A top-down company plan facilitates decision-making and impresses investors.

A startup business plan starts with the product, the target customers, how to reach them, and how to grow the business.

Bottom-up is terrific unless venture investors fund it.

If it can prove how it can exceed $100M in sales, investors will invest. If not, the business may be wonderful, but it's not venture capital-investable.

As a rule, venture investors only fund firms that expect to reach $100M within 5 years.

Investors get nothing until an acquisition or IPO. To make up for 90% of failed investments and still generate 20% annual returns, portfolio successes must exit with a 25x return. A $20M-valued company must be acquired for $500M or more.

This requires $100M in sales (or being on a nearly vertical trajectory to get there). The company has 5 years to attain that milestone and create the requisite ROI.

This motivates venture investors (venture funds and angel investors) to hunt for $100M firms within 5 years. When you pitch investors, you outline how you'll achieve that aim.

I'm wary of pitches after seeing a million hockey sticks predicting $5M to $100M in year 5 that never materialized. Doubtful.

Startups fail because they don't have enough clients, not because they don't produce a great product. That jump from $5M to $100M never happens. The company reaches $5M or $10M, growing at 10% or 20% per year.  That's great, but not enough for a $500 million deal.

Once it becomes clear the company won’t reach orbit, investors write it off as a loss. When a corporation runs out of money, it's shut down or sold in a fire sale. The company can survive if expenses are trimmed to match revenues, but investors lose everything.

When I hear a pitch, I'm not looking for bright income projections but a viable plan to achieve them. Answer these questions in your pitch.

• Is the market size sufficient to generate $100 million in revenue?

• Will the initial beachhead market serve as a springboard to the larger market or as quicksand that hinders progress?

• What marketing plan will bring in $100 million in revenue? Is the market diffuse and will cost millions of dollars in advertising, or is it one, focused market that can be tackled with a team of salespeople?

• Will the business be able to bridge the gap from a small but fervent set of early adopters to a larger user base and avoid lock-in with their current solution?

• Will the team be able to manage a $100 million company with hundreds of people, or will hypergrowth force the organization to collapse into chaos?

• Once the company starts stealing market share from the industry giants, how will it deter copycats?

The requirement to reach $100M may be onerous, but it provides a context for difficult decisions: What should the product be? Where should we concentrate? who should we hire? Every strategic choice must consider how to reach $100M in 5 years.

Focusing on $100M streamlines investor pitches. Instead of explaining everything, focus on how you'll attain $100M.

As an investor, I know I'll lose my money if the startup doesn't reach this milestone, so the revenue prediction is the first thing I look at in a pitch deck.

Reaching the $100M goal needs to be the first thing the entrepreneur thinks about when putting together the business plan, the central story of the pitch, and the criteria for every important decision the company makes.

University entrepreneurship is like a Willy Wonka Factory of ideas. Classes, roommates, discussions, and the cafeteria all inspire new ideas. I've seen people establish a business without knowing its roots.

Chicken or egg? On my mind: I've asked university founders around the world whether the problem or solution came first.

The Problem

One African team I met started with the “instant noodles” problem in their academic ecosystem. Many of us have had money issues in college, which may have led to poor nutritional choices.

Many university students in a war-torn country ate quick noodles or pasta for dinner.

Noodles required heat, water, and preparation in the boarding house. Unreliable power from one hot plate per blue moon. What's healthier, easier, and tastier than sodium-filled instant pots?

BOOM. They were fixing that. East African kids need affordable, nutritious food.

This is a real difficulty the founders faced every day with hundreds of comrades.

This sparked their serendipitous entrepreneurial journey and became their business's cornerstone.

The Solution

I asked a UK team about their company idea. They said the solution fascinated them.

The crew was fiddling with social media algorithms. Why are some people more popular? They were studying platforms and social networks, which offered a way for them.

Solving a problem? Yes. Long nights of university research lead them to it. Is this like world hunger? Social media influencers confront this difficulty regularly.

It made me ponder something. Is there a correct response?

In my heart, yes, but in my head…maybe?

I believe you should lead with empathy and embrace the problem, not the solution. Big or small, businesses should solve problems. This should be your focus. This is especially true when building a social company with an audience in mind.

Philosophically, invention and innovation are occasionally accidental. Also not penalized. Think about bugs and the creation of Velcro, or the inception of Teflon. They tackle difficulties we overlook. The route to the problem may look different, but there is a path there.

There's no golden ticket to the Chicken-Egg debate, but I'll keep looking this summer.

Or the “lie to me” method.

I can predict a startup's success in minutes.

Just interview its founder.

Ask "why?"

I question "why" till I sense him.

I need to feel the person I have in front of me. I need to know if he or she can deliver. Startups aren't easy. Without abilities, a brilliant idea will fail.

Good entrepreneurs have these qualities: He's a leader, determined, and resilient.

For me, they can be split in two categories.

The first entrepreneur aspires to live meaningfully. The second wants to get rich. The second is communicative. He wants to wow the crowd. He's motivated by the thought of one day sailing a boat past palm trees and sunny beaches.

What drives the first entrepreneur is evident in his speech, face, and voice. He will not speak about his product. He's (nearly) uninterested. He's not selling anything. He's not a salesman. He wants to succeed. The product is his fuel.

He'll explain his decision. He'll share his motivations. His desire. And he'll use meaningful words.

Paul Ekman has shown that face expressions aren't cultural. His study influenced the American TV series "lie to me" about body language and speech.

Passionate entrepreneurs are obvious. It's palpable. Faking passion is tough. Someone who wants your favor and money will expose his actual motives through his expressions and language.

The good liar will be able to fool you for a while, but not for long if you pay attention to his body language and how he expresses himself.

And also, if you look at his business plan.

His business plan reveals his goals. Read between the lines.

Entrepreneur 1 will focus on his "why", whereas Entrepreneur 2 will focus on the "how".

Entrepreneur 1 will develop a vision-driven culture.

The second, on the other hand, will focus on his EBITDA.

Why is the culture so critical? Because it will allow entrepreneur 1 to develop a solid team that can tackle his problems and trials. His team's "why" will keep them together in tough times.

"Give me a terrific start-up team with a mediocre idea over a weak one any day." Because a great team knows when to pivot and trusts each other. Weak teams fail.” — Bernhard Schroeder

Closings thoughts

Every VC must ask Why. Entrepreneur's motivations. This "why" will create the team's culture. This culture will help the team adjust to any setback.

Wishing for Lasting Companies, Not Penny Stocks or Goodwill Write-Downs

Get me a NASCAR-style company-logoed cremation urn (notice to the executor of my will, theres gonna be a lot of weird requests). I believe in working on projects that would be on your tombstone. As the Homebrew logo is tattooed on my shoulder, expanding the portfolio to my posthumous commemoration is easy. But this isn't an IRR victory lap; it's a hope that the firms we worked for would last beyond my lifetime.

Venture investors too often take credit or distance themselves from startups based on circumstances. Successful companies tell stories of crucial introductions, strategy conversations, and other value. Defeats Even whether our term involves Board service or systematic ethical violations, I'm just a little investment, so there's not much I can do. Since I'm guilty, I'm tossing stones from within the glass home (although we try to own our decisions through the lifecycle).

Post-exit company trajectories are usually unconfounded. Off the cap table, no longer a shareholder (or a diminishing one as you sell off/distribute), eventually leaving the Board. You can cheer for the squad or forget about it, but you've freed the corporation and it's back to portfolio work.

As I look at the downward track of most SPACs and other tarnished IPOs from the last few years, I wonder how I would feel if those were my legacy. Is my job done? Yes. When investing in a business, the odds are against it surviving, let alone thriving and being able to find sunlight. SPAC sponsors, institutional buyers, retail investments. Free trade in an open market is their right. Risking and losing capital is the system working! But

We were lead or co-lead investors in our first three funds, but as additional VCs joined the company, we were pushed down the cap table. Voting your shares rarely matters; supporting the firm when they need it does. Being valuable, consistent, and helping the company improve builds trust with the founders.

I hope every startup we sponsor becomes a successful public company before, during, and after we benefit. My perspective of American capitalism. Well, a stock ticker has a lot of garbage, and I support all types of regulation simplification (in addition to being a person investor in the Long-Term Stock Exchange). Yet being owned by a large group of investors and making actual gains for them is great. Likewise does seeing someone you met when they were just starting out become a public company CEO without losing their voice, leadership, or beliefs.

I'm just thinking about what we can do from the start to realize value from our investments and build companies with bright futures. Maybe seed venture financing shouldn't impact those outcomes, but I'm not comfortable giving up that obligation.

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.

It's cheaper, faster charging, longer lasting, safer, and better for the environment.

Batteries are the future. Next-gen and planet-saving technology, including solar power and EVs, require batteries. As these smart technologies become more popular, we find that our batteries can't keep up. Lithium-ion batteries are expensive, slow to charge, big, fast to decay, flammable, and not environmentally friendly. MIT just created a new battery that eliminates all of these problems.  So, is this the battery of the future? Or is there a catch?

When I say entirely new, I mean it. This battery employs no currently available materials. Its electrodes are constructed of aluminium and pure sulfur instead of lithium-complicated ion's metals and graphite. Its electrolyte is formed of molten chloro-aluminate salts, not an organic solution with lithium salts like lithium-ion batteries.

How does this change in materials help?

Aluminum, sulfur, and chloro-aluminate salts are abundant, easy to acquire, and cheap. This battery might be six times cheaper than a lithium-ion battery and use less hazardous mining. The world and our wallets will benefit.

But don’t go thinking this means it lacks performance.

This battery charged in under a minute in tests. At 25 degrees Celsius, the battery will charge 25 times slower than at 110 degrees Celsius. This is because the salt, which has a very low melting point, is in an ideal state at 110 degrees and can carry a charge incredibly quickly. Unlike lithium-ion, this battery self-heats when charging and discharging, therefore no external heating is needed.

Anyone who's seen a lithium-ion battery burst might be surprised. Unlike lithium-ion batteries, none of the components in this new battery can catch fire. Thus, high-temperature charging and discharging speeds pose no concern.

These batteries are long-lasting. Lithium-ion batteries don't last long, as any iPhone owner can attest. During charging, metal forms a dendrite on the electrode. This metal spike will keep growing until it reaches the other end of the battery, short-circuiting it. This is why phone batteries only last a few years and why electric car range decreases over time. This new battery's molten salt slows deposition, extending its life. This helps the environment and our wallets.

These batteries are also energy dense. Some lithium-ion batteries have 270 Wh/kg energy density (volume and mass). Aluminum-sulfur batteries could have 1392 Wh/kg, according to calculations. They'd be 5x more energy dense. Tesla's Model 3 battery would weigh 96 kg instead of 480 kg if this battery were used. This would improve the car's efficiency and handling.

These calculations were for batteries without molten salt electrolyte. Because they don't reflect the exact battery chemistry, they aren't a surefire prediction.

This battery seems great. It will take years, maybe decades, before it reaches the market and makes a difference. Right?

Nope. The project's scientists founded Avanti to develop and market this technology.

So we'll soon be driving cheap, durable, eco-friendly, lightweight, and ultra-safe EVs? Nope.

This battery must be kept hot to keep the salt molten; otherwise, it won't work and will expand and contract, causing damage. This issue could be solved by packs that can rapidly pre-heat, but that project is far off.

Rapid and constant charge-discharge cycles make these batteries ideal for solar farms, homes, and EV charging stations. The battery is constantly being charged or discharged, allowing it to self-heat and maintain an ideal temperature.

These batteries aren't as sexy as those making EVs faster, more efficient, and cheaper. Grid batteries are crucial to our net-zero transition because they allow us to use more low-carbon energy. As we move away from fossil fuels, we'll need millions of these batteries, so the fact that they're cheap, safe, long-lasting, and environmentally friendly will be huge. Who knows, maybe EVs will use this technology one day. MIT has created another world-changing technology.