: My first two cash-flow enterprises, which were language teaching, failed terribly. My translation firm is now booming because B2B e-mail marketing is easy.)

Crossover occurs. Your long-term business starts earning more money than your cash flow business.

My cash-flow business (freelancing, translation) makes $7k+/month.

I’ve decided to start a slightly more investment-heavy digital marketing agency

Here are the anticipated business's time- and money-intensive investments:

1. ($$$) Top Front-End designer's Figma/UI-UX design (in negotiation)

2. (Time): A little copywriting (I will do this myself)

3. ($$) Creating an animated webpage with HTML (in negotiation)

4. Backend Development (Duration) (I'll carry out this myself using Laravel.)

5. Logo Design ($$)

6. Logo Intro Video for $

7. Video Intro (I’ll edit this myself with Premiere Pro)

etc.

Then evaluate product, place, price, and promotion. Consider promotion and pricing.

The lean-hedge model's point is:

Don't gamble. Avoid debt. First create a cash-flow project, then grow it steadily.

Check read my previous posts on “Nightmare Mode” (which teaches you how to make work as interesting as video games) and Why most people can't escape a 9-5 to learn how to develop a cash-flow business.

Every week I meet many people interested in VC. Many of them ask me what it's like to be a junior analyst in VC or what I've learned so far.

Looking back, I've learned many things as a junior VC, having gone through an almost-euphoric peak bull market, failed tech IPOs of 2019 including WeWorks' catastrophic fall, and the beginnings of a bearish market.

1. Network, network, network!

VCs spend 80% of their time networking. Junior VCs source deals or manage portfolios. You spend your time bringing startups to your fund or helping existing portfolio companies grow. Knowing stakeholders (corporations, star talent, investors) in your particular areas of investment helps you develop your portfolio.

Networking was one of my strengths. When I first started in the industry, I'd go to startup events and meet 50 people a month. Over time, I realized these relationships were shallow and I was only getting business cards. So I stopped seeing networking as a transaction. VC is a long-term game, so you should work with people you like. Now I know who I click with and can build deeper relationships with them. My network is smaller but more valuable than before.

2. The Most Important Metric Is Founder

People often ask how we pick investments. Why some companies can raise money and others can't is a mystery. The founder is the most important metric for VCs. When a company is young, the product, environment, and team all change, but the founder remains constant. VCs bet on the founder, not the company.

How do we decide which founders are best after 2-3 calls? When looking at a founder's profile, ask why this person can solve this problem. The founders' track record will tell. If the founder is a serial entrepreneur, you know he/she possesses the entrepreneur DNA and will likely succeed again. If it's his/her first startup, focus on industry knowledge to deliver the best solution.

3. A company's fate can be determined by macrotrends.

Macro trends are crucial. A company can have the perfect product, founder, and team, but if it's solving the wrong problem, it won't succeed. I've also seen average companies ride the wave to success. When you're on the right side of a trend, there's so much demand that more companies can get a piece of the pie.

In COVID-19, macro trends made or broke a company. Ed-tech and health-tech companies gained unicorn status and raised funding at inflated valuations due to sudden demand. With the easing of pandemic restrictions and the start of a bear market, many of these companies' valuations are in question.

4. Look for methods to ACTUALLY add value.

You only need to go on VC twitter (read: @vcstartterkit and @vcbrags) for 5 minutes or look at fin-meme accounts on Instagram to see how much VCs claim to add value but how little they actually do. VC is a long-term game, though. Long-term, founders won't work with you if you don't add value.

How can we add value when we're young and have no network? Leaning on my strengths helped me. Instead of viewing my age and limited experience as a disadvantage, I realized that I brought a unique perspective to the table.

As a VC, you invest in companies that will be big in 5-7 years, and millennials and Gen Z will have the most purchasing power. Because you can relate to that market, you can offer insights that most Partners at 40 can't. I added value by helping with hiring because I had direct access to university talent pools and by finding university students for product beta testing.

5. Develop your personal brand.

Generalists or specialists run most funds. This means that funds either invest across industries or have a specific mandate. Most funds are becoming specialists, I've noticed. Top-tier founders don't lack capital, so funds must find other ways to attract them. Why would a founder work with a generalist fund when a specialist can offer better industry connections and partnership opportunities?

Same for fund members. Founders want quality investors. Become a thought leader in your industry to meet founders. Create content and share your thoughts on industry-related social media. When I first started building my brand, I found it helpful to interview industry veterans to create better content than I could on my own. Over time, my content attracted quality founders so I didn't have to look for them.

These are my biggest VC lessons. This list isn't exhaustive, but it's my industry survival guide.

I'd spend hours composing articles only to find out they were useless. Twitter solved the problem.

Twitter is wrinkled, say critics.

Nope. Writing is different. It won't make sense until you write there.

Twitter is resurgent. People are reading again. 15-second TikToks overloaded our senses.

After nuking my 20,000-follower Twitter account and starting again, I wrote every day for 210 days.

I'll explain.

I came across the strange world of microblogging.

Traditional web writing is filler-heavy.

On Twitter, you must be brief. I played Wordle.

Twitter Threads are the most popular writing format. Like a blog post. It reminds me of the famous broetry posts on LinkedIn a few years ago.

Threads combine tweets into an article.

• Sharp, concise sentences

• No regard for grammar

• As important as the information is how the text looks.

Twitter Threads are like Michael Angelo's David monument. He chipped away at an enormous piece of marble until a man with a big willy appeared.

That's Twitter Threads.

I tried to remove unnecessary layers from several of my Wordpress blog posts. Then I realized something.

Tweeting from scratch is easier and more entertaining. It's quicker and makes you think more concisely.

Superpower: saying much with little words. My long-form writing has improved. My article sentences resemble tweets.

You never know what will happen.

Twitter's subcultures are odd. Best-performing tweets are strange.

Unusual trend: working alone and without telling anyone. It's a rebellion against Instagram influencers who share their every moment.

Early on, random thoughts worked:

My friend’s wife is Ukrainian. Her family are trapped in the warzone. He is devastated. And here I was complaining about my broken garage door. War puts everything in perspective. Today is a day to be grateful for peace.

Documenting what's happening triggers writing. It's not about viral tweets. Helping others matters.

There are numerous anonymous users.

Twitter uses pseudonyms.

You don't matter. On sites like LinkedIn, you must use your real name. Welcome to the Cyberpunk metaverse of Twitter :)

One daily piece of writing is a powerful habit.

Habits build creator careers. Read that again.

Twitter is an easy habit to pick up. If you can't tweet in one sentence, something's wrong. Easy-peasy-japanese.

Not what I tweeted, but my constancy, made the difference.

Daily writing is challenging, especially if your supervisor is on your back. Twitter encourages writing.

Tweets evolved as the foundation of all other material.

During my experiment, I enjoyed Twitter's speed.

Tweets get immediate responses, comments, and feedback. My popular tweets become newspaper headlines. I've also written essays from tweet discussions.

Sometimes the tweet and article were clear. Twitter sometimes helped me overcome writer's block.

I used to spend hours composing big things that had little real-world use.

Twitter helped me. No guessing. Data guides my coverage and validates concepts.

Test ideas on Twitter.

It took some time for my email list to grow.

Subscribers are a writer's lifeblood.

Without them, you're broke and homeless when Mark Zuckerberg tweaks the algorithms for ad dollars. Twitter has three ways to obtain email subscribers:

1. Add a link to your bio.

Twitter allows bio links (LinkedIn now does too). My eBook's landing page is linked. I collect emails there.

2. Start an online newsletter.

Twitter bought newsletter app Revue. They promote what they own.

I just established up a Revue email newsletter. I imported them weekly into my ConvertKit email list.

3. Create Twitter threads and include a link to your email list in the final tweet.

Write Twitter Threads and link the last tweet to your email list (example below).

Initial email subscribers were modest.

Numbers are growing. Twitter provides 25% of my new email subscribers. Some days, 50 people join.

Without them, my writing career is over. I'd be back at a 9-5 job begging for time off to spend with my newborn daughter. Nope.

Collect email addresses or die trying.

As insurance against unsubscribes and Zucks, use a second email list or Discord community.

What I still need to do

Twitter's fun. I'm wiser. I need to enable auto-replies and auto-DMs (direct messages).

This adds another way to attract subscribers. I schedule tweets with Tweet Hunter.

It’s best to go slow. People assume you're an internet marketer if you spam them with click requests.

A human internet marketer is preferable to a robot. My opinion.

210 days on Twitter taught me that. I plan to use the platform until I'm a grandfather unless Elon ruins it.

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.