Niches are rich, as the adage goes.

Micro SaaS requires a great micro-niche; otherwise, it's merely plain old SaaS with a large audience.

Instead of targeting broad markets with few identifying qualities, specialise down to a micro-niche. How would you target these users?

Better go tiny. You'll locate and engage new consumers more readily and serve them better with a customized solution.

Imagine you're a real estate lawyer looking for a case management solution. Because it's so specific to you, you'd be lured to this link:

instead of below:

Next, locate mini SaaS niches that could work for you. You're not yet looking at the problems/solutions in these areas, merely shortlisting them.

The market should be growing, not shrinking

We shouldn't design apps for a declining niche. We intend to target stable or growing niches for the next 5 to 10 years.

If it's a developing market, you may be able to claim a stake early. You must balance this strategy with safer, longer-established niches (accountancy, law, health, etc).

First Micro SaaS apps I designed were for Merch By Amazon creators, a burgeoning niche. I found this niche when searching for passive income.

Graphic designers and entrepreneurs post their art to Amazon to sell on clothes. When Amazon sells their design, they get a royalty. Since 2015, this platform and specialty have grown dramatically.

Amazon doesn't publicize the amount of creators on the platform, but it's possible to approximate by looking at Facebook groups, Reddit channels, etc.

I could see the community growing week by week, with new members joining. Merch was an up-and-coming niche, and designers made money when their designs sold. All I had to do was create tools that let designers focus on making bestselling designs.

Look at the Google Trends graph below to see how this niche has evolved and when I released my apps and resigned my job.

Are the users able to afford the tools?

Who's your average user? Consumer or business? Is your solution budgeted?

If they're students, you'll struggle to convince them to subscribe to your study-system app (ahead of video games and beer).

Let's imagine you designed a Shopify plugin that emails customers when a product is restocked. If your plugin just needs 5 product sales a month to justify its cost, everyone wins (just be mindful that one day Shopify could potentially re-create your plugins functionality within its core offering making your app redundant ).

Do specialized users buy tools? If so, that's comforting. If not, you'd better have a compelling value proposition for your end customer if you're the first.

This should include how much time or money your program can save or make the user.

Are you able to understand the Micro SaaS market?

Ideally, you're already familiar about the industry/niche. Maybe you're fixing a challenge from your day job or freelance work.

If not, evaluate how long it would take to learn the niche's users. Health & Fitness is easier to relate to and understand than hedge fund derivatives trading.

Competing in these complex (and profitable) fields might offer you an edge.

B2C, B2M, or B2B?

Consider your user base's demographics. Will you target businesses, consumers, or both? Let's examine the different consumer types:

• B2B refers to business-to-business transactions where customers are other businesses. UpVoty, Plutio, Slingshot, Salesforce, Atlassian, and Hubspot are a few examples of SaaS, ranging from Micro SaaS to SaaS.

• Business to Consumer (B2C), in which your clients are people who buy things. For instance, Duolingo, Canva, and Nomad List.

• For instance, my tool KDP Wizard has a mixed user base of publishing enterprises and also entrepreneurial consumers selling low-content books on Amazon. This is a case of business to many (B2M), where your users are a mixture of businesses and consumers. There is a large SaaS called Dropbox that offers both personal and business plans.

Targeting a B2B vs. B2C niche is very different. The sales cycle differs.

• A B2B sales staff must make cold calls to potential clients' companies. Long sales, legal, and contractual conversations are typically required for each business to get the go-ahead. The cost of obtaining a new customer is substantially more than it is for B2C, despite the fact that the recurring fees are significantly higher.

• Since there is typically only one individual making the purchasing decision, B2C signups are virtually always self-service with reduced recurring fees. Since there is typically no outbound sales staff in B2C, acquisition costs are significantly lower than in B2B.

User Characteristics for B2B vs. B2C

Consider where your niche's users congregate if you don't already have a presence there.

B2B users frequent LinkedIn and Twitter. B2C users are on Facebook/Instagram/Reddit/Twitter, etc.

Churn is higher in B2C because consumers haven't gone through all the hoops of a B2B sale. Consumers are more unpredictable than businesses since they let their bank cards exceed limitations or don't update them when they expire.

With a B2B solution, there's a contractual arrangement and the firm will pay the subscription as long as they need it.

Depending on how you feel about the above (sales team vs. income vs. churn vs. targeting), you'll know which niches to pursue.

You ought to respect potential customers.

Would you hang out with customers?

You'll connect with users at conferences (in-person or virtual), webinars, seminars, screenshares, Facebook groups, emails, support calls, support tickets, etc.

If talking to a niche's user base makes you shudder, you're in for a tough road. Whether they're demanding or dull, avoid them if possible.

Merch users are mostly graphic designers, side hustlers, and entrepreneurs. These laid-back users embrace technologies that assist develop their Merch business.

I discovered there was only one annual conference for this specialty, held in Seattle, USA. I decided to organize a conference for UK/European Merch designers, despite never having done so before.

Hosting a conference for over 80 people was stressful, and it turned out to be much bigger than expected, with attendees from the US, Europe, and the UK.

I met many specialized users, built relationships, gained trust, and picked their brains in person. Many of the attendees were already Merch Wizard users, so hearing their feedback and ideas for future features was invaluable.

focused and specific

Instead of building for a generic, hard-to-reach market, target a specific group.

I liken it to fishing in a little, hidden pond. This small pond has only one species of fish, so you learn what bait it likes. Contrast that with trawling for hours to catch as many fish as possible, even if some aren't what you want.

In the case management scenario, it's difficult to target leads because several niches could use the app. Where do your potential customers hang out? Your generic solution: No.

It's easier to join a community of Real Estate Lawyers and see if your software can answer their pain points.

My Success with Micro SaaS

In my case, my Micro SaaS apps have been my chrome extensions. Since I launched them, they've earned me an average $10k MRR, allowing me to quit my lousy full-time job years ago.

I sold my apps after scaling them for a life-changing lump amount. Since then, I've helped unfulfilled software developers escape the 9-5 through Micro SaaS.

Whether it's a profitable side hustle or a liferaft to quit their job and become their own Micro SaaS boss.

Having built my apps to the point where I could quit my job, then scaled and sold them, I feel I can share my skills with software developers worldwide.

Read my free guide on self-funded SaaS to discover more about Micro SaaS, or download your own copy. 12 chapters cover everything from Idea to Exit.

Watch my YouTube video to learn how to construct a Micro SaaS app in 10 steps.

And tips for anyone trying.

We see the same story every few years.

BREAKING NEWS: [Platform X] launched a social network. With Facebook's reputation down, the new startup bets millions will switch.

Despite the excitement surrounding each new platform (Diaspora, Ello, Path, MeWe, Minds, Vero, etc.), no major exodus occurred.

Snapchat and TikTok attracted teens with fresh experiences (ephemeral messaging and rapid-fire videos). These features aren't Facebook, even if Facebook replicated them.

Facebook's core is simple: you publish items (typically text/images) and your friends (generally people you know IRL) can discuss them.

It's cool. Sometimes I don't want to, but sh*t. I like it.

Because, well, I like many folks I've met. I enjoy keeping in touch with them and their banter.

I dislike Facebook's corporation. I've been cautiously optimistic whenever a Facebook-killer surfaced.

None succeeded.

Why? Two causes, I think:

People couldn't switch quickly enough, which is reason #1

Your buddies make a social network social.

Facebook started in self-contained communities (college campuses) then grew outward. But a new platform can't.

If we're expected to leave Facebook, we want to know that most of our friends will too.

Most Facebook-killers had bottlenecks. You have to waitlist or jump through hoops (e.g. setting up a server).

Same outcome. Upload. Chirp.

After a week or two of silence, individuals returned to Facebook.

Reason #2: The fundamental experience was different.

Even when many of our friends joined in the first few weeks, it wasn't the same.

There were missing features or a different UX.

Want to reply with a meme? No photos in comments yet. (Trying!)

Want to tag a friend? Nope, sorry. 2019!

Want your friends to see your post? You must post to all your friends' servers. Good luck!

It's difficult to introduce a platform with 100% of the same features as one that's been there for 20 years, yet customers want a core experience.

If you can't, they'll depart.

The causes that led to the causes

Having worked on software teams for 14+ years, I'm not surprised by these challenges. They are a natural development of a few tech sector meta-problems:

Lean startup methodology

Silicon Valley worships lean startup. It's a way of developing software that involves testing a stripped-down version with a limited number of people before selecting what to build.

Billion people use Facebook's functions. They aren't tested. It must work right away*

*This may seem weird to software people, but it's how non-software works! You can't sell a car without wheels.

2. Creativity

Startup entrepreneurs build new things, not copies. I understand. Reinventing the wheel is boring.

We know what works. Different experiences raise adoption friction. Once millions have transferred, more features (and a friendlier UX) can be implemented.

3. Cost scaling

True. Building a product that can sustain hundreds of millions of users in weeks is expensive and complex.

Your lifeboats must have the same capacity as the ship you're evacuating. It's required.

4. Pure ideologies

People who work on Facebook-alternatives are (understandably) critical of Facebook.

They build an open-source, fully-distributed, data-portable, interface-customizable, offline-capable, censorship-proof platform.

Prioritizing these aims can prevent replicating the straightforward experience users expect. Github, not Facebook, is for techies only.

What about the business plan, though?

Facebook-killer attempts have followed three models.

1. Utilize VC funding to increase your user base, then monetize them later. (If you do this, you won't kill Facebook; instead, Facebook will become you.)

2. Users must pay to utilize it. (This causes a huge bottleneck and slows the required quick expansion, preventing it from seeming like a true social network.)

3. Make it a volunteer-run, open-source endeavor that is free. (This typically denotes that something is cumbersome, difficult to operate, and is only for techies.)

Wikipedia is a fourth way.

Wikipedia is one of the most popular websites and a charity. No ads. Donations support them.

A Facebook-killer managed by a good team may gather millions (from affluent contributors and the crowd) for their initial phase of development. Then it might sustain on regular donations, ethical transactions (e.g. fees on commerce, business sites, etc.), and government grants/subsidies (since it would essentially be a public utility).

When you're not aiming to make investors rich, it's remarkable how little money you need.

If you want to build a Facebook competitor, follow these tips:

1. Drop the lean startup philosophy. Wait until you have a finished product before launching. Build it, thoroughly test it for bugs, and then release it.

2. Delay innovating. Wait till millions of people have switched before introducing your great new features. Make it nearly identical for now.

3. Spend money climbing. Make sure that guests can arrive as soon as they are invited. Never keep them waiting. Make things easy for them.

4. Make it accessible to all. Even if doing so renders it less philosophically pure, it shouldn't require technical expertise to utilize.

5. Constitute a nonprofit. Additionally, develop community ownership structures. Profit maximization is not the only strategy for preserving valued assets.

Last thoughts

Nobody has killed Facebook, but Facebook is killing itself.

The startup is burying the newsfeed to become a TikTok clone. Meta itself seems to be ditching the platform for the metaverse.

I wish I was happy, but I'm not. I miss (understandably) removed friends' postings and remarks. It could be a ghost town in a few years. My dance moves aren't TikTok-worthy.

Who will lead? It's time to develop a social network for the people.

Greetings if you're working on it. I'm not a company founder, but I like to help hard-working folks.

One great idea might make you rich.

Imagine having a million-dollar concept in college that made a million.

2005 precisely.

Alex Tew, 21, from Wiltshire, England, created The Million Dollar Homepage in August 2005. The idea is basic but beyond the ordinary, which is why it worked.

Alex built a 1,000,000-pixel webpage.

Each website pixel would cost $1. Since pixels are hard to discern, he sold 10x10 squares for $100.

He'd make a million if all the spots sold.

He may have thought about NFTs and the Metaverse decades ago.

MillionDollarHomepage.com launched in 2005.

Businesses and individuals could buy a website spot and add their logo, website link, and tagline. You bought an ad, but nobody visited the website.

If a few thousand people visited the website, it could drive traffic to your business's site.

Alex promised buyers the website would be up for 5 years, so it was a safe bet.

Alex's friend with a music website was the first to buy real estate on the site. Within two weeks, 4,700 pixels sold, and a tracker showed how many were sold and available.

Word-of-mouth marketing got the press's attention quickly. Everyone loves reading about new ways to make money, so it was a good news story.

By September, over 250,000 pixels had been sold, according to a BBC press release.

Alex and the website gained more media and public attention, so traffic skyrocketed. Two months after the site launched, 1,400 customers bought more than 500,000 pixels.

Businesses bought online real estate. They heard thousands visited the site, so they could get attention cheaply.

Unless you bought a few squares, I'm not sure how many people would notice your ad or click your link.

A sponge website owner emailed Alex:

“We tried Million Dollar Homepage because we were impressed at the level of ingenuity and the sheer simplicity of it. If we’re honest, we didn’t expect too much from it. Now, as a direct result, we are pitching for £18,000 GBP worth of new clients and have seen our site traffic increase over a hundred-fold. We’re even going to have to upgrade our hosting facility! It’s been exceptional.”

Web.archive.org screenshots show how the website changed.

“The idea is to create something of an internet time capsule: a homepage that is unique and permanent. Everything on the internet keeps changing so fast, it will be nice to have something that stays solid and permanent for many years. You can be a part of that!” Alex Tew, 2005

The last 1,000 pixels were sold on January 1, 2006.

By then, the homepage had hundreds of thousands of monthly visitors. Alex put the last space on eBay due to high demand.

MillionDollarWeightLoss.com won the last pixels for $38,100, bringing revenue to $1,037,100 in 4 months.

Many have tried to replicate this website's success. They've all failed.

This idea only worked because no one had seen this website before.

This winner won't be repeated, but it should inspire you to try something new and creative.

Still popular, you could buy one of the linked domains. You can't buy pixels, but you can buy an expired domain.

One link I clicked costs $59,888.

You'd own a piece of internet history if you spent that much on a domain.

Someone bought stablesgallery.co.uk after the domain expired and restored it.

Many of the linked websites have expired or been redirected, but some still link to the original. I couldn't find sponge's website. Can you?

This is a great example of how a simple creative idea can go viral.

Comment on this amazing success story.

Sundar Pichai unveiled Simplicity Sprint at Google's latest all-hands conference.

To boost employee efficiency.

Not surprising. Few envisioned Google declaring a productivity drive.

Sunder Pichai's speech:

“There are real concerns that our productivity as a whole is not where it needs to be for the head count we have. Help me create a culture that is more mission-focused, more focused on our products, more customer focused. We should think about how we can minimize distractions and really raise the bar on both product excellence and productivity.”

The primary driver driving Google's efficiency push is:

Google's efficiency push follows 13% quarterly revenue increase. Last year in the same quarter, it was 62%.

Market newcomers may argue that the previous year's figure was fuelled by post-Covid reopening and growing consumer spending. Investors aren't convinced. A promising company like Google can't afford to drop so quickly.

Google’s quarterly revenue growth stood at 13%, against 62% in last year same quarter.

Google isn't alone. In my recent essay regarding 2025 programmers, I warned about the economic downturn's effects on FAAMG's workforce. Facebook had suspended hiring, and Microsoft had promised hefty bonuses for loyal staff.

In the same article, I predicted Google's troubles. Online advertising, especially the way Google and Facebook sell it using user data, is over.

FAAMG and 2nd rung IT companies could be the first to fall without Post-COVID revival and uncertain global geopolitics.

Google has hardly ever discussed effectiveness:

Apparently openly.

Amazon treats its employees like robots, even in software positions. It has significant turnover and a terrible reputation as a result. Because of this, it rarely loses money due to staff productivity.

Amazon trumps Google. In reality, it treats its employees poorly.

Google was the founding father of the modern-day open culture.

Larry and Sergey Google founded the IT industry's Open Culture. Silicon Valley called Google's internal democracy and transparency near anarchy. Management rarely slammed decisions on employees. Surveys and internal polls ensured everyone knew the company's direction and had a vote.

20% project allotment (weekly free time to build own project) was Google's open-secret innovation component.

After Larry and Sergey's exit in 2019, this is Google's first profitability hurdle. Only Google insiders can answer these questions.

• Would Google's investors compel the company's management to adopt an Amazon-style culture where the developers are treated like circus performers?

• If so, would Google follow suit?

• If so, how does Google go about doing it?

Before discussing Google's likely plan, let's examine programming productivity.

What determines a programmer's productivity is simple:

How would we answer Google's questions?

As a programmer, I'm more concerned about Simplicity Sprint's aftermath than its economic catalysts.

Large organizations don't care much about quarterly and annual productivity metrics. They have 10-year product-launch plans. If something seems horrible today, it's likely due to someone's lousy judgment 5 years ago who is no longer in the blame game.

Deconstruct our main question.

• How exactly do you change the culture of the firm so that productivity increases?

• How can you accomplish that without affecting your capacity to profit? There are countless ways to increase output without decreasing profit.

• How can you accomplish this with little to no effect on employee motivation? (While not all employers care about it, in this case we are discussing the father of the open company culture.)

• How do you do it for a 10-developer IT firm that is losing money versus a 1,70,000-developer organization with a trillion-dollar valuation?

When implementing a large-scale organizational change, success must be carefully measured.

The fastest way to do something is to do it right, no matter how long it takes.

You require clearly-defined group/team/role segregation and solid pass/fail matrices to:

• You can give performers rewards.

• Ones that are average can be inspired to improve

• Underachievers may receive assistance or, in the worst-case scenario, rehabilitation

As a 20-year programmer, I associate productivity with greatness.

Doing something well, no matter how long it takes, is the fastest way to do it.

Let's discuss a programmer's productivity.

Why productivity is a strange term in programming:

Productivity is work per unit of time.

Money=time This is an economic proverb. More hours worked, more pay. Longer projects cost more.

As a buyer, you desire a quick supply. As a business owner, you want employees who perform at full capacity, creating more products to transport and boosting your profits.

All economic matrices encourage production because of our obsession with it. Productivity is the only organic way a nation may increase its GDP.

Time is money — is not just a proverb, but an economical fact.

Applying the same productivity theory to programming gets problematic. An automating computer. Its capacity depends on the software its master writes.

Today, a sophisticated program can process a billion records in a few hours. Creating one takes a competent coder and the necessary infrastructure. Learning, designing, coding, testing, and iterations take time.

Programming productivity isn't linear, unlike manufacturing and maintenance.

Average programmers produce code every day yet miss deadlines. Expert programmers go days without coding. End of sprint, they often surprise themselves by delivering fully working solutions.

Reversing the programming duties has no effect. Experts aren't needed for productivity.

These patterns remind me of an XKCD comic.

Programming productivity depends on two factors:

• The capacity of the programmer and his or her command of the principles of computer science

• His or her productive bursts, how often they occur, and how long they last as they engineer the answer

At some point, productivity measurement becomes Schrödinger’s cat.

Product companies measure productivity using use cases, classes, functions, or LOCs (lines of code). In days of data-rich source control systems, programmers' merge requests and/or commits are the most preferred yardstick. Companies assess productivity by tickets closed.

Every organization eventually has trouble measuring productivity. Finer measurements create more chaos. Every measure compares apples to oranges (or worse, apples with aircraft.) On top of the measuring overhead, the endeavor causes tremendous and unnecessary stress on teams, lowering their productivity and defeating its purpose.

Macro productivity measurements make sense. Amazon's factory-era management has done it, but at great cost.

Google can pull it off if it wants to.

What Google meant in reality when it said that employee productivity has decreased:

When Google considers its employees unproductive, it doesn't mean they don't complete enough work in the allotted period.

They can't multiply their work's influence over time.

• Programmers who produce excellent modules or products are unsure on how to use them.

• The best data scientists are unable to add the proper parameters in their models.

• Despite having a great product backlog, managers struggle to recruit resources with the necessary skills.

• Product designers who frequently develop and A/B test newer designs are unaware of why measures are inaccurate or whether they have already reached the saturation point.

• Most ignorant: All of the aforementioned positions are aware of what to do with their deliverables, but neither their supervisors nor Google itself have given them sufficient authority.

So, Google employees aren't productive.

How to fix it?

• Business analysis: White suits introducing novel items can interact with customers from all regions. Track analytics events proactively, especially the infrequent ones.

• SOLID, DRY, TEST, and AUTOMATION: Do less + reuse. Use boilerplate code creation. If something already exists, don't implement it yourself.

• Build features-building capabilities: N features are created by average programmers in N hours. An endless number of features can be built by average programmers thanks to the fact that expert programmers can produce 1 capability in N hours.

• Work on projects that will have a positive impact: Use the same algorithm to search for images on YouTube rather than the Mars surface.

• Avoid tasks that can only be measured in terms of time linearity at all costs (if a task can be completed in N minutes, then M copies of the same task would cost M*N minutes).

In conclusion:

Software development isn't linear. Why should the makers be measured?

Notation for The Big O

I'm discussing a new way to quantify programmer productivity. (It applies to other professions, but that's another subject)

The Big O notation expresses the paradigm (the algorithmic performance concept programmers rot to ace their Google interview)

Google (or any large corporation) can do this.

1. Sort organizational roles into categories and specify their impact vs. time objectives. A CXO role's time vs. effect function, for instance, has a complexity of O(log N), meaning that if a CEO raises his or her work time by 8x, the result only increases by 3x.

2. Plot the influence of each employee over time using the X and Y axes, respectively.

3. Add a multiplier for Y-axis values to the productivity equation to make business objectives matter. (Example values: Support = 5, Utility = 7, and Innovation = 10).

4. Compare employee scores in comparable categories (developers vs. devs, CXOs vs. CXOs, etc.) and reward or help employees based on whether they are ahead of or behind the pack.

After measuring every employee's inventiveness, it's straightforward to help underachievers and praise achievers.

Example of a Big(O) Category:

If I ran Google (God forbid, its worst days are far off), here's how I'd classify it. You can categorize Google employees whichever you choose.

The Google interview truth:

O(1) < O(log n) < O(n) < O(n log n) < O(n^x) where all logarithmic bases are < n.

O(1): Customer service workers' hours have no impact on firm profitability or customer pleasure.

CXOs Most of their time is spent on travel, strategic meetings, parties, and/or meetings with minimal floor-level influence. They're good at launching new products but bad at pivoting without disaster. Their directions are being followed.

Devops, UX designers, testers Agile projects revolve around deployment. DevOps controls the levers. Their automation secures results in subsequent cycles.

UX/UI Designers must still prototype UI elements despite improved design tools.

All test cases are proportional to use cases/functional units, hence testers' work is O(N).

Architects Their effort improves code quality. Their right/wrong interference affects product quality and rollout decisions even after the design is set.

Core Developers Only core developers can write code and own requirements. When people understand and own their labor, the output improves dramatically. A single character error can spread undetected throughout the SDLC and cost millions.

Core devs introduce/eliminate 1000x bugs, refactoring attempts, and regression. Following our earlier hypothesis.

The fastest way to do something is to do it right, no matter how long it takes.

Conclusion:

Google is at the liberal extreme of the employee-handling spectrum

Microsoft faced an existential crisis after 2000. It didn't choose Amazon's data-driven people management to revitalize itself.

Instead, it entrusted developers. It welcomed emerging technologies and opened up to open source, something it previously opposed.

Google is too lax in its employee-handling practices. With that foundation, it can only follow Amazon, no matter how carefully.

Any attempt to redefine people's measurements will affect the organization emotionally.

The more Google compares apples to apples, the higher its chances for future rebirth.

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.